Artificial intelligence for non‐polypoid colorectal neoplasms

Abstract

The miss rate of flat advanced colorectal neoplasia is still unacceptably high, especially in the Western setting, notwithstanding the widespread implementation of quality improvement programs and training. It is well known that flat morphology is associated with miss rate of colorectal neoplasia, and that this subset of lesions often shows a more aggressive biological behaviour. Artificial intelligence (AI) applied to the detection of colorectal neoplasia has been shown to increase adenoma detection rate, consistently across all lesion sizes and locations in the colon. However, there is still uncertainty whether AI can reduce the miss rate of flat advanced neoplasia, mainly because all published trials report a low number of flat colorectal lesions in their training sets, and this could reduce AI accuracy for this subset of lesions. In addition, flat lesions have different morphologies with variable prevalence and potentially different accuracy in their detection. For example, the subtle appearance and rarer frequency of a non-granular laterally spreading tumor (LST) could be much harder to identify than a granular mixed LST. In this review, we present a summary of the evidence on the role of AI in the identification of colorectal flat neoplasia, with a focus on data regarding presence of LSTs in the training/validation sets of the AI systems currently available on the market.

Artificial intelligence-aided colonoscopy: Recent developments and future perspectives

“…Another domain in which CADe performance has yet to be improved is the detection of non-polypoid lesions[ 32 ]. These colorectal lesions account for a large portion of missed colorectal neoplasia and may be associated with a more aggressive biological behaviour.…”

“…These colorectal lesions account for a large portion of missed colorectal neoplasia and may be associated with a more aggressive biological behaviour. A recent review[ 32 ] has shown that among the published RCTs on CADe systems, some of them did not report the number of flat lesions included in the training sets and others did not report sub-analysis on the performance of AI specifically for flat lesions. The authors concluded that in future CADe systems, development and refinement, additional training and validation for the recognition of the individual subtypes of non-polypoid lesions, especially for non-granular lateral spreading tumors (LST-NG), is urgently needed.…”

AI everywhere in endoscopy, not only for detection and characterization

“…Let’s be honest! Most AI engines in colonoscopy have never been presented with enough cases of rarer but highly clinically relevant lesions, such as non-granular laterally spreading tumors or depressed invasive cancers 9 .…”

“…Let's be honest! Most AI engines in colonoscopy have never been presented with enough cases of rarer but highly clinically relevant lesions, such as non-granular laterally spreading tumors or depressed invasive cancers [9]. Annotation-related burden is primary responsible for the persistent delay in AI implementation throughout the field of gastrointestinal endoscopy.…”

Defining the next steps for artificial intelligence in colonoscopy

“…Large flat or minimally elevated laterally spreading lesions are under-represented in existing training datasets but have a higher rate of covert malignancy or progression to cancer than diminutive polyps. CADe has been shown to increase the detection of diminutive polyps; however, detection of the inconspicuous laterally spreading lesions and advanced adenomas may have a greater influence on cancer risk reduction [5]. Endoscopists with high ADRs detect high rates of both advanced adenomas and diminutive polyps [6], but a recent meta-analysis of AI CADe only showed a trend toward increased detection of advanced adenomas [4].…”