Development of Artificial Intelligence (AI) based algorithms to classify the Pneumoconioses

开发基于人工智能 (AI) 的算法来对尘肺病进行分类

• 批准号: 10709621
• 负责人: Adam M Alessio
• 金额: $ 20.16万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2024-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10709621
• 关键词: Development; Artificial; Intelligence; AI; based

Project Summary Pneumoconiosis is a major occupational lung disease. Medical screening programs of workers exposed to asbestos, coal and silica and the compensation program for Black Lung Benefits require the use of the International Labor Organization (ILO) guidelines to classify radiographs for pneumoconiosis. NIOSH has developed a certification program to standardize the classification. Despite the use of certified B readers in screening and compensation programs, the small number of certified B readers, inter- and intra-reader variability, and potential financial conflict of interest have remained important challenges. There is a pressing need for a system to improve the objective and consistent classification of the pneumoconioses. Artificial intelligence (AI)- based models have demonstrated value for other lung diseases such as lung lesions, edema, and pneumonia. Our study aims to develop AI-based models to assist in the classification of radiographs for the pneumoconioses according to the ILO guidelines. Aim 1 will curate a novel set of expert classified chest radiographs with and without pneumoconiosis for training AI models. Aim 2 will develop machine learned methods including pre- trained Convolutional Neural Network (CNN) methods and hybrid CNN methods combined with handpicked features to distinguish parenchymal abnormalities and pleural abnormalities from normal radiographs. Aim 3 will further classify pneumoconiosis radiographs based on the ILO classification guideline by four major categories of small opacities; affected zones of the lung and shape; three sizes of large opacities and three subtypes of pleural abnormalities. In this aim, Deep Learning (DL) algorithms including Bayesian deep learning and Category-wise residual attention learning (CRAL) algorithm will be developed for uncertainty estimation and higher prediction accuracy in multi-class and multi-label classification problem. Our project will be the first study in the US to develop AI algorithms to classify pneumoconiosis based on the ILO guidelines. Particular attention in algorithm development will be given to the classification of borderline radiographs (i.e. profusion, 0/1 vs. 1/0) with estimated uncertainties. The AI algorithms developed in this study will be tested using a new set of radiographs with expectation of classifying pneumoconiosis based on ILO guideline with high accuracy especially for individuals with an early stage of pneumoconiosis. The project aligns with the NIOSH Research to Practice (r2p) approach, as the results of the proposed algorithms will be shared with NIOSH for dissemination to B readers. Computer-aided algorithms developed in this study will provide an objective and consistent classification that will assist in addressing the problems of small number of certified B readers, inter- and intra- reader variability, and potential financial conflict of interest.

项目概要 尘肺病是一种主要的职业性肺部疾病。接触过的工人的医疗筛查计划 石棉、煤炭和二氧化硅以及黑肺福利补偿计划要求使用 国际劳工组织 (ILO) 放射线照片对尘肺病进行分类的指南。 NIOSH 有 制定了认证计划来标准化分类。尽管使用经过认证的 B 阅读器 筛选和补偿计划、经过认证的 B 读者数量较少、读者之间和内部的差异、 潜在的财务利益冲突仍然是重要的挑战。迫切需要一个 系统以提高尘肺病分类的客观性和一致性。人工智能（AI）- 基于模型的研究已证明对其他肺部疾病（例如肺部病变、水肿和肺炎）的价值。我们的 研究旨在开发基于人工智能的模型，以协助尘肺病的放射线照片分类 根据国际劳工组织的指导方针。目标 1 将策划一套新颖的专家分类胸部 X 线照片，其中包含和 没有尘肺病来训练AI模型。目标 2 将开发机器学习方法，包括预 训练有素的卷积神经网络（CNN）方法和混合CNN方法与精心挑选的相结合 区分实质异常和胸膜异常与正常放射线照片的特征。目标3将 根据ILO分类指南将尘肺病X光片进一步分类为四大类 小的不透明度；肺部受影响的区域和形状；三种尺寸的大阴影和三种胸膜亚型 异常。为了实现这一目标，深度学习（DL）算法包括贝叶斯深度学习和类别明智 将开发残余注意力学习（CRAL）算法用于不确定性估计和更高的预测 多类和多标签分类问题的准确性。 我们的项目将是美国第一个开发人工智能算法来对尘肺病进行分类的研究 关于国际劳工组织的指导方针。算法开发中将特别注意分类 具有估计不确定性的边界射线照片（即丰富，0/1 与 1/0）。开发的人工智能算法 这项研究将使用一组新的放射线照片进行测试，期望根据以下标准对尘肺病进行分类： 国际劳工组织的指南具有很高的准确性，特别是对于早期尘肺病患者。项目 与 NIOSH 研究实践 (r2p) 方法保持一致，因为所提出算法的结果将是 与 NIOSH 共享，以便向 B 读者传播。本研究中开发的计算机辅助算法将 提供客观、一致的分类，有助于解决少数群体的问题 经过认证的 B 读者、读者之间和内部的差异以及潜在的财务利益冲突。