Singh Yuvaraj, Gogtay Maya, Yekula Anuroop, Soni Aakriti, Mishra Ajay Kumar, Tripathi Kartikeya, Abraham G M
Department of Internal Medicine, Saint Vincent Hospital, Worcester, MA 01608, United States.
Hospice and Palliative Medicine, University of Texas Health-San Antonio, San Antonio, TX 78201, United States.
World J Hepatol. 2023 Jan 27;15(1):107-115. doi: 10.4254/wjh.v15.i1.107.
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).
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.
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.
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.
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.
丙型肝炎病毒以其致癌潜力而闻名，尤其是在肝细胞癌和非霍奇金淋巴瘤方面。多项研究表明，慢性丙型肝炎（CHC）患者患结直肠癌（CRC）的风险增加。
分析这种正相关关系，并使用机器学习（ML）算法开发一种基于人工智能（AI）的工具，将这些患者群体分层为CRC/腺瘤检测的风险组。
为开发AI自动计算器，我们应用ML训练模型，以预测结肠镜检查中腺瘤检测的概率和数量。数据集按70:30的比例分为训练集和内部验证集。使用Scikit-learn标准缩放器对连续变量的值进行缩放。结肠镜检查结果用作金标准，并使用深度学习架构训练六个ML模型进行预测。使用Flask（可定制的Python框架）应用程序编程接口（API）将准确率最高的训练好的ML模型部署为Web应用程序。最后，使用Heroku将基于Web的API部署到https://adenomadetection.herokuapp.com。
415例患者中，206例有结肠镜检查结果。在内部验证中，伯努利朴素贝叶斯模型预测腺瘤检测概率的准确率最高，为56%，精确率为55%，召回率为55%，F1值为54%。支持向量回归器预测腺瘤数量的平均绝对误差最小，为0.905。
我们基于AI的工具可以帮助医疗服务提供者将CHC患者分层，以便早期转诊进行结肠镜筛查。除了提供数字百分比外，该计算器还可以对胃肠病学家可能发现的腺瘤性息肉数量进行评估，从而提高腺瘤检测率。
