Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Department of Anatomical Pathology, University of the Witwatersrand and National Health Laboratory Services, Johannesburg, South Africa.
Am J Clin Pathol. 2021 Jun 17;156(1):117-128. doi: 10.1093/ajcp/aqaa215.
This study aimed to develop and validate a deep learning algorithm to screen digitized acid fast-stained (AFS) slides for mycobacteria within tissue sections.
A total of 441 whole-slide images (WSIs) of AFS tissue material were used to develop a deep learning algorithm. Regions of interest with possible acid-fast bacilli (AFBs) were displayed in a web-based gallery format alongside corresponding WSIs for pathologist review. Artificial intelligence (AI)-assisted analysis of another 138 AFS slides was compared to manual light microscopy and WSI evaluation without AI support.
Algorithm performance showed an area under the curve of 0.960 at the image patch level. More AI-assisted reviews identified AFBs than manual microscopy or WSI examination (P < .001). Sensitivity, negative predictive value, and accuracy were highest for AI-assisted reviews. AI-assisted reviews also had the highest rate of matching the original sign-out diagnosis, were less time-consuming, and were much easier for pathologists to perform (P < .001).
This study reports the successful development and clinical validation of an AI-based digital pathology system to screen for AFBs in anatomic pathology material. AI assistance proved to be more sensitive and accurate, took pathologists less time to screen cases, and was easier to use than either manual microscopy or viewing WSIs.
本研究旨在开发和验证一种深度学习算法，以对组织切片中的抗酸杆菌（AFB）进行数字化酸染色（AFS）载玻片筛查。
共使用了 441 张 AFS 组织材料的全玻片图像（WSI）来开发深度学习算法。在基于网络的画廊格式中，与相应的 WSI 一起显示可能存在抗酸杆菌（AFB）的感兴趣区域，以供病理学家进行审查。将人工智能（AI）辅助分析的另外 138 张 AFS 幻灯片与无 AI 支持的手动显微镜检查和 WSI 评估进行了比较。
在图像斑块水平上，算法性能的曲线下面积为 0.960。与手动显微镜或 WSI 检查相比，更多的 AI 辅助审查发现了 AFB（P <.001）。AI 辅助审查的敏感性、阴性预测值和准确性最高。AI 辅助审查也具有最高的与原始诊断相符的比率，所需时间更短，并且病理学家更容易进行（P <.001）。
本研究报告了一种基于人工智能的数字病理学系统的成功开发和临床验证，该系统可用于筛查解剖病理学材料中的 AFB。AI 辅助证明比手动显微镜检查或查看 WSI 更敏感、更准确，病理学家筛查病例所需的时间更短，使用起来也更容易。
