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©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
Detection of colorectal adenomas using artificial intelligence models in patients with chronic hepatitis C
Yuvaraj Singh, Maya Gogtay, Anuroop Yekula, Aakriti Soni, Ajay Kumar Mishra, Kartikeya Tripathi, GM Abraham
Yuvaraj Singh, Anuroop Yekula, Aakriti Soni, Department of Internal Medicine, Saint Vincent Hospital, Worcester, MA 01608, United States
Maya Gogtay, Hospice and Palliative Medicine, University of Texas Health-San Antonio, San Antonio, TX 78201, United States
Ajay Kumar Mishra, Division of Cardiology, Saint Vincent Hospital, Worcester, MA 01608, United States
Kartikeya Tripathi, Division of Gastroenterology and Hepatology, UMass Chan School-Baystate Medical Center, Springfield, MA 01199, United States
GM Abraham, Division of Infectious Disease, Chief of Medicine, Saint Vincent Hospital, Worcester, MA 01608, United States
Author contributions: Singh Y and Gogtay M contributed to the conceptual design of the study; Singh Y, Gogtay M, Yekula A, and Soni A independently screened the medical records and extracted the data; Tripathi K conducted the statistical analysis; Singh Y, Gogtay M, Yekula A and Soni A contributed to the write-up and submission of the study; Tripathi K, Mishra AK, and Abraham G reviewed the final manuscript; and all authors reviewed and agreed the final content of the article.
Institutional review board statement: We obtained approval from the joint institutional review board at MetroWest Medical Center (IRB#2021-035) before initiation and complied with the study protocol. The IRB waived the requirement of consent from study participants.
Informed consent statement: The institutional review board waived the requirement of consent from study participants.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
https://creativecommons.org/Licenses/by-nc/4.0/ Corresponding author: Yuvaraj Singh, MD, Chief Medical Resident, Staff Physician, Department of Internal Medicine, Saint Vincent Hospital, 123 Summer Street, Worcester, MA 01608, United States. yuvarajmle@gmail.com
Received: September 21, 2022
Peer-review started: September 21, 2022
First decision: October 12, 2022
Revised: October 21, 2022
Accepted: November 14, 2022
Article in press: November 14, 2022
Published online: January 27, 2023
ARTICLE HIGHLIGHTS
Research background
Several studies have shown that chronic hepatitis C virus (HCV) increases the risk of developing colorectal cancer (CRC). We conducted a study to analyze this positive relationship. We developed an artificial intelligence (AI) based tool using machine learning (ML) algorithms to stratify these patient populations into risk groups for CRC/adenoma detection.
Research motivation
We acknowledge the increased applications of AI with ML in medicine. Gastroenterology and hepatology have immense potential for AI integration. Hence, to develop an AI automated calculator, we applied ML to train models to predict the probability and the number of adenomas detected on colonoscopy.
Research objectives
Our objective was the create a readily available AI tool in the form of a calculator that healthcare providers throughout the globe can access to predict the prevalence of adenoma/CRC.
Research methods
We used colonoscopy findings as the gold standard and applied a deep learning architecture to train ML models for prediction. The institutional review board approved the study.
Research results
Data on 415 patients were collected. We discovered a higher incidence of adenoma/CRC in patients with chronic HCV in the untreated patient population. On internal validation, the Bernoulli naive Bayes ML model showed the highest predictive accuracy and recall for adenoma detection rates.
Research conclusions
Our AI-based tool shows an association between HCV and colorectal adenomas. This tool can help providers stratify their patients at increased risk of CRC and prompt early referral for colonoscopy.
Research perspectives
In the future, we would like to see this calculator being used in clinical practice as a preventative measure to increase early diagnosis of high-risk adenomas/CRC.
