Eigbire-Molen Odianosen J, Cassol Clarissa A, Kenan Daniel J, Napier Johnathan O H, Burdine Lyle J, Coley Shana M, Sharma Shree G
Arkana Laboratories, 10810 Executive Center Dr. Suite 100, Little Rock, AR 72211, USA.
Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
J Pathol Inform. 2024 May 31;15:100385. doi: 10.1016/j.jpi.2024.100385. eCollection 2024 Dec.
Kidney biopsy is the gold-standard for diagnosing medical renal diseases, but the accuracy of the diagnosis greatly depends on the quality of the biopsy specimen, particularly the amount of renal cortex obtained. Inadequate biopsies, characterized by insufficient cortex or predominant medulla, can lead to inconclusive or incorrect diagnoses, and repeat biopsy. Unfortunately, there has been a concerning increase in the rate of inadequate kidney biopsies, and not all medical centers have access to trained professionals who can assess biopsy adequacy in real time. In response to this challenge, we aimed to develop a machine learning model capable of assessing the percentage cortex of each biopsy pass using smartphone images of the kidney biopsy tissue at the time of biopsy.
747 kidney biopsy cores and corresponding smartphone macro images were collected from five unused deceased donor kidneys. Each core was imaged, formalin-fixed, sectioned, and stained with Periodic acid-Schiff (PAS) to determine cortex percentage. The fresh unfixed core images were captured using the macro camera on an iPhone 13 Pro. Two experienced renal pathologists independently reviewed the PAS-stained sections to determine the cortex percentage. For the purpose of this study, the biopsies with less than 30% cortex were labeled as inadequate, while those with 30% or more cortex were classified as adequate. The dataset was divided into training (=643), validation (=30), and test (=74) sets. Preprocessing steps involved converting High-Efficiency Image Container iPhone format images to JPEG, normalization, and renal tissue segmentation using a U-Net deep learning model. Subsequently, a classification deep learning model was trained on the renal tissue region of interest and corresponding class label.
The deep learning model achieved an accuracy of 85% on the training data. On the independent test dataset, the model exhibited an accuracy of 81%. For inadequate samples in the test dataset, the model showed a sensitivity of 71%, suggesting its capability to identify cases with inadequate cortical representation. The area under the receiver-operating curve (AUC-ROC) on the test dataset was 0.80.
We successfully developed and tested a machine learning model for classifying smartphone images of kidney biopsies as either adequate or inadequate, based on the amount of cortex determined by expert renal pathologists. The model's promising results suggest its potential as a smartphone application to assist real-time assessment of kidney biopsy tissue, particularly in settings with limited access to trained personnel. Further refinements and validations are warranted to optimize the model's performance.
肾活检是诊断医学性肾脏疾病的金标准，但诊断的准确性很大程度上取决于活检标本的质量，特别是获取的肾皮质量。活检不充分，表现为皮质不足或髓质为主，可导致诊断不确定或错误，并需要重复活检。不幸的是，肾活检不充分的发生率令人担忧地增加，而且并非所有医疗中心都有能够实时评估活检充分性的专业人员。为应对这一挑战，我们旨在开发一种机器学习模型，能够在活检时使用肾活检组织中的智能手机图像评估每次活检穿刺的皮质百分比。
从五个未使用的已故供体肾脏中收集了747个肾活检组织条及相应的智能手机宏观图像。每个组织条进行成像、福尔马林固定、切片，并用高碘酸-希夫（PAS）染色以确定皮质百分比。使用iPhone 13 Pro上的微距相机拍摄新鲜未固定的组织条图像。两名经验丰富的肾脏病理学家独立检查PAS染色切片以确定皮质百分比。在本研究中，皮质少于30%的活检被标记为不充分，而皮质为30%或更多的活检被分类为充分。数据集分为训练集（=643）、验证集（=30）和测试集（=74）。预处理步骤包括将高效图像容器iPhone格式图像转换为JPEG、归一化，以及使用U-Net深度学习模型进行肾组织分割。随后，在肾组织感兴趣区域和相应类别标签上训练一个分类深度学习模型。
深度学习模型在训练数据上的准确率达到85%。在独立测试数据集上，该模型的准确率为81%。对于测试数据集中不充分的样本，该模型的灵敏度为71%，表明其有能力识别皮质代表性不足的病例。测试数据集上的受试者操作特征曲线下面积（AUC-ROC）为0.80。
我们成功开发并测试了一种机器学习模型，该模型可根据肾脏病理专家确定的皮质量，将肾活检的智能手机图像分类为充分或不充分。该模型的良好结果表明其作为智能手机应用辅助肾活检组织实时评估的潜力，特别是在专业人员有限的环境中。需要进一步改进和验证以优化模型性能。
