Gastrointestinal (GI) diseases pose significant challenges for healthcare systems, largely due to the complexities involved in their detection and treatment. Despite the advancements in deep neural networks, their high computational demands hinder their practical use in clinical environments.

This study aims to address the computational inefficiencies of deep neural networks by proposing a lightweight model that integrates model compression techniques, ConvLSTM layers, and ConvNext Blocks, all optimized through Knowledge Distillation (KD).

A dataset of 6000 endoscopic images of various GI diseases was utilized. Advanced image preprocessing techniques, including adaptive noise reduction and image detail enhancement, were employed to improve accuracy and interpretability. The model's performance was assessed in terms of accuracy, computational cost, and disk space usage.

The proposed lightweight model achieved an exceptional overall accuracy of 99.38 %. It operates efficiently with a computational cost of 0.61 GFLOPs and occupies only 3.09 MB of disk space. Additionally, Grad-CAM visualizations demonstrated enhanced model saliency and interpretability, offering insights into the decision-making process of the model post-KD.

The proposed model represents a significant advancement in the diagnosis of GI diseases. It provides a cost-effective and efficient alternative to traditional deep neural network methods, overcoming their computational limitations and contributing valuable insights for improved clinical application.

Video-capsule endoscopy (VCE) is an efficient tool that has proven to be highly useful in approaching several gastrointestinal diseases. VCE was implemented in Colombia in 2003, however current characterization of patients undergoing VCE in Colombia is limited, and mainly comes from two investigations conducted before the SARS-CoV-2 pandemic period.

To describe the characteristics of patients undergoing VCEs and establish the main indications, findings, technical limitations, and other outstanding features.

A descriptive study was carried out using data from reports of VCE (PillCam SB3 system) use in a Gastroenterology Unit in Bogotá, Colombia between September 2019 and January 2023. Demographic and clinical variables such as indication for the VCE, gastric and small bowel transit times (GTT, SBTT), endoscopic preparation quality, and limitations were described [n (%), median (IQR)].

A total of 133 VCE reports were analyzed. Most were in men with a median age of 70 years. The majority had good preparation (96.2%), and there were technical limitations in 15.8% of cases. The main indications were unexplained anemia (91%) or occult bleeding (23.3%). The median GTT and SBTT were 14 and 30 minutes, respectively. The frequencies of bleeding stigma (3.79%) and active bleeding (9.09%) were low, and the most frequent abnormal findings were red spots (28.3%), erosions (17.6%), and vascular ectasias (12.5%).

VCE showed high-level safety. The main indication was unexplained anemia. Active bleeding was the most frequent finding. Combined with artificial intelligence, VCE can improve diagnostic precision and targeted therapeutic interventions.

The gastrointestinal (GI) tract, particularly the small bowel (SB), can be challenging for novel investigation tools [...].

The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery.

A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted.

In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively).

The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.

During the course of Crohn's disease, the response of mesenteric adipose tissue to the production of inflammatory mediators and bacterial invasion through the intestinal mucosa results in the formation of creeping fat. Creeping fat describes the arresting finger-like projections that surround the inflamed bowel. In this review, the microscopic and macroscopic features of creeping fat and histological evidence for the importance of this tissue are discussed. Moreover, the most recent insights into the radiological assessment of creeping fat in patients with Crohn's disease are reported. Advances in imaging techniques have revolutionized the possibility of visualization and quantification of adipose tissue depots with excellent accuracy. Visceral fat has been significantly correlated with various Crohn's-disease-related outcomes. Despite the difficulties in distinguishing physiologic perienteric fat from creeping fat, the growing interest in fat-wrapping in Crohn's disease has rejuvenated radiologic research. With regard to the noninvasive fat-wrapping assessment, a novel CT enterography-based mesenteric creeping fat index has been developed for the mitigation of the confounding effect of normal retroperitoneal and perienteric adipose tissue. Research on machine learning algorithms and computational radiomics in conjunction with mechanistic studies may be the key for the elucidation of the complex role of creeping fat in Crohn's disease.

Intestinal strictures remain one of the most intractable and common complications of Crohn's disease (CD). Approximately 70% of CD patients will develop fibrotic strictures after 10 years of CD diagnosis. Since specific antifibrotic therapies are unavailable, endoscopic balloon dilation and surgery remain the mainstay treatments despite a high recurrence rate. Besides, there are no reliable methods for accurately evaluating intestinal fibrosis. This is largely due to the fact that the mechanisms of initiation and propagation of intestinal fibrosis are poorly understood. There is growing evidence implying that the pathogenesis of stricturing CD involves the intricate interplay of factors including aberrant immune and nonimmune responses, host-microbiome dysbiosis, and genetic susceptibility. Currently, the progress on intestinal strictures has been fueled by the advent of novel techniques, such as single-cell sequencing, multi-omics, and artificial intelligence. Here, we perform a timely and comprehensive review of the substantial advances in intestinal strictures in 2021, aiming to provide prompt information regarding fibrosis and set the stage for the improvement of diagnosis, treatment, and prognosis of intestinal strictures.

The aim of this study was to better understand the characteristics and etiology of acute nonvariceal upper gastrointestinal bleeding (ANVUGIB) in recent years in this region and to provide evidence-based medical evidence.

100 patients with acute nonvariceal upper gastrointestinal bleeding (ANVUGIB) who met the clinical diagnostic criteria of ANVUGIB admitted to Suzhou First People's Hospital from January 2017 to December 2021 were analyzed, as well as the age difference and change rule. According to age, 100 patients were divided into young (18-39 years), middle-aged (40-59 years), and elderly (60 years and above), and the differences in the three groups were compared. The etiology was confirmed by endoscopic examination and was recorded one by one in a well-designed ANVUGIB case data registration form. Statistical software SPSS 23.0 was used for analysis.

Gastric ulcer was the main cause in the elderly group (50.0%), duodenal ulcer was the main cause in the middle and young groups, and gastrointestinal cancer (7.1%) and marginal ulcer (2.3%) in the elderly group were higher than those in the young group. Nonsteroidal anti-inflammatory drugs (52.3%) were the main inducement in the elderly group, which was significantly higher than in the middle-aged group (13.1%) and the young group (5%) (P < 0.01). Drinking, fatigue, and emotional excitement led to a higher proportion in the middle-aged group and the young group, in comparison to the elderly group (P < 0.01).

Peptic ulcer is the most common cause of acute nonvariceal upper gastrointestinal bleeding, followed by acute gastric mucosal lesions and upper digestive system tumors, compared with nonulcer.

Medical databases usually contain a significant volume of images, therefore search engines based on low-level features frequently used to retrieve similar images are necessary for a fast operation. Color, texture, and shape are the most common features used to characterize an image, however extracting the proper features for image retrievals in a similar manner with the human cognition remains a constant challenge. These algorithms work by sorting the images based on a similarity index that defines how different two or more images are, and histograms are one of the most employed methods for image comparison. In this paper, we have extended the concept of image database to the set of frames acquired following wireless capsule endoscopy (from a unique patient). Then, we have used color and texture histograms to identify very similar images (considered duplicates) and removed one of them for each pair of two successive frames. The volume reduction represented an average of 20% from the initial data set, only by removing frames with very similar informational content.