A Noninvasive Risk Stratification Tool Build Using an Artificial Intelligence Approach for Colorectal Polyps Based on Annual Checkup Data

Abstract

Colorectal cancer is the leading cause of cancer-related deaths worldwide, and early detection has proven to be an effective method for reducing mortality. The machine learning method can be implemented to build a noninvasive stratifying tool that helps identify patients with potential colorectal precancerous lesions (polyps). This study aimed to develop a noninvasive risk-stratified tool for colorectal polyps in asymptomatic, healthy participants. A total of 20,129 consecutive asymptomatic patients who underwent a health checkup between January 2005 and August 2007 were recruited. Positive relationships between noninvasive risk factors, such as age, Helicobacter pylori infection, hypertension, gallbladder polyps/stone, and BMI and colorectal polyps were observed (p < 0.0001), regardless of sex, whereas significant findings were noted in men with tooth disease (p = 0.0053). A risk stratification tool was developed, for colorectal polyps, that considers annual checkup results from noninvasive examinations. For the noninvasive stratified tool, the area under the receiver operating characteristic curve (AUC) of obese females (males) aged <50 years was 91% (83%). In elderly patients (>50 years old), the AUCs of the stratifying tools were >85%. Our results indicate that the risk stratification tool can be built by using random forest and serve as an efficient noninvasive tool to identify patients requiring colonoscopy.

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

“…Lee et al 23 reported a retrospective study that had limitations such as lack of information on diet and physical activity, but they developed a non-invasive colorectal polyp risk stratification tool using AI. Sex (male, female), age JIMT group (old, young) divided by age 50, and BMI (normal, overweight, obese, divided into 4 stages were combined and divided into 16 subgroups to show discrimination through AUC values; values ranged from 0.61 to 0.91, and were generally high in the obese combination 23 .…”

Artificial Intelligence in Colon Neoplasm Diagnosis: Development, Implementation of Technology, and Initial Results

“…Одним из вариантов «коррекции» человеческого фактора являются аппаратные средства помощи в обнаружении предраковой и онкологической патологии кишечника. В мировой литературе активно обсуждается вопрос применения систем на основе искусственного интеллекта в момент проведения диагностической колоноскопии с целью повышения качества диагностики предраковой и онкологической патологии толстого кишечника [2,7,9,10]. Суть методики состоит в компьютерном анализе получаемого с эндоскопа изображения с применением различных алгоритмов компьютерной обработки данных.…”

“…Суть методики состоит в компьютерном анализе получаемого с эндоскопа изображения с применением различных алгоритмов компьютерной обработки данных. Одна из решаемых задач -это акцентирование внимания врача на патологическом очаге с целью верификации диагноза [2,8,9,10]. В настоящее время появились работы, демонстрирующие эффективность систем искусственного интеллекта в предположении диагноза на основе анализа получаемой картины прямо во время диагностической колоноскопии [2,14].…”