AI Shows Potential for Detecting Mucosal Healing in Ulcerative Colitis

Improving AI Training

The authors found moderate-high levels of heterogeneity among the studies, which limited the quality of the evidence. Only 2 of the 12 studies used an external dataset to validate the AI systems, and 1 evaluated the AI system on a mixed database. However, seven used an internal validation dataset separate from the training dataset.

It is crucial to find a shared consensus on training for AI models, with a shared definition of mucosal healing and cutoff thresholds based on recent guidelines, Dr. Rimondi and colleagues noted. Training data ideally should be on the basis of a broad and shared database containing images and videos with high interobserver agreement on the degree of inflammation, they added.

“We probably need a consensus or guidelines that identify the standards for training and testing newly developed software, stating the bare minimum number of images or videos for the training and testing sections,” Dr. Rimondi said.

In addition, due to interobserver misalignment, an expert-validated database could help serve the purpose of a gold standard, he added.

“In my opinion, artificial intelligence tends to better perform when it is required to evaluate a dichotomic outcome (such as polyp detection, which is a yes or no task) than when it is required to replicate more difficult tasks (such as polyp characterization or judging a degree of inflammation), which have a continuous range of expression,” Dr. Rimondi said.

The authors declared no financial support for this study. Dr. Rimondi and Dr. Gross reported no financial disclosures.

A version of this article appeared on Medscape.com.