基于人工智能的肾小球疾病电镜图像分析系统。

IF 9.7 1区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Pengcheng Ma, Jinbang Li, Zhengyu Zhang, Weihao Qiu, Danyi Li, Jing Wang, Bingbing Li, Shujing Guo, Jin Zhang, Zhijian Cen, Jian Geng, Xiangsheng Huang, Xiaolei Xue, Aihetaimu Aimaier, Huanjiao Liu, Minyi Liang, Hao Chen, Qifeng Jiang, Xiaoyan Su, Tianjun Guan, Yu Tong, Weiyuan Lin, Li Liu, Jun Xu, Jie Lin, Yaping Ye, Li Liang
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引用次数: 0

摘要

重要性:通过透射电子显微镜(TEM)进行肾活检病理对诊断肾小球疾病至关重要,它提供了肾小球基底膜(GBM)厚度、足突(FP)数量和电子致密沉积(EDDs)的关键信息。这些任务既费力又费时。目的:开发并验证一种人工智能(AI)诊断系统——基于TEM图像的AI辅助设备(TEM- aid),该系统可以准确地分割和测量肾小球超微结构(包括GBM、FPs和EDDs),并利用TEM图像确定肾小球疾病亚型。设计、环境和参与者:本诊断研究使用了一个大型多中心队列,包括来自2021年1月至2023年12月6个医疗中心的31 670名慢性肾脏疾病患者的160张 727张TEM图像。TEM-AID在来自1个中心的26 650例患者中进行了培训和验证,并在5020例患者(5个测试集)加上一个人类ai测试集(454例患者代表7种肾小球疾病亚型)上进行了外部测试。数据分析时间为2024年1月至12月。暴露:TEM-AID集成了4个模块。分割结合了YOLO-v8检测,分割任何模型,以及human-in- loop细化,以分割GBMs,足细胞FPs和edd。测量量化了GBM厚度、FP融合程度和EDD沉积部位。采用最小绝对收缩法和选择算子选择深度学习及统计特征叠加分类器进行分类,诊断出免疫球蛋白a肾病、膜性肾病、狼疮肾炎、糖尿病肾病、微小改变病、系膜增生性肾小球肾炎、基底膜薄肾病等7种肾小球疾病亚型。主要结果和测量指标:关注的结果包括分割性能(平均交叉-过并度(IOU)、Dice系数)、亚型分类精度、受试者工作特征曲线下面积(AUC)和人-人工智能诊断一致性。结果:共31 670例患者(平均[SD]年龄43.2[16.5]岁;17 372例[54.9%]男性)提供160 727张TEM图像进行分析。分割的平均(SD) IOU为0.835 (0.062),Dice为0.874(0.023)。内部验证的亚型分类准确率为0.911 (95% CI, 0.904 ~ 0.918),外部试验为0.895 ~ 0.914。各队列的宏观auc范围为0.972 ~ 0.989。在454例患者中,TEM-AID准确率(0.886 (95% CI, 0.859-0.912); AUC (0.963 [95% CI, 0.937-0.989])超过临床医生的独立表现。使用TEM-AID时,临床医生的准确率平均提高了11.7%(5.2%)。结论及意义:在这项多中心诊断研究中,TEM- aid精确量化肾小球超微结构,并从TEM图像中确定肾小球疾病亚型,显著提高了诊断效率和准确性。该系统提供了定量评估工具,以支持临床病理学家的诊断工作流程,展示了强大的多中心性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI-Based System for Analysis of Electron Microscope Images in Glomerular Disease.

Importance: Kidney biopsy pathology via transmission electron microscopy (TEM) is essential for diagnosing glomerular diseases, offering critical information on glomerular basement membrane (GBM) thickness, foot process (FP) number, and electron-dense deposits (EDDs). These tasks are laborious and time-consuming.

Objective: To develop and validate an artificial intelligence (AI) diagnostic system, TEM image-based AI-assisted device (TEM-AID), that accurately segments and measures glomerular ultrastructures (including the GBM, FPs, and EDDs) and determines glomerular disease subtypes using TEM images.

Design, setting, and participants: This diagnostic study used a large, multicenter cohort including 160 727 TEM images from 31 670 patients with chronic kidney disease across 6 medical centers from January 2021 to December 2023. TEM-AID was trained and validated on 26 650 patients from 1 center and tested externally on 5020 patients (5 test sets) plus a human-AI test set (454 patients representing 7 glomerular disease subtypes). Data were analyzed from January to December 2024.

Exposures: TEM-AID integrates 4 modules. Segmentation combined YOLO-v8 detection, segment anything model, and human-in-the-loop refinement to segment GBMs, podocyte FPs, and EDDs. Measurement quantified GBM thickness, FP fusion degree, and EDD deposition sites. Classification used least absolute shrinkage and selection operator-selected deep learning and statistical features with a stacking classifier to diagnose 7 glomerular disease subtypes: immunoglobin A nephropathy, membranous nephropathy, lupus nephritis, diabetic nephropathy, minimal change disease, mesangial proliferative glomerulonephritis, and thin basement membrane nephropathy.

Main outcomes and measures: Outcomes of interest were segmentation performance (mean intersection-over-union [IOU], Dice coefficient), subtype classification accuracy, area under the receiver operating characteristic curve (AUC), and human-AI diagnostic concordance.

Results: A total of 31 670 patients (mean [SD] age, 43.2 [16.5] years; 17 372 [54.9%] male) contributed 160 727 TEM images for analysis. Segmentation achieved a mean (SD) IOU of 0.835 (0.062) and Dice of 0.874 (0.023). Subtype classification accuracy was 0.911 (95% CI, 0.904-0.918) in internal validation and 0.895 to 0.914 in external tests. Macro-AUC ranged from 0.972 to 0.989 across cohorts. In human-AI testing (454 patients), TEM-AID accuracy (0.886 (95% CI, 0.859-0.912]; AUC, 0.963 [95% CI, 0.937-0.989]) exceeded clinicians' unaided performance. Clinicians' accuracy improved by a mean (SD) of 11.7% (5.2%) when they used TEM-AID.

Conclusions and relevance: In this multicenter diagnostic study, TEM-AID precisely quantified glomerular ultrastructures and determined glomerular disease subtypes from TEM images, significantly enhancing diagnostic efficiency and accuracy. This system provides quantitative evaluation tools to support clinical pathologists in diagnostic workflows, demonstrating robust multicenter performance.

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来源期刊
JAMA Network Open
JAMA Network Open Medicine-General Medicine
CiteScore
16.00
自引率
2.90%
发文量
2126
审稿时长
16 weeks
期刊介绍: JAMA Network Open, a member of the esteemed JAMA Network, stands as an international, peer-reviewed, open-access general medical journal.The publication is dedicated to disseminating research across various health disciplines and countries, encompassing clinical care, innovation in health care, health policy, and global health. JAMA Network Open caters to clinicians, investigators, and policymakers, providing a platform for valuable insights and advancements in the medical field. As part of the JAMA Network, a consortium of peer-reviewed general medical and specialty publications, JAMA Network Open contributes to the collective knowledge and understanding within the medical community.
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