Prospective pilot trial for evaluating the feasibility of an artificial intelligence algorithm for predicting the risk of colorectal adenoma using health screening questionnaire

Abstract

Background: In colorectal cancer (CRC) screening, artificial intelligence (AI)-based software was developed to predict the risk of adenomas to compensate for the inconvenience and low sensitivity of fecal occult blood tests. This study investigates the applicability of this program for predicting risk using health-screening questionnaire data collected in real clinical settings. Materials and Methods: Using a questionnaire accessible through web/mobile applications, data were collected from 516 examinees from an institution. The risk of colorectal adenoma and high-risk adenomas was then predicted by applying the AI-based algorithm, and the accuracy of the predictions evaluated by comparison with colonoscopy and pathology results. Additionally, the satisfaction survey of the examinees with the use of the program was investigated. Result: The subjects were 49.7% male, median age was 59 years, 35.2% had body mass index over 25, family history of CRC was 8.6%, smoking was 39.7%, and alcohol consumption was 53.2%. Pathological results showed that adenomas were diagnosed in 42.2%, high-risk adenomas in 6.3%, and cancer in 4 patients (0.8%). There was a significant difference in the risk score between the adenoma positive and negative groups (40.8 vs 35.8, P≤0.001), and the accuracy of predicting risk of adenoma was 60.3%. The satisfaction survey received a positive response score of 75% or more. Conclusion: Using a questionnaire in the form of familiar applications has the potential to supplement current screening methods, and is expected to be used in the future for effective CRC prevention through continuous data learning.