Supplementary Material for: Utilizing Deep Learning to Identify Electron-Dense Deposits in Renal Biopsy Electron Microscopy Images

Introduction: Electron microscopy (EM) is a crucial technique for identifying and distinguishing the specific location of deposits within glomeruli. Manual classification of these deposit locations are not only time-consuming but also yields inconsistent results among pathologists. This study aimed to develop a deep learning -based platform to automatically classify the locations of electron-dense deposits in EM images of kidney biopsies.
Methods: We retrospectively collected 4303 EM images at magnifications of 4000X to 8000X from 1039 kidney biopsies performed on native kidneys at the Renal Pathology of King Medical Diagnostics Center of Guangzhou between June 1, 2022, and July 2, 2022. EM images were independently evaluated by pathologists Zhu and Luo for electron-dense deposits, categorized into mesangial, subepithelial, intramembranous, and subendothelial if present. These evaluations served as ground truth for model training and evaluation. Of these, 3443 EM images were allocated to the training group and 860 to the test group. The ResNet18 architecture was selected for our task. To evaluate the model's classification capability, we created a binary classification model to identify the presence of deposits in EM images. Furthermore, we implemented a subnet classification network to predict the probability of mesangial, subepithelial, intramembranous, and subendothelial deposits. Four renal pathologists (two EM pathologists and two comprehensive renal pathologists) were invited to compare their agreement with the deep learning model. Comparing deep learning models against pathologists with Cohen's Kappa and accuracy.
Results: The deep learning model can accurately identify the presence of electron-dense deposits in EM images, with an area under the receiver operating characteristic curve (AUC) of 0.959 and an accuracy of 0.899. The classification subnet trained to identify mesangial, subepithelial, intramembranous, and subendothelial deposits yielded AUCs of 0.928, 0.987, 0.986, and 0.944, with accuracies of 0.880, 0.962, 0.959, and 0.883, respectively. Subepithelial and intramembranous deposits had near-perfect agreement, while mesangial and subendothelial deposits showed substantial agreement with the ground truth. The accuracy of deep learning models in assessing the presence and locations of deposits were lower than that of EM pathologists but higher than that of comprehensive renal pathologists. A web platform has been developed in which users can upload EM images of renal biopsies to receive probabilities regarding the four locations of deposits based on our algorithm.
Conclusion: We successfully developed a web platform for the automated assessment of the locations of electron-dense deposits in kidney biopsy EM images. The performance of this model surpasses that of experienced comprehensive renal pathologists, offering an efficient and reliable tool.

引言：电子显微镜（Electron Microscopy，以下简称EM）是识别与区分肾小球（glomerulus，复数形式glomeruli）内沉积物具体位置的关键技术。对沉积物位置进行人工分类不仅耗时，且不同病理医师间的诊断结果一致性较差。本研究旨在开发一款基于深度学习的平台，以自动分类肾活检EM图像中的电子致密沉积物位置。
方法：2022年6月1日至7月2日期间，从广州金域医学检验中心肾脏病理科的1039例自体肾肾活检标本中，回顾性收集了4303张放大倍数为4000×至8000×的EM图像。由病理医师朱某和罗某独立对EM图像中的电子致密沉积物进行评估，若存在沉积物，则将其分为系膜区（mesangial）、上皮下（subepithelial）、膜内（intramembranous）及内皮下（subendothelial）四类。上述评估结果作为模型训练与验证的金标准（ground truth）。其中3443张EM图像划分为训练集，剩余860张划分为测试集。本研究选用ResNet18架构作为模型基础。为评估模型的分类性能，我们构建了二分类模型以识别EM图像中是否存在沉积物。此外，我们搭建了子分类网络，以预测沉积物为系膜区、上皮下、膜内及内皮下类型的概率。邀请4名肾脏病理医师（2名EM病理医师、2名综合肾脏病理医师）与本深度学习模型的诊断结果进行一致性对比，采用科恩kappa系数（Cohen's Kappa）与准确率对深度学习模型与病理医师的诊断结果进行比较。
结果：本深度学习模型可准确识别EM图像中的电子致密沉积物，其受试者工作特征曲线（Receiver Operating Characteristic Curve，ROC）下面积（AUC）为0.959，准确率为0.899。用于识别系膜区、上皮下、膜内及内皮下沉积物的子分类网络，其AUC分别为0.928、0.987、0.986及0.944，准确率分别为0.880、0.962、0.959及0.883。上皮下与膜内沉积物的诊断结果与金标准近乎完全一致，而系膜区及内皮下沉积物的诊断结果与金标准具有较高一致性。深度学习模型在评估沉积物存在与否及位置的准确率，低于EM病理医师，但高于综合肾脏病理医师。本研究已开发一款网页平台，用户可上传肾活检EM图像，通过本算法获取沉积物四类位置的预测概率。
结论：本研究成功开发了一款可自动评估肾活检EM图像中电子致密沉积物位置的网页平台。该模型的性能优于经验丰富的综合肾脏病理医师，可为临床提供一款高效且可靠的辅助工具。