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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
BACKGROUND
Hepatitis C virus is known for its oncogenic potential, especially in hepatocellular carcinoma and non-Hodgkin lymphoma. Several studies have shown that chronic hepatitis C (CHC) has an increased risk of the development of colorectal cancer (CRC).
AIM
To analyze this positive relationship and develop an artificial intelligence (AI)-based tool using machine learning (ML) algorithms to stratify these patient populations into risk groups for CRC/adenoma detection.
METHODS
To develop the AI automated calculator, we applied ML to train models to predict the probability and the number of adenomas detected on colonoscopy. Data sets were split into 70:30 ratios for training and internal validation. The Scikit-learn standard scaler was used to scale values of continuous variables. Colonoscopy findings were used as the gold standard and deep learning architecture was used to train six ML models for prediction. A Flask (customizable Python framework) application programming interface (API) was used to deploy the trained ML model with the highest accuracy as a web application. Finally, Heroku was used for the deployment of the web-based API to https://adenomadetection.herokuapp.com.
RESULTS
Of 415 patients, 206 had colonoscopy results. On internal validation, the Bernoulli naive Bayes model predicted the probability of adenoma detection with the highest accuracy of 56%, precision of 55%, recall of 55%, and F1 measure of 54%. Support vector regressor predicted the number of adenomas with the least mean absolute error of 0.905.
CONCLUSION
Our AI-based tool can help providers stratify patients with CHC for early referral for screening colonoscopy. Along with providing a numerical percentage, the calculator can also comment on the number of adenomatous polyps a gastroenterologist can expect, prompting a higher adenoma detection rate.
Core Tip: Hepatitis C is associated with a wide array of extra-hepatic manifestations. In this study, we evaluated the incidence of colorectal adenomas and adenoma detection rates in hepatitis C patients. We developed an artificial intelligence-based tool to guide physicians in the detection and diagnosis of pre-malignant and malignant colorectal pathologies in these patient populations.
