Artificial intelligence in anal fistula: mapping evidence to IDEAL stages.

Abstract

Artificial intelligence (AI) is increasingly applied in colorectal and anorectal surgery, particularly for complex conditions such as anal fistula (AF). Conventional diagnostic and therapeutic approaches remain limited by intricate anatomy, high recurrence risk, and the need to preserve continence. This narrative review, evaluates the role of AI in AF and related anorectal disorders, with evidence mapped to the IDEAL (idea, development, exploration, assessment, and long-term follow-up) framework (stages 1-4, with stage 2 subdivided into 2a and 2b). Relevant literature was identified through targeted searches of PubMed, Embase, and Scopus. Studies investigating AI applications in AF or related anorectal conditions, including imaging, surgical planning, predictive modeling, and functional assessment, were included. Evidence was categorized according to IDEAL stages, ranging from proof-of-concept to long-term quality assurance. AI demonstrates potential across 4 key domains: (1) preoperative imaging (stages 1-2b); (2) intraoperative planning and assistance (stages 1-2a); (3) predictive modeling (stages 2a-2b); and (4) broader anorectal applications, including anorectal manometry and the EndoFLIP (endoluminal functional lumen imaging probe) procedure (stages 1-2b). Feasibility studies report high diagnostic performance, particularly for magnetic resonance imaging and computed tomography-based deep learning models; however, these findings are constrained by small sample sizes, limited external validation, and challenges related to workflow integration. Overall, AI has the potential to enhance diagnostic accuracy, surgical planning, and functional assessment in AF and related disorders. However, most studies remain within early IDEAL stages (1-2b), highlighting the need for multicenter validation, cost-effectiveness analyses, and robust ethical frameworks before widespread clinical implementation.