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

项目摘要 尘肺病是一种主要的职业性肺病。接触有害物质的工人的健康检查计划 石棉、煤和硅石以及黑肺福利的补偿计划要求使用 国际劳工组织（ILO）尘肺病X线片分类指南。NIOSH已经 制定了一个认证程序，以标准化分类。尽管使用认证的B读者在 筛选和补偿计划，认证的B读者数量少，读者间和读者内的可变性， 以及潜在的财务利益冲突仍然是重要的挑战。目前迫切需要一个 系统，以改善肺尘埃沉病的客观和一致的分类。人工智能（AI）- 基于的模型已经证明了对其他肺部疾病如肺部病变、水肿和肺炎的价值。我们 这项研究旨在开发基于人工智能的模型，以帮助对尘肺病的X光片进行分类 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片， 没有尘肺病的人来训练人工智能模型。Aim 2将开发机器学习方法，包括预 训练卷积神经网络（CNN）方法和混合CNN方法结合手工挑选 特征以区分实质异常和胸膜异常与正常X线片。目标3将 根据国际劳工组织的分类指引，将肺尘埃沉着病X光片进一步分为四个主要类别 肺的受累区域和形状;三种大小的大阴影和三种胸膜炎亚型 异常在这个目标中，深度学习（DL）算法包括贝叶斯深度学习和类别智能 提出了一种用于不确定性估计和更高预测的剩余注意力学习算法 在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的研究， 国际劳工组织的指导方针。在算法开发中，将特别注意以下分类： 具有估计不确定性的边界射线照片（即大量，0/1与1/0）。人工智能算法开发于 这项研究将使用一组新的X光片进行测试，期望根据以下因素对尘肺进行分类： 国际劳工组织指南，特别是对早期尘肺患者的准确性高。项目 与NIOSH从研究到实践（r2 p）方法一致，因为所提出的算法的结果将是 与NIOSH共享以传播给B读者。在这项研究中开发的计算机辅助算法将 提供一个客观和一致的分类，这将有助于解决少数问题， 认证的B读者，读者之间和读者内部的差异，以及潜在的财务利益冲突。

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.