Artificial Intelligence Models Could Enhance the Diagnostic Accuracy (DA) of Fecal Immunochemical Test (FIT) in the Detection of Colorectal Adenoma in a Screening Setting

Background/aim: This study evaluated the diagnostic accuracy (DA) for colorectal adenomas (CRA), screened by fecal immunochemical test (FIT), using five artificial intelligence (AI) models: logistic regression (LR), support vector machine (SVM), neural network (NN), random forest (RF), and gradient boosting machine (GBM). These models were tested together with clinical features categorized as low-risk (lowR) and high-risk (highR).

Patients and methods: The colorectal neoplasia (CRN) screening cohort of 5,090 patients included 222 CRA patients and 264 non-CRA patients. Three consecutive fecal samples from each individual were analyzed by two fecal occult blood (FOB) assays. Five AI models including clinical features of CRN patients and CV test results were used to test the DA for CRA measured by receiving operating characteristic (ROC) curves.

Results: In conventional ROC analysis, the area under the curve (AUC) values for different AI models ranged from 0.659 and 0.691 (for AIs with LR and SVM), while the highest AUC values were reached by NN, RF, and GBM models (0.809, 0.840, and 0.858, respectively). In the hierarchical summary ROC (HSROC) analysis, the AUC values were as follows: i) with lowR variables, AUC=0.508; ii) with highR variables, AUC=0.566 and iii) with all AI models, AUC= 0.789. The differences in AUC values were: between i) and ii) p=0.008; between i) and iii) p<0.0001 and between ii) and iii) p<0.0001.

Conclusion: In detection of CRA, the AI models proved to be superior to the diagnostic features without AI. This is the first study to report that DA in the diagnosis of CRA can be enhanced by AI models that include clinical data of the patients and results of FIT test.