• Artificial intelligence
• Colorectal cancer
• Colorectal surgery
• Eureka
• Navigation
• Nerve

• Humans
• Laparoscopy/methods
• Laparoscopy/education
• Artificial Intelligence
• Colorectal Surgery/education
• Clinical Competence
• Surgery, Computer-Assisted/methods

• 22K16524 JSPS KAKENHI

• Colorectal cancer
• Diagnosis and imaging (inc chromoendoscopy, NBI, iSCAN, FICE, CLE...)
• Endoscopy Lower GI Tract

• Diagnosis
• Liver cirrhosis
• Machine learning
• Peritonitis

• Colorectal cancer
• Diagnosis and imaging (inc chromoendoscopy, NBI, iSCAN, FICE, CLE...)
• Endoscopy Lower GI Tract
• Image and data processing, documentatiton
• Polyps / adenomas / ...
• Quality and logistical aspects

Endoscopy is the cornerstone in the management of digestive diseases. Over the last few decades, technology has played an important role in the development of this field, helping endoscopists in better detecting and characterizing luminal lesions. However, despite ongoing advancements in endoscopic technology, the incidence of missed pre-neoplastic and neoplastic lesions remains high due to the operator-dependent nature of endoscopy and the challenging learning curve associated with new technologies. Artificial intelligence (AI), an operator-independent field, could be an invaluable solution. AI can serve as a “second observer”, enhancing the performance of endoscopists in detecting and characterizing luminal lesions. By utilizing deep learning (DL), an innovation within machine learning, AI automatically extracts input features from targeted endoscopic images. DL encompasses both computer-aided detection and computer-aided diagnosis, assisting endoscopists in reducing missed detection rates and predicting the histology of luminal digestive lesions. AI applications in clinical gastrointestinal diseases are continuously expanding and evolving the entire digestive tract. In all published studies, real-time AI assists endoscopists in improving the performance of non-expert gastroenterologists, bringing it to a level comparable to that of experts. The development of DL may be affected by selection biases. Studies have utilized different AI-assisted models, which are heterogeneous. In the future, algorithms need validation through large, randomized trials. Theoretically, AI has no limit to assist endoscopists in increasing the accuracy and the quality of endoscopic exams. However, practically, we still have a long way to go before standardizing our AI models to be accepted and applied by all gastroenterologists.

• Artificial intelligence
• Digestive tract
• Gastroenterology
• Gastroscopy
• Coloscopy

• Computer-aided diagnosis
• Endoscopic resection
• T1 colorectal cancer
• Treatment strategy

• Humans
• Colorectal Neoplasms/pathology
• Colorectal Neoplasms/therapy
• Colorectal Neoplasms/diagnosis
• Artificial Intelligence
• Lymphatic Metastasis
• Neoplasm Staging
• Neoplasm Invasiveness
• Colonoscopy/methods
• Lymph Node Excision

• colorectal cancer
• colorectal polyp
• precision
• precision endoscopy

• Humans
• Colonic Polyps/surgery
• Colonic Polyps/pathology
• Colonic Polyps/diagnostic imaging
• Colonoscopy/methods
• Colorectal Neoplasms/surgery
• Colorectal Neoplasms/pathology
• Colorectal Neoplasms/diagnostic imaging
• Colorectal Neoplasms/diagnosis
• Microscopy, Confocal
• Precision Medicine

The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.

• Artificial intelligence
• Gastrointestinal malignancies
• Machine learning
• Helicobacter pylori
• State-of-the-art deep learning models

• Artificial intelligence
• Colorectal neoplasms
• Deep learning
• Lymphatic metastasis

• Artificial intelligence
• Colonoscopy
• Colorectal cancer
• Computer-aided diagnosis
• Invasion depth

• Humans
• Diagnosis, Computer-Assisted/methods
• Endoscopy/methods
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/pathology
• Diagnostic Tests, Routine
• Computers

• Humans
• Colorectal Neoplasms/diagnostic imaging
• Computers
• Diagnosis, Computer-Assisted
• Endoscopy/methods

• Artificial intelligence
• Colorectal cancers
• Intestinal carcinoma
• Machine learning
• Malignant
• Polyps

• Attention mechanism
• Colon cancer
• Colon lesions classification
• Dual branch network
• Endoscopic images
• Gaussian loss function

• Swin Transformer
• classification
• colorectal adenoma
• pathology
• whole slide image

• endoscopy
• translational research

• artificial intelligence
• colonoscopy
• colorectal cancer
• invasion depth

• Adenoma Detection Rate (ADR)
• Artificial Intelligence (AI)
• Colonoscopy
• Colorectal Cancer (CRC)
• Polyp Detection
• Screening

Deep learning models have been shown to achieve high performance in diagnosing colon cancer compared to conventional image processing and hand-crafted machine learning methods. Hence, several studies have focused on developing hybrid learning, end-to-end, and transfer learning techniques to reduce manual interaction and for labelling the regions of interest. However, these weak learning techniques do not always provide a clear diagnosis. Therefore, it is necessary to develop a clear explainable learning method that can highlight factors and form the basis of clinical decisions. However, there has been little research carried out employing such transparent approaches. This study discussed the aforementioned models for colon cancer diagnosis.

Early detection of colorectal cancer can significantly facilitate clinicians’ decision-making and reduce their workload. This can be achieved using automatic systems with endoscopic and histological images. Recently, the success of deep learning has motivated the development of image- and video-based polyp identification and segmentation. Currently, most diagnostic colonoscopy rooms utilize artificial intelligence methods that are considered to perform well in predicting invasive cancer. Convolutional neural network-based architectures, together with image patches and preprocesses are often widely used. Furthermore, learning transfer and end-to-end learning techniques have been adopted for detection and localization tasks, which improve accuracy and reduce user dependence with limited datasets. However, explainable deep networks that provide transparency, interpretability, reliability, and fairness in clinical diagnostics are preferred. In this review, we summarize the latest advances in such models, with or without transparency, for the prediction of colorectal cancer and also address the knowledge gap in the upcoming technology.

• Artificial intelligence
• Classification
• Computer-assisted diagnosis
• Deep learning
• Detection
• Endoscopy

• Convolutional neural network
• Deep learning
• Pouchitis
• Prediction
• Ulcerative colitis

Confocal microscopy image analysis is a useful method for neoplasm diagnosis. Many ambiguous cases are difficult to distinguish with the naked eye, thus leading to high inter-observer variability and significant time investments for learning this method. We aimed to develop a deep learning-based neoplasm classification model that classifies confocal microscopy images of 10× magnified colon tissues into three classes: neoplasm, inflammation, and normal tissue. ResNet50 with data augmentation and transfer learning approaches was used to efficiently train the model with limited training data. A class activation map was generated by using global average pooling to confirm which areas had a major effect on the classification. The proposed method achieved an accuracy of 81%, which was 14.05% more accurate than three machine learning-based methods and 22.6% better than the predictions made by four endoscopists. ResNet50 with data augmentation and transfer learning can be utilized to effectively identify neoplasm, inflammation, and normal tissue in confocal microscopy images. The proposed method outperformed three machine learning-based methods and identified the area that had a major influence on the results. Inter-observer variability and the time required for learning can be reduced if the proposed model is used with confocal microscopy image analysis for diagnosis.

• Japan NBI Expert Team (JNET) classification
• NBI International Colorectal Endoscopic (NICE) classification
• colorectal
• computer-aided diagnosis
• narrow-band imaging (NBI)

Artificial intelligence (AI) is a new branch of computer science involving many disciplines and technologies. Since its application in the medical field, it has been constantly studied and developed. AI includes machine learning and neural networks to create new technologies or to improve existing ones. Various AI supporting systems are available for a personalized and novel strategy for the management of colorectal cancer (CRC). This mini-review aims to summarize the progress of research and possible clinical applications of AI in the investigation, early diagnosis, treatment, and management of CRC, to offer elements of knowledge as a starting point for new studies and future applications.

• Artificial intelligence
• Oncology
• Colorectal cancer
• Digestive surgery
• Computer-assisted diagnosis

• Computer-aided diagnosis (CAD)
• colon cancer
• polyp

Colorectal cancer has the second highest incidence of malignant tumors and is the fourth leading cause of cancer deaths in China. Early diagnosis and treatment of colorectal cancer will lead to an improvement in the 5-year survival rate, which will reduce medical costs. The current diagnostic methods for early colorectal cancer include excreta, blood, endoscopy, and computer-aided endoscopy. In this paper, research on image analysis and prediction of colorectal cancer lesions based on deep learning is reviewed with the goal of providing a reference for the early diagnosis of colorectal cancer lesions by combining computer technology, 3D modeling, 5G remote technology, endoscopic robot technology, and surgical navigation technology. The findings will supplement the research and provide insights to improve the cure rate and reduce the mortality of colorectal cancer.

• Deep learning
• Artificial intelligence
• Image analysis
• Endoscopic
• Colorectal lesions
• Colorectal cancer

• artificial intelligence
• colorectal cancer
• esophageal cancer
• gastric cancer
• gastrointestinal cancer
• hepatocellular cancer
• pancreaticobiliary cancer

• artificial intelligence
• cancer diagnosis
• cancer research
• cancer treatment
• convolutional neural network
• deep learning
• deep neural network
• oncology

• Artificial Intelligence
• Early Detection of Cancer
• Humans
• Medical Oncology
• Neoplasms/diagnosis
• Neoplasms/therapy
• Precision Medicine

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

• Artificial intelligence
• Machine learning
• Deep learning
• Clinical applications
• Gastroenterology

• Artificial Intelligence
• Barrett Esophagus
• Diagnostic Imaging
• Endoscopy
• Gastroenterology
• Humans

Early gastrointestinal (GI) cancer has been the core of clinical endoscopic work. Its early detection and treatment are tightly associated with patients’ prognoses. As a novel technology, artificial intelligence has been improved and applied in the field of endoscopy. Studies on detection, diagnosis, risk, and prognosis evaluation of diseases in the GI tract have been in development, including precancerous lesions, adenoma, early GI cancers, and advanced GI cancers. In this review, research on esophagus, stomach, and colon was concluded, and associated with the process from precancerous lesions to early GI cancer, such as from Barrett’s esophagus to early esophageal cancer, from dysplasia to early gastric cancer, and from adenoma to early colonic cancer. A status quo of research on early GI cancers and artificial intelligence was provided.

• Artificial intelligence
• Early esophageal cancer
• Early gastric cancer
• Early colonic cancer

With the rapid development of science and technology, artificial intelligence (AI) systems are becoming ubiquitous, and their utility in gastroenteroscopy is beginning to be recognized. Digestive endoscopy is a conventional and reliable method of examining and diagnosing digestive tract diseases. However, with the increase in the number and types of endoscopy, problems such as a lack of skilled endoscopists and difference in the professional skill of doctors with different degrees of experience have become increasingly apparent. Most studies thus far have focused on using computers to detect and diagnose lesions, but improving the quality of endoscopic examination process itself is the basis for improving the detection rate and correctly diagnosing diseases. In the present study, we mainly reviewed the role of AI in monitoring systems, mainly through the endoscopic examination time, reducing the blind spot rate, improving the success rate for detecting high-risk lesions, evaluating intestinal preparation, increasing the detection rate of polyps, automatically collecting maps and writing reports. AI can even perform quality control evaluations for endoscopists, improve the detection rate of endoscopic lesions and reduce the burden on endoscopists.

• application
• artificial intelligence
• gastrointestinal endoscopy
• improving
• quality control

• breast cancer
• explainable artificial intelligence (XAI)
• machine learning
• survival analysis
• tumor microenvironment

• advanced gastric cancer
• convolutional neural network
• early gastric cancer
• peptic ulcer
• submucosal tumor

• Artificial intelligence
• Challenges
• Gastroenterology
• Hepatology
• Status

• Artificial Intelligence
• Gastroenterology
• Humans
• Prognosis
• Radiology
• Retrospective Studies

Artificial intelligence is an emerging technology whose application is rapidly increasing in several medical fields. The numerous applications of artificial intelligence in gastroenterology have shown promising results, especially in the setting of gastrointestinal oncology. Therefore, we would like to highlight and summarize the research progress and clinical application value of artificial intelligence in the diagnosis, treatment, and prognosis of colorectal cancer to provide evidence for its use as a promising diagnostic and therapeutic tool in this setting.

• Artificial intelligence
• Colorectal cancer
• Diagnosis
• Treatment
• Prognosis

• ADR, adenoma detection rate
• AI, artificial intelligence
• AMR, adenoma miss rate
• ANN, artificial neural network
• BE, Barrett’s esophagus
• CAD, computer-aided diagnosis
• CADe, CAD studies for colon polyp detection
• CADx, CAD studies for colon polyp classification
• CI, confidence interval
• CNN, convolutional neural network
• CRC, colorectal cancer
• DL, deep learning
• GI, gastroenterology
• HD-WLE, high-definition white light endoscopy
• HDWL, high-definition white light
• ML, machine learning
• NBI, narrow-band imaging
• NPV, negative predictive value
• PIVI, preservation and Incorporation of Valuable Endoscopic Innovations
• SVM, support vector machine
• VLE, volumetric laser endomicroscopy
• WCE, wireless capsule endoscopy
• WL, white light

• Inflammatory response
• Noncoding RNAs
• Super enhancers
• Therapy
• Tumorigenesis