Department of Orthopaedic Surgery, Stanford University School of Medicine, Stanford, California, USA.
Department of Mechanical Engineering, Stanford University School of Engineering, Stanford, California, USA.
J Orthop Res. 2022 Aug;40(8):1801-1809. doi: 10.1002/jor.25201. Epub 2021 Oct 27.
Osteonecrosis of the femoral head (ONFH) is a disease in which inadequate blood supply to the subchondral bone causes the death of cells in the bone marrow. Decalcified histology and assessment of the percentage of empty lacunae are used to quantify the severity of ONFH. However, the current clinical practice of manually counting cells is a tedious and inefficient process. We utilized the power of artificial intelligence by training an established deep convolutional neural network framework, Faster-RCNN, to automatically classify and quantify osteocytes (healthy and pyknotic) and empty lacunae in 135 histology images. The adjusted correlation coefficient between the trained cell classifier and the ground truth was R = 0.98. The methods detailed in this study significantly reduced the manual effort of cell counting in ONFH histological samples and can be translated to other fields of image quantification.
股骨头坏死（ONFH）是一种疾病，其中骨软骨下骨的血液供应不足导致骨髓细胞死亡。脱钙组织学和空骨陷窝百分比的评估用于量化 ONFH 的严重程度。然而，目前手动计数细胞的临床实践是一个繁琐且低效的过程。我们利用人工智能的力量，通过训练一个已建立的深度卷积神经网络框架 Faster-RCNN，自动分类和量化 135 张组织学图像中的成骨细胞（健康和成束的）和空骨陷窝。训练后的细胞分类器与真实值之间的调整相关系数为 R=0.98。本研究中详述的方法大大减少了 ONFH 组织学样本中细胞计数的人工工作量，并且可以转化到其他图像量化领域。
