Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications

Abstract

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.

Keywords: anorectal manometry; artificial intelligence; colonoscopy; coloproctology; high-resolution anoscopy.

Figure 1

Study organization by diagnostic and therapeutic techniques used in coloproctology.

Figure 2

Artificial intelligence model predictions in differentiating an HSIL from an LSIL. HSIL—High-grade squamous intraepithelial lesion; LSIL—Low-grade squamous intraepithelial lesion. Image kindly provided by the working group of Precision Medicine Unir, CHUSJ/FMUP/FEUP.

Figure 3

Examples of various anorectal maneuvers. Image kindly provided by the working group of Precision Medicine Unir, CHUSJ/FMUP/FEUP.

Figure 4

Healthcare-related costs over time with the application of AI in colonoscopy. Initially, these costs are expected to rise due to the upfront investment in equipment and increased detection rates, potentially leading to higher costs for conducting pathological studies. However, in the long run, these costs are expected to decrease, as greater diagnostic confidence may reduce the need for pretreatment biopsies.