Background/Objectives: Colorectal anastomotic leak (CAL) is one of the most severe postoperative complications in colorectal surgery, impacting patient morbidity and mortality. Current risk assessment methods rely on clinical and intraoperative factors, but no real-time predictive tool exists. This study aimed to develop an artificial intelligence model based on intraoperative laparoscopic recording of the anastomosis for CAL prediction. Methods: A convolutional neural network (CNN) was trained with annotated frames from colorectal surgery videos across three international high-volume centers (Instituto Português de Oncologia de Lisboa, Hospital das Clínicas de Ribeirão Preto, and Royal Liverpool University Hospital). The dataset included a total of 5356 frames from 26 patients, 2007 with CAL and 3349 showing normal anastomosis. Four CNN architectures (EfficientNetB0, EfficientNetB7, ResNet50, and MobileNetV2) were tested. The models’ performance was evaluated using their sensitivity, specificity, accuracy, and area under the receiver operating characteristic (AUROC) curve. Heatmaps were generated to identify key image regions influencing predictions. Results: The best-performing model achieved an accuracy of 99.6%, AUROC of 99.6%, sensitivity of 99.2%, specificity of 100.0%, PPV of 100.0%, and NPV of 98.9%. The model reliably identified CAL-positive frames and provided visual explanations through heatmaps. Conclusions: To our knowledge, this is the first AI model developed to predict CAL using intraoperative video analysis. Its accuracy suggests the potential to redefine surgical decision-making by providing real-time risk assessment. Further refinement with a larger dataset and diverse surgical techniques could enable intraoperative interventions to prevent CAL before it occurs, marking a paradigm shift in colorectal surgery.

Artificial intelligence (AI) has emerged as a transformative tool across several specialties, namely gastroenterology, where it has the potential to optimize both diagnosis and treatment as well as enhance patient care. Coloproctology, due to its highly prevalent pathologies and tremendous potential to cause significant mortality and morbidity, has drawn a lot of attention regarding AI applications. In fact, its application has yielded impressive outcomes in various domains, colonoscopy being one prominent example, where it aids in the detection of polyps and early signs of colorectal cancer with high accuracy and efficiency. With a less explored path but equivalent promise, AI-powered capsule endoscopy ensures accurate and time-efficient video readings, already detecting a wide spectrum of anomalies. High-resolution anoscopy is an area that has been growing in interest in recent years, with efforts being made to integrate AI. There are other areas, such as functional studies, that are currently in the early stages, but evidence is expected to emerge soon. According to the current state of research, AI is anticipated to empower gastroenterologists in the decision-making process, paving the way for a more precise approach to diagnosing and treating patients. This review aims to provide the state-of-the-art use of AI in coloproctology while also reflecting on future directions and perspectives.

Undifferentiated pleomorphic sarcoma (UPS) remains one of the most clinically aggressive and poorly characterized soft-tissue sarcoma subtypes. To uncover distinctive molecular traits for UPS, a multiomics analysis of UPS compared with leiomyosarcoma (LMS) and liposarcoma (LPS) was performed. Transcriptomic profiling revealed that UPS exhibits overexpression of genes encoding succinate dehydrogenase (SDH) subunits, particularly SDHB, SDHC, and SDHD, distinguishing it from LMS and dedifferentiated LPS. This finding was validated using The Cancer Genome Atlas Sarcoma dataset. High SDHB expression in UPS was significantly associated with shorter overall survival, shorter recurrence-free survival, and shorter overall survival from the date of first metastasis. IHC validated elevated SDHB protein levels in UPS and LMS relative to dedifferentiated LPS. Despite overexpression of SDH subunits, metabolomic profiling demonstrated a significantly higher succinate-to-fumarate ratio in UPS, suggesting functional impairment of SDH enzymatic activity potentially due to posttranslational modifications, altered assembly of SDH subunits, or imbalanced tricarboxylic acid flux. This paradoxical phenotype of SDH overexpression with enzymatic dysfunction defines a unique molecular and metabolic subtype of UPS with prognostic significance. Recognition of this distinct SDH-associated molecular and metabolic phenotype provides insights into UPS pathogenesis, identifies a potential novel prognostic biomarker, and suggests a new avenue for metabolic-targeted therapy. Significance: This study identified a paradoxical phenotype of SDH subunit overexpression with functional impairment in UPS, defining a molecular/metabolic subtype associated with poor prognosis. It offers new data on UPS pathogenesis and treatment.

Supplementary Figure 25 from Succinate Dehydrogenase B (SDHB) Overexpression with Enzymatic Dysfunction Defines a Distinct Subtype of Undifferentiated Pleomorphic Sarcoma