Obesity and endometriosis are two very common entities, yet there is uncertainty on their exact relationship. Observational studies have repeatedly shown an inverse correlation between endometriosis and a low body mass index (BMI). However, obesity does not protect against endometriosis and on the contrary an increased BMI may lead to more severe forms of the disease. Besides, BMI is not accurate in all cases of obesity. Consequently, other anthropometric and phenomic traits have been studied, including body adiposity content, as well as the effect of BMI early in life on the manifestation of endometriosis in adulthood. Some studies have shown that the phenotypic inverse correlation between the two entities has a genetic background; however, others have indicated that certain polymorphisms are linked with endometriosis in females with increased BMI. The advent of metabolic bariatric surgery and pertinent research have led to the emergence of biomolecules that may be pivotal in understanding the pathophysiological interaction of the two entities, especially in the context of angiogenesis and inflammation. Future research should focus on three objectives: detection and interpretation of obesity-related biomarkers in experimental models with endometriosis; integration of endometriosis-related queries into bariatric registries; and multidisciplinary approach and collaboration among specialists.

Neuroendocrine neoplasms (NENs) and tumors (NETs) are rare neoplasms that may affect any part of the gastrointestinal system. In this scoping review, we attempt to map existing evidence on the role of artificial intelligence, machine learning and deep learning in the diagnosis and management of NENs of the gastrointestinal system. After implementation of inclusion and exclusion criteria, we retrieved 44 studies with 53 outcome analyses. We then classified the papers according to the type of studied NET (26 Pan-NETs, 59.1%; 3 metastatic liver NETs (6.8%), 2 small intestinal NETs, 4.5%; colorectal, rectal, non-specified gastroenteropancreatic and non-specified gastrointestinal NETs had from 1 study each, 2.3%). The most frequently used AI algorithms were Supporting Vector Classification/Machine (14 analyses, 29.8%), Convolutional Neural Network and Random Forest (10 analyses each, 21.3%), Random Forest (9 analyses, 19.1%), Logistic Regression (8 analyses, 17.0%), and Decision Tree (6 analyses, 12.8%). There was high heterogeneity on the description of the prediction model, structure of datasets, and performance metrics, whereas the majority of studies did not report any external validation set. Future studies should aim at incorporating a uniform structure in accordance with existing guidelines for purposes of reproducibility and research quality, which are prerequisites for integration into clinical practice.

During the past several years, there has been a shift in terminology from bariatric surgery alone to bariatric and metabolic surgery (BMS). More than a change in name, this signifies a paradigm shift that incorporates the metabolic effects of operations performed for weight loss and the amelioration of related medical problems. Metabolomics is a relatively novel concept in the field of bariatrics, with some consistent changes in metabolite concentrations before and after weight loss. However, the abundance of metabolites is not easy to handle. This is where artificial intelligence, and more specifically deep learning, would aid in revealing hidden relationships and would help the clinician in the decision-making process of patient selection in an individualized way.

Background: Machine learning (ML) and other applications of artificial intelligence (AI) are revolutionizing medicine, particularly in the field of surgery. These models have the potential to outperform traditional predictive tools, aiding clinicians in decision making and enhancing operative safety through improved patient selection. Methods: A systematic search was conducted across PubMed/MEDLINE and Google Scholar, guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement, to identify studies employing ML and AI algorithms to predict postoperative complications following metabolic bariatric surgery (MBS). The search included primary studies published in English up to November 2024. The area under the receiver operating characteristic curve (AUROC) was used as a surrogate metric for algorithm performance, with values exceeding 0.8 considered clinically significant; however, studies were not excluded based on AUROC thresholds. Results: The search identified 23 studies meeting the inclusion criteria. These were categorized into seven domains: general complications (8 studies, 34.8%), readmissions after MBS (4 studies, 17.4%), hemorrhage (1 study, 4.3%), leaks (1 study, 4.3%), venous thromboembolism (3 studies, 13.0%), nutritional deficiencies (4 studies, 17.4%), and miscellaneous complications such as gastroesophageal reflux disease, gallbladder disease, and major adverse cardiovascular events (MACE) (3 studies, 13.0%). The studies spanned from 2007 to 2024, with 87.0% (20/23) published in or after 2019. In total, 87 AI/ML algorithms were analyzed. While several studies reported AUROC values exceeding 0.7, the highest achieved was 0.94. However, most studies exhibited methodological limitations, including a lack of external validation and inadequate handling of imbalanced datasets, where complication events were markedly fewer than non-events. Conclusions: While AI and ML approaches generally outperform traditional predictive models in forecasting postoperative complications following MBS, few algorithms demonstrated clinically significant performance with AUROC values above 0.8. Future research should adopt more rigorous methodologies and implement strategies to address imbalanced datasets, ensuring broader clinical applicability of AI/ML tools.