PDF(Pubmed)
Abstract：
BACKGROUND: The study aimed to develop and validate a deep learning-based Computer Aided Triage (CADt) algorithm for detecting pleural effusion in chest radiographs using an active learning (AL) framework. This is aimed at addressing the critical need for a clinical grade algorithm that can timely diagnose pleural effusion, which affects approximately 1.5 million people annually in the United States.
METHODS: In this multisite study, 10,599 chest radiographs from 2006 to 2018 were retrospectively collected from an institution in Taiwan to train the deep learning algorithm. The AL framework utilized significantly reduced the need for expert annotations. For external validation, the algorithm was tested on a multisite dataset of 600 chest radiographs from 22 clinical sites in the United States and Taiwan, which were annotated by three U.S. board-certified radiologists.
RESULTS: The CADt algorithm demonstrated high effectiveness in identifying pleural effusion, achieving a sensitivity of 0.95 (95% CI: [0.92, 0.97]) and a specificity of 0.97 (95% CI: [0.95, 0.99]). The area under the receiver operating characteristic curve (AUC) was 0.97 (95% DeLong\'s CI: [0.95, 0.99]). Subgroup analyses showed that the algorithm maintained robust performance across various demographics and clinical settings.
CONCLUSIONS: This study presents a novel approach in developing clinical grade CADt solutions for the diagnosis of pleural effusion. The AL-based CADt algorithm not only achieved high accuracy in detecting pleural effusion but also significantly reduced the workload required for clinical experts in annotating medical data. This method enhances the feasibility of employing advanced technological solutions for prompt and accurate diagnosis in medical settings.
METHODS: In this multisite study, 10,599 chest radiographs from 2006 to 2018 were retrospectively collected from an institution in Taiwan to train the deep learning algorithm. The AL framework utilized significantly reduced the need for expert annotations. For external validation, the algorithm was tested on a multisite dataset of 600 chest radiographs from 22 clinical sites in the United States and Taiwan, which were annotated by three U.S. board-certified radiologists.
RESULTS: The CADt algorithm demonstrated high effectiveness in identifying pleural effusion, achieving a sensitivity of 0.95 (95% CI: [0.92, 0.97]) and a specificity of 0.97 (95% CI: [0.95, 0.99]). The area under the receiver operating characteristic curve (AUC) was 0.97 (95% DeLong\'s CI: [0.95, 0.99]). Subgroup analyses showed that the algorithm maintained robust performance across various demographics and clinical settings.
CONCLUSIONS: This study presents a novel approach in developing clinical grade CADt solutions for the diagnosis of pleural effusion. The AL-based CADt algorithm not only achieved high accuracy in detecting pleural effusion but also significantly reduced the workload required for clinical experts in annotating medical data. This method enhances the feasibility of employing advanced technological solutions for prompt and accurate diagnosis in medical settings.
摘要：
背景：该研究旨在开发和验证基于深度学习的计算机辅助分类（CADt）算法，该算法用于使用主动学习（AL）框架检测胸片中的胸腔积液。这旨在解决对能够及时诊断胸腔积液的临床级算法的关键需求。每年影响美国约150万人。
方法：在这项多中心研究中，从台湾一家机构回顾性地收集了2006年至2018年的10599张胸片，以训练深度学习算法。使用的AL框架大大减少了对专家注释的需求。对于外部验证，该算法在来自美国和台湾22个临床站点的600张胸片的多站点数据集上进行了测试，由三名美国委员会认证的放射科医生注释。
结果：CADt算法在识别胸腔积液方面表现出很高的有效性，灵敏度为0.95（95%CI：[0.92，0.97]），特异性为0.97（95%CI：[0.95，0.99]）。受试者工作特征曲线下面积(AUC)为0.97(95%DeLong'sCI:[0.95,0.99])。亚组分析表明，该算法在各种人口统计学和临床设置中保持了稳健的性能。
结论：本研究为开发临床级CADt方案诊断胸腔积液提供了一种新方法。基于AL的CADt算法不仅在检测胸腔积液方面取得了较高的准确性，而且显着减少了临床专家注释医学数据所需的工作量。这种方法增强了在医疗环境中采用先进技术解决方案进行及时准确诊断的可行性。
方法：在这项多中心研究中，从台湾一家机构回顾性地收集了2006年至2018年的10599张胸片，以训练深度学习算法。使用的AL框架大大减少了对专家注释的需求。对于外部验证，该算法在来自美国和台湾22个临床站点的600张胸片的多站点数据集上进行了测试，由三名美国委员会认证的放射科医生注释。
结果：CADt算法在识别胸腔积液方面表现出很高的有效性，灵敏度为0.95（95%CI：[0.92，0.97]），特异性为0.97（95%CI：[0.95，0.99]）。受试者工作特征曲线下面积(AUC)为0.97(95%DeLong'sCI:[0.95,0.99])。亚组分析表明，该算法在各种人口统计学和临床设置中保持了稳健的性能。
结论：本研究为开发临床级CADt方案诊断胸腔积液提供了一种新方法。基于AL的CADt算法不仅在检测胸腔积液方面取得了较高的准确性，而且显着减少了临床专家注释医学数据所需的工作量。这种方法增强了在医疗环境中采用先进技术解决方案进行及时准确诊断的可行性。
