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

• Published in: Healthcare (Basel) 2022-01, Vol.10 (1), p.169
• Main Authors: Lee, Chieh, Lin, Tsung-Hsing, Lin, Chen-Ju, Kuo, Chang-Fu, Pai, Betty Chien-Jung, Cheng, Hao-Tsai, Lai, Cheng-Chou, Chen, Tsung-Hsing
• Format: Article
• Language: English
• Subjects: Abdomen; Age; Artificial intelligence; Asymptomatic; Blood pressure; Cancer; Colonoscopy; Colorectal cancer; colorectal polyp; Diabetes; Disease; Helicobacter pylori infection; Hypertension; Hypotheses; Infections; Logistics; Machine learning; non-invasive; Overweight; Patients; Polyps; precancerous lesions; Risk factors; risk stratifying tool; teeth disease; Ultrasonic imaging; Workloads
• ISSN: 2227-9032; 2227-9032
• DOI: 10.3390/healthcare10010169

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, infection, hypertension, gallbladder polyps/stone, and BMI and colorectal polyps were observed ( < 0.0001), regardless of sex, whereas significant findings were noted in men with tooth disease ( = 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 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.