Artificial Intelligence (AI) offers a revolutionary approach to improve decision-making in medicine through the use of advanced computational tools. Its ability to analyze large and complex datasets enables a thorough evaluation of multiple factors, leading to a deeper understanding of medical procedures. Numerous studies have demonstrated that AI has made significant advancements in areas such as organ allocation, donor-recipient matching, and immunosuppression protocols in organ transplantation. The transplantation process consists of three key stages: pre-transplant evaluation, the surgical procedure, and post-transplant management. AI can enhance all three stages by analyzing and integrating data from histopathological reports, lab results, radiological features, and patient demographics to aid in matching donors and recipients. Additionally, AI supports robotic-assisted surgery and optimizes post-transplant regimens while evaluating complications. Various researches have utilized machine learning (ML) to predict medication bioavailability immediately after transplantation and assess the risk of post-transplant complications based on factors like genetic phenotypes, age, gender, and body mass index. This review aims to gather information on AI applications across various stages of organ transplantation and elaborate the strategies and tools relevant to these processes.

In lung transplantation (LTx), a priority is assigned to each candidate on the waiting list. Our primary objective was to identify the key factors that influence the allocation of priorities in LTx using machine learning (ML) techniques to enhance the process of prioritising patients.

Developing a prediction model.

Our data were retrieved from the United Network for Organ Sharing (UNOS) open-source database of transplant patients between 2005 and 2023.

After the preprocessing process, a feature engineering technique was employed to select the most relevant features. Then, six ML models with optimised hyperparameters including multiple linear regression, random forest regressor (RF), support vector machine regressor, XGBoost regressor, a multilayer perceptron model and a deep learning model were developed based on the UNOS dataset.

The performance of each model was evaluated using R-squared (R2) and other error rate metrics. Next, the Shapley Additive Explanations (SHAP) technique was used to identify the most important features in the prediction.

The raw dataset contains 196 270 records with 545 features in all organs. After preprocessing, 32 966 records with 15 features remain. Among various models, the RF model achieved a high R2 score. Additionally, the RF model exhibited the lowest error values, indicating its superior precision compared with other regression models. The SHAP technique in conjunction with the RF model revealed the 11 most important features for priority allocation. Subsequently, we developed a web-based decision support tool using Python and the Streamlit framework based on the best-fine-tuned model.

The deployment of the ML model has the potential to act as an automated tool to aid physicians in assessing the priority of lung transplants and identifying significant factors that play a role in patient survival.

Stem cell research has significantly transformed regenerative medicine, with pluripotent stem cells (PSCs) serving as the cornerstone for disease modeling, drug screening, and therapeutic applications. Embryonic stem cells (ESCs) exhibit unparalleled self-renewal and tri-lineage differentiation, while induced pluripotent stem cells (iPSCs) bypass ethical constraints through somatic cell reprogramming. Clinical trials highlight the potential of mesenchymal stem cells (MSCs) in osteoarthritis and graft-versus-host disease, which leverage their immunomodulatory and paracrine effects. Despite advancements, challenges persist: iPSCs face epigenetic instability and tumorigenic risks, and adult stem cells struggle with inefficient differentiation. This paper systematically reviews stem cell source classification, differentiation regulatory mechanisms, cutting-edge technologies such as CRISPR/Cas9, and explores field-specific controversies (e.g., epigenetic stability of iPSCs) and future directions (e.g., integration of organoids and biomaterials). By analyzing current progress and challenges, it provides a multidimensional perspective for stem cell research.

Background/Objectives: Solid organ transplantation remains a critical life-saving treatment for end-stage organ failure, yet it faces persistent challenges, such as organ scarcity, graft rejection, and postoperative complications. Artificial intelligence (AI) has the potential to address these challenges by revolutionizing transplantation practices. Methods: This review article explores the diverse applications of AI in solid organ transplantation, focusing on its impact on diagnostics, treatment, and the evolving market landscape. We discuss how machine learning, deep learning, and generative AI are harnessing vast datasets to predict transplant outcomes, personalized immunosuppressive regimens, and optimize patient selection. Additionally, we examine the ethical implications of AI in transplantation and highlight promising AI-driven innovations nearing FDA evaluation. Results: AI improves organ allocation processes, refines predictions for transplant outcomes, and enables tailored immunosuppressive regimens. These advancements contribute to better patient selection and enhance overall transplant success rates. Conclusions: By bridging the gap in organ availability and improving long-term transplant success, AI holds promise to significantly advance the field of solid organ transplantation.

Owing to the complex and often asymptomatic presentations, the diagnosis of biliopancreatic diseases, including pancreatic and biliary malignancies, remains challenging. Recent technological advancements have remarkably improved the diagnostic accuracy and patient outcomes in these diseases. This review explores key advancements in diagnostic modalities, including biomarkers, imaging techniques, and artificial intelligence (AI)-based technologies. Biomarkers, such as cancer antigen 19-9, KRAS mutations, and inflammatory markers, provide crucial insights into disease progression and treatment responses. Advanced imaging modalities include enhanced computed tomography (CT), positron emission tomography-CT, magnetic resonance cholangiopancreatography, and endoscopic ultrasound. AI integration in imaging and pathology has enhanced diagnostic precision through deep learning algorithms that analyze medical images, automate routine diagnostic tasks, and provide predictive analytics for personalized treatment strategies. The applications of these technologies are diverse, ranging from early cancer detection to therapeutic guidance and real-time imaging. Biomarker-based liquid biopsies and AI-assisted imaging tools are essential for non-invasive diagnostics and individualized patient management. Furthermore, AI-driven models are transforming disease stratification, thus enhancing risk assessment and decision-making. Future studies should explore standardizing biomarker validation, improving AI-driven diagnostics, and expanding the accessibility of advanced imaging technologies in resource-limited settings. The continued development of non-invasive diagnostic techniques and precision medicine approaches is crucial for optimizing the detection and management of biliopancreatic diseases. Collaborative efforts between clinicians, researchers, and industry stakeholders will be pivotal in applying these advancements in clinical practice.

The shortage of liver grafts compared to recipients necessitates precise organ assessment. This study aimed to develop a Machine learning (ML) model to predict postoperative delayed graft function (DGF) and visualize the decision-making process for clinical application.

Data from 5242 donor-recipient pairs who underwent liver transplantation (LT) at the top 10 liver transplant centers in China (January 2017 to December 2022) were collected. The dataset was divided into training and validation sets. Sixty-three variables, including demographics, donor characteristics, diagnosis, preoperative lab results, and surgical information were analyzed. The primary outcome was posttransplantation DGF and the second outcome was posttransplantation 1-month and 3-month survival. Recursive feature elimination selected critical variables, and models were built using ML algorithms and logistic regression. Model performance was evaluated by AUC, accuracy, sensitivity, and specificity. The best model was validated with an independent dataset of 394 LT cases (January to June 2023). The SHapley Additive exPlanations package interpreted the top model's decisions.

Among 5242 cases, 328 (6.26%) developed DGF, with 15 cases (3.81%) in the external validation set. Thirty critical features were selected. The eXtreme Gradient Boosting algorithm achieved the highest AUC (0.877) and accuracy (0.936) in the internal set, and a comparable AUC (0.776) and accuracy (0.957) in the external set. SHAP analysis identified short perfusion time, high donor serum sodium, excessive bleeding during transplantation, high donor γ-glutamyl transpeptidase, and blood glucose levels as top predictors of post-LT DGF. The proposed model AUC's 1-month survival prediction was 0.841 and the 3-month survival prediction was 0.834.

The developed model for predicting postoperative DGF demonstrated excellent predictive performance, aiding clinicians in evaluating donor grafts and making informed decisions.

Predicting graft failure risk in pediatric liver transplantation (LT) recipients could identify areas for improving management. Persistent cognitive, motor, academic, and functional deficits are common in recipients and their impact on graft survival following LT helps inform risk prediction.

Using SRTR data 2008-2023, we evaluated the cognitive, motor, academic, and functional deficits of LT recipients at time of transplant to 14 years post-LT. We compared all cause graft failure (ACGF) among patients with versus without pre-LT and 1-year post-LT deficits using Cox regression, adjusting for recipient characteristics. We calculated an individual risk score for ACGF.

In 8062 pediatric LT recipients median age 3 (IQR: 1, 10), 28.0%, 29.5%, 35.0%, and 79.8% of recipients had pre-LT deficits in cognition, motor, academic activity, and functional status respectively. This decreased to 23.0%, 18.1%, 14.2%, and 38.7% 1-year post-LT. Increased hazard of ACGF was noted in recipients with pre-LT decreased functional status (aHR = 1.13 (per 10% decrease), 95% CI: 1.10-1.15, p < 0.001), definite motor delay (aHR = 1.60, 95% CI: 1.21-2.10, p < 0.001), and inability to participate in academics (aHR = 1.49, 95% CI: 1.08-1.89, p = 0.01), but not delays in cognition (aHR = 0.91, 95% CI: 0.69-1.21, p = 0.19). Our risk score predicting ACGF demonstrated improved predictive performance compared to clinical parameters alone (C-statistic = 0.70 (0.67, 0.72) vs. 0.66 (0.64, 0.69), p < 0.001).

Pediatric LT recipients with pre- or post-LT motor, academic, and functional deficits are at higher risk for ACGF. Care should be taken to assess deficits to identify patients who may benefit from functional intervention to potentially reduce ACGF risk.

Machine learning (ML), a major branch of artificial intelligence, has not only demonstrated the potential to significantly improve numerous sectors of healthcare but has also made significant contributions to the field of solid organ transplantation. ML provides revolutionary opportunities in areas such as donor-recipient matching, post-transplant monitoring, and patient care by automatically analyzing large amounts of data, identifying patterns, and forecasting outcomes.

To conduct a comprehensive bibliometric analysis of publications on the use of ML in transplantation to understand current research trends and their implications.

On July 18, a thorough search strategy was used with the Web of Science database. ML and transplantation-related keywords were utilized. With the aid of the VOS viewer application, the identified articles were subjected to bibliometric variable analysis in order to determine publication counts, citation counts, contributing countries, and institutions, among other factors.

Of the 529 articles that were first identified, 427 were deemed relevant for bibliometric analysis. A surge in publications was observed over the last four years, especially after 2018, signifying growing interest in this area. With 209 publications, the United States emerged as the top contributor. Notably, the "Journal of Heart and Lung Transplantation" and the "American Journal of Transplantation" emerged as the leading journals, publishing the highest number of relevant articles. Frequent keyword searches revealed that patient survival, mortality, outcomes, allocation, and risk assessment were significant themes of focus.

The growing body of pertinent publications highlights ML's growing presence in the field of solid organ transplantation. This bibliometric analysis highlights the growing importance of ML in transplant research and highlights its exciting potential to change medical practices and enhance patient outcomes. Encouraging collaboration between significant contributors can potentially fast-track advancements in this interdisciplinary domain.

Over the past six decades, liver transplantation (LT) has evolved from an experimental procedure into a standardized and life-saving intervention, reshaping the landscape of organ transplantation. Driven by pioneering breakthroughs, technological advancements, and a deepened understanding of immunology, LT has seen remarkable progress. Some of the most notable breakthroughs in the field include advances in immunosuppression, a revised model for end-stage liver disease, and artificial intelligence (AI)-integrated imaging modalities serving diagnostic and therapeutic roles in LT, paired with ever-evolving technological advances. Additionally, the refinement of transplantation procedures, resulting in the introduction of alternative transplantation methods, such as living donor LT, split LT, and the use of marginal grafts, has addressed the challenge of organ shortage. Moreover, precision medicine, guiding personalized immunosuppressive strategies, has significantly improved patient and graft survival rates while addressing emergent issues, such as short-term complications and early allograft dysfunction, leading to a more refined strategy and enhanced post-operative recovery. Looking ahead, ongoing research explores regenerative medicine, diagnostic tools, and AI to optimize organ allocation and post-transplantation car. In summary, the past six decades have marked a transformative journey in LT with a commitment to advancing science, medicine, and patient-centered care, offering hope and extending life to individuals worldwide.

The objective of this study was to develop and evaluate automated machine learning (aML) models for predicting short-term (1-month) and medium-term (3-month) functional outcomes [Modified Rankin Scale (mRS)] in patients suffering from poor-grade aneurysmal subarachnoid hemorrhage (aSAH), using readily available and routinely measured laboratory and radiological parameters at admission. Data from a pilot non-randomized trial of 60 poor-grade aSAH patients (Hunt-Hess grades IV or V) were analyzed. Patients were evenly divided between targeted temperature management (TTM) and standard treatment groups. The current state-of-the-art for aML was adopted to employ nine ML algorithms with hyperparameter tuning to develop algorithmic models predicting 1 month and 3-months mRS scores. Model performance was evaluated using macro-weighted average Area Under the Receiver Operating Curve (mWA-AUROC) analysis and additional metrics. Logistic regression algorithmic models achieved perfect prediction (mWA-AUROC = 1, accuracy = 100%, sensitivity and specificity = 100% [95% CI: 83.16 - 100%]) for both 1-month and 3-month mRS outcomes. For 1-month outcomes, neutrophil count, platelet count, and gamma-glutamyl transferase levels were identified as key predictors. For 3-month outcomes, patient gender, activated partial thromboplastin time, and serum aspartate aminotransferase levels were most impactful. Decision tree algorithms (mWA-AUROC = 0.9-0.925) identified specific cut-points for various parameters, providing actionable information for clinical decision-making. Positive prognostic factors included alkaline phosphatase levels higher than mid-value of their normal range, absence of hydrocephalus, use of targeted temperature management (TTM), and specific cut-offs for coagulation and liver function parameters. The use of TTM was reinforced as a key prognosticator of mRS outcomes at both time points. We have made our developed models and the associated architecture available at GitHub. This study demonstrated the potential of aML in predicting functional outcomes for poor-grade aSAH patients. The identification of novel predictors, including liver function and coagulation parameters, opens new avenues for research and intervention. While the perfect predictive performance warrants cautious interpretation and further validation, these models represent a step towards personalized medicine in aSAH management, potentially improving prognostication and treatment strategies.

Solid organ transplantation (SOT) is vital for end-stage organ failure but faces challenges like organ shortage and rejection. Artificial intelligence (AI) offers potential to improve outcomes through better matching, success prediction, and automation. However, the evolution of AI in SOT research remains underexplored. This study uses bibliometric analysis to identify trends, hotspots, and key contributors in the field.

821 articles from the Web of Science Core Collection were exported for analysis. Microsoft Excel 2021 was used for descriptive statistics. VOSviewer, CiteSpace, Scimago Graphica, and Biblioshiny were used for bibliometric analysis. The ggalluvial package in R was utilized to create Sankey diagrams, and top articles were selected based on citation count.

This analysis reveals the rapid expansion of AI in SOT. Key areas include robotic surgery, organ allocation, outcome prediction, immunosuppression management, and precision medicine. Robotic surgery has improved transplant outcomes. AI algorithms optimize organ matching and enhance fairness. Machine learning models predict outcomes and guide treatment, while AI-based systems advance personalized immunosuppression. AI in precision medicine, including diagnostics and imaging, is crucial for transplant success.

This study highlights AI's transformative potential in SOT, with significant contributions from countries like the USA, Canada, and the UK. Key institutions such as the University of Toronto and the University of Pittsburgh have played vital roles. However, practical challenges like ethical issues, bias, and data integration remain. Fostering international and interdisciplinary collaborations is crucial for overcoming these challenges and accelerating AI's integration into clinical practice, ultimately improving patient outcomes.

Liver transplantation (LT) is the standard of care for both end-stage liver failure and hepatocellular carcinoma (HCC). Side effects of the main used immunosuppressive drugs have a noteworthy impact on the long-term outcome of LT recipients. Consequently, to achieve a balance between optimal immunosuppression and minimal side effects is a cornerstone of the post-LT period. Today, there are no validated markers for overimmunosuppression and underimmunosuppression, only a few drugs have therapeutic drug monitoring, and immunosuppression regimens vary from center to center and from country to country. Currently, there are many drugs with different efficacy and safety profiles. Using different agents permits a decrease in the dosage and minimizes the toxicities. A small subset of recipients achieves immunotolerance with the chance to stop immunosuppressive therapy. This article focuses on the side effects of immunosuppressive drugs, which significantly impact long-term outcomes for LT recipients. The primary aim is to highlight the balance between achieving effective immunosuppression and minimizing adverse effects, emphasizing the role of personalized therapeutic strategies. Moreover, this review evaluates the mechanisms of action and specific complications associated with immunosuppressive agents. Finally, special attention is given to strategies for reducing immunosuppressive burdens, improving patient quality of life, and identifying immunotolerant individuals.

The principles of urgency, utility, and benefit are fundamental concepts guiding the ethical and practical decision-making process for organ allocation; however, LT allocation still follows an urgency model.

To identify and analyze data elements used in Machine Learning (ML) and Artificial Intelligence (AI) methods, data sources, and their focus on urgency, utility, or benefit in LT.

A comprehensive search across Ovid Medline and Scopus was conducted for studies published from 2002 to June 2023. Inclusion criteria targeted quantitative studies using ML/AI for candidates, donors, or recipients. Two reviewers assessed eligibility and extracted data, following PRISMA guidelines.

A total of 20 papers were included, synthesizing results into five major categories. Eight studies were led by a Spanish team, focusing on donor-recipient matching and proposing machine learning models to predict post- LT survival. Other international studies addressed organ supply-demand issues and developed predictive models to optimize LT outcomes. The studies highlight the potential of ML/AI to enhance LT allocation and outcomes. Despite advancements, limitations included the lack of robust transplant-related benefit models and improvements in urgency models compared to MELD.

This review highlighted the potential of AI and ML to enhance liver transplant allocation and outcomes. Significant advancements were noted, but limitations such as the need for better urgency models and the absence of a transplant-related benefit model remain. Most studies emphasized utility, focusing on survival outcomes. Future research should address the interpretability and generalizability of these models to improve organ allocation and post-LT survival predictions.

Incisional hernias are a significant source of morbidity in the United States that impact quality of life and can cause life-threatening complications. Complex patient factors, collected as structured and unstructured data, contribute to the risk of developing an incisional hernia following abdominal surgery. It is unknown how risk prediction models derived from imaging data, or radiomic features, can enhance pre-operative surgical planning. This study investigates surgeons' perspectives regarding risk prediction models derived from radiomic features and assesses the model's impact on surgeon behavior.

An online cross-sectional survey assessing perceptions of a pre-operative risk prediction model was administered to surgeons across the US from April 23, 2024- May 30, 2024. Surgeons' beliefs of the risk model's impact on surgeon behavior and its applicability in the clinical setting were assessed.

A total of 166 completed surveys were analyzed. Mean age was 52.3 (SD 10.1), 71.1% were male, 78.9% were White, and 90.4% were not Hispanic or Latino. The majority of the respondents were general surgeons (58%), colorectal surgeons (14%), thoracic surgeons (12%), and urologists (7%). The mean level of accuracy predicted from radiomic features needed to prompt a change in management was 74.5% (SD 15.1%). The mean at which FPR and FNR were unacceptable was 25.9% (SD 16.9%) and 26.1% (SD 21.7%), respectively. Most believed a risk prediction model tool would improve their peri-operative management.

A majority of surgeons were positively supportive of incorporating a hernia risk-prediction clinical decision tool incorporating radiomic features in their clinical practice.

Liver transplantation is a critical and evolving field in modern medicine, offering life-saving treatment for patients with end-stage liver disease and other hepatic conditions. Despite its transformative potential, transplantation faces persistent challenges, including a global organ shortage, increasing liver disease prevalence, and significant waitlist mortality rates. Current donor evaluation practices often discard potentially viable livers, underscoring the need for refined graft assessment tools. This review explores advancements in graft evaluation and utilization aimed at expanding the donor pool and optimizing outcomes. Emerging technologies, such as imaging techniques, dynamic functional tests, and biomarkers, are increasingly critical for donor assessment, especially for marginal grafts. Machine learning and artificial intelligence, exemplified by tools like LiverColor, promise to revolutionize donor-recipient matching and liver viability predictions, while bioengineered liver grafts offer a future solution to the organ shortage. Advances in perfusion techniques are improving graft preservation and function, particularly for donation after circulatory death (DCD) grafts. While challenges remain-such as graft rejection, ischemia-reperfusion injury, and recurrence of liver disease-technological and procedural advancements are driving significant improvements in graft allocation, preservation, and post-transplant outcomes. This review highlights the transformative potential of integrating modern technologies and multidisciplinary approaches to expand the donor pool and improve equity and survival rates in liver transplantation.

Survival analysis is critical in many fields, particularly in healthcare where it can guide medical decisions. Conventional survival analysis methods like Kaplan-Meier and Cox proportional hazards models to generate survival curves indicating probability of survival v. time have limitations, especially for long-term prediction, due to assumptions that all instances follow a general population-level survival curve. Machine learning classification models, even those designed for survival predictions like random survival forest (RSF), also struggle to provide accurate long-term predictions due to class imbalance. We improve upon traditional survival machine learning approaches through a novel framework called classification-augmented survival estimation (CASE), which treats survival as a classification task that ultimately yields survival curves, beginning with dataset augmentation to improve class imbalance for use with any classification model. Unlike other approaches, CASE additionally provides an exact survival time prediction. We demonstrate CASE on a liver transplant case study to predict >20 years survival post-transplant, finding that CASE dataset augmentation improved AUCs from 0.69 to 0.88 and F1 scores from 0.32 to 0.73. Compared to Kaplan-Meier, Cox, and RSF survival models, the CASE framework demonstrated better performance across various existing survival metrics, as well as our novel metric, mean of individual areas under the survival curve (mAUSC). Further, we develop novel temporal feature importance methods to understand how different features may vary in survival importance over time, potentially providing actionable insights in real-world survival problems.

Acute-on-chronic liver failure (ACLF) defines a heterogeneous syndrome involving acute decompensation in patients with pre-existing liver disease accompanied by (multi-)organ failure. This study aimed to develop a simple, reliable machine learning (ML) model to predict mortality in ACLF patients receiving intensive care unit (ICU) treatment. Data from 206 patients admitted to the ICU at RWTH Aachen University Hospital between 2015 and 2021 were retrospectively analyzed with ICU mortality as the primary outcome. An ICU mortality prediction model was developed by logistic regression and validated by 5-fold cross validation. Performance metrics were assessed to evaluate the model's accuracy and compare to existing mortality scores. ICU mortality was 60%. The chronic-liver-failure-consortium ACLF score (CLIF-C ACLFs) was the best predictor of ICU mortality. ML generated seven models using five to thirteen features. The best-performing model included CLIF-C ACLFs, number of organ failures, Horovitz quotient (FiO2/PaO2), FiO2 and lactate. The newly developed Aachen ACLF ICU (ACICU) score demonstrated exceptional predictive accuracy for ICU mortality (AUROC 0.96), underscoring its potential for mortality and futility assessment in critically ill ACLF patients complementing existing prognostic tools. The ACICU score www.acicu-score.com is an easy-to-use tool for predicting ICU mortality in patients with ACLF offering high predictive performance.

Liver transplantation is the gold standard treatment for patients with hepatocellular carcinoma (HCC). Current allocation systems face a complex issue due to the imbalance between available organs and recipients. The prioritization of HCC patients remains controversial, leading to potential disparities in access to transplantation. Factors such as tumor size, alpha-fetoprotein (AFP) levels, Model of End-Stage Liver Disease (MELD) score, and response to locoregional therapy (LRT) contribute to determining waitlist dropout risk in HCC patients. Several statistical and machine learning (ML) models have been proposed to predict waitlist dropout, incorporating variables related to tumor and patient factors, underlying liver disease, and waitlist time. This narrative review aims to summarize the evidence regarding different prediction models of HCC waitlist dropout.

All published articles up to December 25, 2023, were considered. Articles not based on prediction models using conventional statistical methods or ML models were excluded.

Factors such as tumor size, AFP levels, MELD score, and LRT response have been shown to impact disease progression in these patients, influencing waitlist dropout. Most articles in the literature are based on statistical models. Both ML and statistical models may offer promising results, but their application is currently limited. Several attempts have been made to find the best model to stratify the risk of waitlist dropout in HCC patients. However, to date, none of the explored models have been implemented. The allocation of HCC recipients is still based on supplementary scoring systems or geographical criteria.

Improving methodology and databases in future research is essential to obtain accurate and reliable models for clinicians. This is the only way to achieve real applicability.

To validate the Klinrisk machine learning model for prediction of chronic kidney disease (CKD) progression in patients with type 2 diabetes in the pooled CANVAS/CREDENCE trials.

We externally validated the Klinrisk model for prediction of CKD progression, defined as 40% or higher decline in estimated glomerular filtration rate (eGFR) or kidney failure. Model performance was assessed for prediction up to 3 years with the area under the receiver operating characteristic curve (AUC), Brier scores and calibration plots of observed and predicted risks. We compared performance of the model with standard of care using eGFR (G1-G4) and urine albumin-creatinine ratio (A1-A3) Kidney Disease Improving Global Outcomes (KDIGO) heatmap categories.

The Klinrisk model achieved an AUC of 0.81 (95% confidence interval [CI] 0.78-0.83) at 1 year, and 0.88 (95% CI 0.86-0.89) at 3 years. The Brier scores were 0.020 (0.018-0.022) and 0.056 (0.052-0.059) at 1 and 3 years, respectively. Compared with the KDIGO heatmap, the Klinrisk model had improved performance at every interval (P < .01).

The Klinrisk machine learning model, using routinely collected laboratory data, was highly accurate in its prediction of CKD progression in the CANVAS/CREDENCE trials. Integration of the model in electronic medical records or laboratory information systems can facilitate risk-based care.

Diabetic foot ulcer (DFU) and the resulting lower extremity amputation are associated with a poor survival prognosis. The objective of this study is to generate a model for predicting the probability of major amputation in hospitalized patients with DFU.

The National Inpatient Sample (NIS) database from 2008 to 2014 was used to select patients with DFU, who were then further divided by major amputation status. International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) and Agency for Healthcare Research and Quality (AHRQ) comorbidity codes were used to compare patient characteristics. For the descriptive statistics, the Student t test, the χ2 test, and the Spearman correlation were utilized. The five most predictive variables were identified. A decision tree model (CTREE) based on conditional inference framework algorithm and a random forest model were used to develop the algorithm.

A total of 326 853 inpatients with DFU were identified, and 5.9% underwent major amputation. The top five contributory variables (all with P < .001) were gangrene (odds ratio [OR] = 11.8, 95% confidence interval [CI] = 11.5-12.2), peripheral vascular disease (OR = 2.9, 95% CI = 2.8-3.0), weight loss (OR = 2.6, 95% CI = 2.5-2.8), systemic infection (OR = 2.5, 95% CI = 2.4-2.53), and osteomyelitis (OR = 1.7, 95% CI = 1.6-1.73). The model performance of the training data was 77.7% (76.1% sensitivity and 79.3% specificity) and of the testing data was 77.8% (76.2% sensitivity and 79.4% specificity). The model was further validated with boosting and random forest models which demonstrated similar performance and area under the curve (AUC) (0.84, 95% CI = 0.83-0.85).

Utilizing machine learning methods, we have developed a clinical algorithm that predicts the risk of major lower extremity amputation for inpatients with diabetes with 77.8% accuracy.

Liver transplantation is the only treatment for patients with liver failure. As demand for liver transplantation grows, it remains a challenge to predict the short- and long-term survival of the liver graft. Recently, artificial intelligence models have been used to evaluate the short- and long-term survival of the liver transplant. To make the models more accurate, suitable liver transplantation characteristics must be used as input to train them. In this narrative review, we reviewed studies concerning liver transplantations published in the PubMed, Web of Science, and Cochrane databases between 2017 and 2022. We picked out 17 studies using our selection criteria and analyzed them, evaluating which medical characteristics were used as input for creation of artificial intelligence models. In eight studies, models estimating only short-term liver graft survival were created, while in five of the studies, models for the prediction of only long-term liver graft survival were built. In four of the studies, artificial intelligence algorithms evaluating both the short- and long-term liver graft survival were created. Medical characteristics that were used as input in reviewed studies and had the biggest impact on the accuracy of the model were the recipient's age, recipient's body mass index, creatinine levels in the recipient's serum, recipient's international normalized ratio, diabetes mellitus, and recipient's model of end-stage liver disease score. To conclude, in order to define important liver transplantation characteristics that could be used as an input for artificial intelligence algorithms when predicting liver graft survival, more models need to be created and analyzed, in order to fully support the results of this review.

The incidence of graft failure following liver transplantation (LTx) is consistent. While traditional risk scores for LTx have limited accuracy, the potential of machine learning (ML) in this area remains uncertain, despite its promise in other transplant domains. This study aims to determine ML's predictive limitations in LTx by replicating methods used in previous heart transplant research.

This study utilized the UNOS STAR database, selecting 64,384 adult patients who underwent LTx between 2010 and 2020. Gradient boosting models (XGBoost and LightGBM) were used to predict 14, 30, and 90-day graft failure compared to conventional logistic regression model. Models were evaluated using both shuffled and rolling cross-validation (CV) methodologies. Model performance was assessed using the AUC across validation iterations.

In a study comparing predictive models for 14-day, 30-day and 90-day graft survival, LightGBM consistently outperformed other models, achieving the highest AUC of.740,.722, and.700 in shuffled CV methods. However, in rolling CV the accuracy of the model declined across every ML algorithm. The analysis revealed influential factors for graft survival prediction across all models, including total bilirubin, medical condition, recipient age, and donor AST, among others. Several features like donor age and recipient diabetes history were important in two out of three models.

LightGBM enhances short-term graft survival predictions post-LTx. However, due to changing medical practices and selection criteria, continuous model evaluation is essential. Future studies should focus on temporal variations, clinical implications, and ensure model transparency for broader medical utility.

Liver transplantation (LT) is a life-saving intervention for patients with end-stage liver disease. However, the equitable allocation of scarce donor organs remains a formidable challenge. Prognostic tools are pivotal in identifying the most suitable transplant candidates. Traditionally, scoring systems like the model for end-stage liver disease have been instrumental in this process. Nevertheless, the landscape of prognostication is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence models.

To assess the utility of ML models in prognostication for LT, comparing their per formance and reliability to established traditional scoring systems.

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we conducted a thorough and standardized literature search using the PubMed/MEDLINE database. Our search imposed no restrictions on publication year, age, or gender. Exclusion criteria encompassed non-English stu dies, review articles, case reports, conference papers, studies with missing data, or those exhibiting evident methodological flaws.

Our search yielded a total of 64 articles, with 23 meeting the inclusion criteria. Among the selected studies, 60.8% originated from the United States and China combined. Only one pediatric study met the criteria. Notably, 91% of the studies were published within the past five years. ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values (ranging from 0.6 to 1) across all studies, surpassing the performance of traditional scoring systems. Random forest exhibited superior predictive capa bilities for 90-d mortality following LT, sepsis, and acute kidney injury (AKI). In contrast, gradient boosting excelled in predicting the risk of graft-versus-host disease, pneumonia, and AKI.

This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT, marking a significant evolution in the field of prognostication.

Solid organ transplantation confronts numerous challenges ranging from donor organ shortage to post-transplant complications. Here, we provide an overview of the latest attempts to address some of these challenges using artificial intelligence (AI). We delve into the application of machine learning in pretransplant evaluation, predicting transplant rejection, and post-operative patient outcomes. By providing a comprehensive overview of AI's current impact, this review aims to inform clinicians, researchers, and policy-makers about the transformative power of AI in enhancing solid organ transplantation and facilitating personalized medicine in transplant care.

Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.

Since the advent of University of Wisconsin preservation solution in the 1980s, clinicians have learned to work within its confines. While affording improved outcomes, considerable limitations still exist and contribute to the large number of livers that go unused each year, often for fear they may never work. The last 10 years have seen the widespread availability of new perfusion modalities which provide an opportunity for assessing organ viability and prolonged organ storage. This review will discuss the role of in situ normothermic regional perfusion for livers donated after circulatory death. It will also describe the different modalities of ex situ perfusion, both normothermic and hypothermic, and discuss how they are thought to work and the opportunities afforded by them.

Liver transplantation (LT) is a lifesaving yet complex intervention with considerable challenges impacting graft and patient outcomes. Despite best practices, 5-year graft survival is only 70%. Sophisticated quantitative techniques offer potential solutions by assimilating multifaceted data into insights exceeding human cognition. Optimizing donor-recipient matching and graft allocation presents additional intricacies, involving the integration of clinical and laboratory data to select the ideal donor and recipient pair. Allocation must balance physiological variables with geographical and logistical constraints and timing. Quantitative methods can integrate these complex factors to optimize graft utilization. Such methods can also aid in personalizing treatment regimens, drawing on both pretransplant and posttransplant data, possibly using continuous immunological monitoring to enable early detection of graft injury or infected states. Advanced analytics is thus poised to transform management in LT, maximizing graft and patient survival. In this review, we describe quantitative methods applied to organ transplantation, with a focus on LT. These include quantitative methods for (1) utilizing and allocating donor organs equitably and optimally, (2) improving surgical planning through preoperative imaging, (3) monitoring graft and immune status, (4) determining immunosuppressant doses, and (5) establishing and maintaining the health of graft and patient after LT.

Primary graft dysfunction (PGD) is the leading cause of morbidity and mortality in the first 30 days after lung transplantation. Risk factors for the development of PGD include donor and recipient characteristics, but how multiple variables interact to impact the development of PGD and how clinicians should consider these in making decisions about donor acceptance remain unclear. This was a single-center retrospective cohort study to develop and evaluate machine learning pipelines to predict the development of PGD grade 3 within the first 72 hours of transplantation using donor and recipient variables that are known at the time of donor offer acceptance. Among 576 bilateral lung recipients, 173 (30%) developed PGD grade 3. The cohort underwent a 75% to 25% train-test split, and lasso regression was used to identify 11 variables for model development. A K-nearest neighbor's model showing the best calibration and performance with relatively small confidence intervals was selected as the final predictive model with an area under the receiver operating characteristics curve of 0.65. Machine learning models can predict the risk for development of PGD grade 3 based on data available at the time of donor offer acceptance. This may improve donor-recipient matching and donor utilization in the future.

Routine surveillance with duplex ultrasound (DUS) examination is recommended after femoral-popliteal and femoral-tibial-pedal vein bypass grafts with various intervals postoperatively. The presently used methodology to analyze bypass graft DUS examination does not use all the available data and has been shown to have a significant rate for missing impending bypass graft failure. The objective of this research is to investigate recurrent neural networks (RNNs) to predict future bypass graft occlusion or stenosis.

This study includes DUS examinations of 663 patients who had bypass graft operations done between January 2009 and June 2022. Only examinations without missing values were included. We developed two RNNs (a bidirectional long short-term memory unit and a bidirectional gated recurrent unit) to predict bypass graft occlusion and stenosis based on peak systolic velocities collected in the 2 to 5 previous DUS examinations. We excluded the examinations with missing values and split our data into training and test sets. Then, we applied 10-fold cross-validation on training to optimize the hyperparameters and compared models using the test data.

The bidirectional long short-term memory unit model can gain an overall sensitivity of 0.939, specificity of 0.963, and area under the curve of 0.950 on the prediction of bypass graft occlusion, and an overall sensitivity of 0.915, specificity of 0.909, and area under the curve of 0.912 predicting the development of a future critical stenosis. The results on different bypass types show that the system performs differently on different types. The results on subcohorts based on gender, smoking status, and comorbidities show that the performance on current smokers is lower than the never smoker.

We found that RNNs can gain good sensitivity, specificity, and accuracy for the detection of impending bypass graft occlusion or the future development of a critical bypass graft stenosis using all the available peak systolic velocity data in the present and previous bypass graft DUS examinations. Integrating clinical data, including demographics, social determinants, medication, and other risk factors, together with the DUS examination may result in further improvements.

Detecting bypass graft failure before it occurs is important clinically to prevent amputations, salvage limbs, and save lives. Current methods evaluating screening duplex ultrasound examinations have a significant failure rate for detecting a bypass graft at risk for failure. Artificial intelligence using recurrent neural networks has the potential to improve the detection of at-risk bypass graft before they fail. Additionally, artificial intelligence is in the news and is being applied to many fields. Vascular surgeons need to know its potential to improve vascular outcomes.

Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure. Analyzing the post-transplant cause of death (CoD) after organ transplant provides a powerful tool for clinical decision making, including personalized treatment and organ allocation. However, traditional methods like Model for End-stage Liver Disease (MELD) score and conventional machine learning (ML) methods are limited in CoD analysis due to two major data and model-related challenges. To address this, we propose a novel framework called CoD-MTL leveraging multi-task learning to model the semantic relationships between various CoD prediction tasks jointly. Specifically, we develop a novel tree distillation strategy for multi-task learning, which combines the strength of both the tree model and multi-task learning. Experimental results are presented to show the precise and reliable CoD predictions of our framework. A case study is conducted to demonstrate the clinical importance of our method in the liver transplant.

Liver transplantation is a life-saving procedure for patients with end-stage liver disease. There are two main challenges in liver transplant: finding the best matching patient for a donor and ensuring transplant equity among different subpopulations. The current MELD scoring system evaluates a patient's mortality risk if not receiving an organ within 90 days. However, the donor-patient matching should also consider post-transplant risk factors, such as cardiovascular disease, chronic rejection, etc., which are all common complications after transplant. Accurate prediction of these risk scores remains a significant challenge. In this study, we used predictive models to solve the above challenges. Specifically, we proposed a deep learning model to predict multiple risk factors after a liver transplant. By formulating it as a multi-task learning problem, the proposed deep neural network was trained to simultaneously predict the five post-transplant risks and achieve equal good performance by exploiting task-balancing techniques. We also proposed a novel fairness-achieving algorithm to ensure prediction fairness across different subpopulations. We used electronic health records of 160,360 liver transplant patients, including demographic information, clinical variables, and laboratory values, collected from the liver transplant records of the United States from 1987 to 2018. The model's performance was evaluated using various performance metrics such as AUROC and AUPRC. Our experiment results highlighted the success of our multi-task model in achieving task balance while maintaining accuracy. The model significantly reduced the task discrepancy by 39 %. Further application of the fairness-achieving algorithm substantially reduced fairness disparity among all sensitive attributes (gender, age group, and race/ethnicity) in each risk factor. It underlined the potency of integrating fairness considerations into the task-balancing framework, ensuring robust and fair predictions across multiple tasks and diverse demographic groups.

Liver transplantation (LT) is a treatment for acute-on-chronic liver failure (ACLF), but high post-LT mortality has been reported. Existing post-LT models in ACLF have been limited. We developed an Expert-Augmented Machine Learning (EAML) model to predict post-LT outcomes. We identified ACLF patients who underwent LT in the University of California Health Data Warehouse. We applied the RuleFit machine learning (ML) algorithm to extract rules from decision trees and create intermediate models. We asked human experts to rate the rules generated by RuleFit and incorporated these ratings to generate final EAML models. We identified 1384 ACLF patients. For death at 1 year, areas under the receiver-operating characteristic curve were 0.707 (confidence interval [CI] 0.625-0.793) for EAML and 0.719 (CI 0.640-0.800) for RuleFit. For death at 90 days, areas under the receiver-operating characteristic curve were 0.678 (CI 0.581-0.776) for EAML and 0.707 (CI 0.615-0.800) for RuleFit. In pairwise comparisons, both EAML and RuleFit models outperformed cross-sectional models. Significant discrepancies between experts and ML occurred in rankings of biomarkers used in clinical practice. EAML may serve as a method for ML-guided hypothesis generation in further ACLF research.

Allocation of scarce organs for transplantation is ethically challenging. Artificial intelligence (AI) has been proposed to assist in liver allocation, however the ethics of this remains unexplored and the view of the public unknown. The aim of this paper was to assess public attitudes on whether AI should be used in liver allocation and how it should be implemented.

We first introduce some potential ethical issues concerning AI in liver allocation, before analysing a pilot survey including online responses from 172 UK laypeople, recruited through Prolific Academic.

Most participants found AI in liver allocation acceptable (69.2%) and would not be less likely to donate their organs if AI was used in allocation (72.7%). Respondents thought AI was more likely to be consistent and less biased compared to humans, although were concerned about the "dehumanisation of healthcare" and whether AI could consider important nuances in allocation decisions. Participants valued accuracy, impartiality, and consistency in a decision-maker, more than interpretability and empathy. Respondents were split on whether AI should be trained on previous decisions or programmed with specific objectives. Whether allocation decisions were made by transplant committee or AI, participants valued consideration of urgency, survival likelihood, life years gained, age, future medication compliance, quality of life, future alcohol use and past alcohol use. On the other hand, the majority thought the following factors were not relevant to prioritisation: past crime, future crime, future societal contribution, social disadvantage, and gender.

There are good reasons to use AI in liver allocation, and our sample of participants appeared to support its use. If confirmed, this support would give democratic legitimacy to the use of AI in this context and reduce the risk that donation rates could be affected negatively. Our findings on specific ethical concerns also identify potential expectations and reservations laypeople have regarding AI in this area, which can inform how AI in liver allocation could be best implemented.

The liver is a specialized organ and plays an important role in our immune system. The liver constitutes parenchymal cells which are hepatocytes and cholangiocytes (60-80%) and non-parenchymal cells like liver sinusoidal endothelial cells (LSECs), hepatic satellite/Ito cells, Kupffer cells, neutrophils, mononuclear cells, T and B lymphocytes (conventional and non-conventional), natural killer cells, and natural killer T (NKT) cells. The liver mounts a rapid and strong immune response, under unfavorable conditions and acts as an immune tolerance to a variety of non-pathogenic antigens. This delicate and dynamic interaction between different kinds of immune cells in the liver maintains a balance between immune screening and immune tolerance. The liver allografts are privileged immunologically; however, allograft rejection is not uncommon and is classified as cell or antibody-mediated. Advancements in transplant immunology help in the prevention of allografts rejection by immune reactions of the host thus leading to better graft and host survival. Fewer patients may not require immunosuppression due to systemic donor-specific T-cell tolerance. The liver tolerance mechanism is poorly studied, and LSEC and unconventional lymphocytes play an important role that dampens T cell response either by inducing apoptosis of cells or inhibiting co-stimulatory pathways. Newer cell-based therapy based on Treg, dendritic cells, and mesenchymal stromal cells will probably change the future of immunosuppression. Various invasive and non-invasive biomarkers and artificial intelligence have also been investigated to predict graft survival, post-transplant complications, and immunotolerance in the future.

Over the past 2 decades, the field of hepatology has witnessed major developments in diagnostic tools, prognostic models, and treatment options making it one of the most complex medical subspecialties. Through artificial intelligence (AI) and machine learning, computers are now able to learn from complex and diverse clinical datasets to solve real-world medical problems with performance that surpasses that of physicians in certain areas. AI algorithms are currently being implemented in liver imaging, interpretation of liver histopathology, noninvasive tests, prediction models, and more. In this review, we provide a summary of the state of AI in hepatology and discuss current challenges for large-scale implementation including some ethical aspects. We emphasize to the readers that most AI-based algorithms that are discussed in this review are still considered in early development and their utility and impact on patient outcomes still need to be assessed in future large-scale and inclusive studies. Our vision is that the use of AI in hepatology will enhance physician performance, decrease the burden and time spent on documentation, and reestablish the personalized patient-physician relationship that is of utmost importance for obtaining good outcomes.

The aim of this study was to determine if machine learning can improve the specificity of detecting transplant hepatic artery pathology over conventional quantitative measures while maintaining a high sensitivity.This study presents a retrospective review of 129 patients with transplanted hepatic arteries. We illustrate how beyond common clinical metrics such as stenosis and resistive index, a more comprehensive set of waveform data (including flow half-lives and Fourier transformed waveforms) can be integrated into machine learning models to obtain more accurate screening of stenosis and occlusion. We present a novel framework of Extremely Randomized Trees and Shapley values, we allow for explainability at the individual level.The proposed framework identified cases of clinically significant stenosis and occlusion in hepatic arteries with a state-of-the-art specificity of 65%, while maintaining sensitivity at the current standard of 94%. Moreover, through 3 case studies of correct and mispredictions, we demonstrate examples of how specific features can be elucidated to aid in interpreting driving factors in a prediction.This work demonstrated that by utilizing a more complete set of waveform data and machine learning methodologies, it is possible to reduce the rate of false-positive results in using ultrasounds to screen for transplant hepatic artery pathology compared with conventional quantitative measures. An advantage of such techniques is explainability measures at the patient level, which allow for increased radiologists' confidence in the predictions.

Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.

Elucidating contributors affecting liver transplant survival is paramount. Current methods offer crude global group outcomes. To refine patient-specific mortality probability estimation and to determine covariate interaction using recipient and donor data, we generated a survival tree algorithm, Recipient Survival After Orthotopic Liver Transplantation (ReSOLT), using United Network Organ Sharing (UNOS) transplant data.

The UNOS database was queried for liver transplants in patients ≥18 years old between 2000 and 2021. Preoperative factors were evaluated with stepwise logistic regression; 43 significant factors were used in survival tree modeling. Graft survival of <7 days was excluded. The data were split into training and testing sets and further validated with 10-fold cross-validation. Survival tree pruning and model selection was achieved based on Akaike information criterion and log-likelihood values. Log-rank pairwise comparisons between subgroups and estimated survival probabilities were calculated.

A total of 122,134 liver transplant patients were included for modeling. Multivariable logistic regression (area under the curve = 0.742, F1 = 0.822) and survival tree modeling returned 8 significant recipient survival factors: recipient age, donor age, recipient primary payment, recipient hepatitis C status, recipient diabetes, recipient functional status at registration and at transplantation, and deceased donor pulmonary infection. Twenty subgroups consisting of combinations of these factors were identified with distinct Kaplan-Meier survival curves (p < 0.001 among all by log rank test) with 5- and 10-year survival probabilities.

Survival trees are a flexible and effective approach to understand the effects and interactions of covariates on survival. Individualized survival probability following liver transplant is possible with ReSOLT, allowing for more coherent patient and family counseling and prediction of patient outcome using both recipient and donor factors.

Critically ill patients presenting with acute on chronic liver failure (ACLF) represent a particularly vulnerable population due to various considerations surrounding the syndrome definition, lack of robust prospective evaluation of outcomes, and allocation of resources such as organs for transplantation. Ninety-day mortality related to ACLF is high and patients who do leave the hospital are frequently readmitted. Artificial intelligence (AI), which encompasses various classical and modern machine learning techniques, natural language processing, and other methods of predictive, prognostic, probabilistic, and simulation modeling, has emerged as an effective tool in various areas of healthcare. These methods are now being leveraged to potentially minimize physician and provider cognitive load and impact both short-term and long-term patient outcomes. However, the enthusiasm is tempered by ethical considerations and a current lack of proven benefits. In addition to prognostic applications, AI models can likely help improve the understanding of various mechanisms of morbidity and mortality in ACLF. Their overall impact on patient-centered outcomes and countless other aspects of patient care remains unclear. In this review, we discuss various AI approaches being utilized in healthcare and discuss the recent and expected future impact of AI on patients with ACLF through prognostic modeling and AI-based approaches.