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EMB对心脏同种异体移植排斥反应的深度学习评估
作者:小柯机器人 发布时间:2022/3/27 14:13:17

美国哈佛医学院布莱根妇女医院Faisal Mahmood团队取得最新进展。他们提出心内膜心肌活检 (EMB)对心脏同种异体移植排斥反应的深度学习评估。2022年3月21日出版的《自然—医学》杂志发表了该论文。

他们提出了一个基于深度学习的人工智能 (AI) 系统,用于自动评估从 EMB 获得的千兆像素整张幻灯片图像,该系统同时解决了同种异体移植排斥的检测、子类型和分级。为了评估模型性能,他们策划了一个来自美国的大型数据集,以及来自土耳其和瑞士的独立测试队列,其中包括跨人群、样品制备和玻片扫描仪器的大规模变异。该模型检测到同种异体移植排斥反应的接受者操作特征曲线 (AUC) 下面积为 0.962;评估细胞和抗体介导的排斥类型,AUC 分别为 0.958 和 0.874;检测 Quilty B 病变,良性排斥反应,AUC 为 0.939;并以 0.833 的 AUC 区分低级和高级拒绝。在一项人类读者研究中,人工智能系统表现出不逊于传统评估的性能,并减少了观察者间的变异性和评估时间。这种对心脏同种异体移植排斥反应的有力评估为临床试验铺平了道路,以确定 AI 辅助 EMB 评估的功效及其改善心脏移植结果的潜力。

据悉,EMB筛查代表了心脏移植后检测同种异体移植排斥的护理标准。EMB的人工解释受到观察者间和观察者内显著差异的影响,这通常导致免疫抑制药物治疗不当、不必要的后续活检和移植结果不佳。

附:英文原文

Title: Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies

Author: Lipkova, Jana, Chen, Tiffany Y., Lu, Ming Y., Chen, Richard J., Shady, Maha, Williams, Mane, Wang, Jingwen, Noor, Zahra, Mitchell, Richard N., Turan, Mehmet, Coskun, Gulfize, Yilmaz, Funda, Demir, Derya, Nart, Deniz, Basak, Kayhan, Turhan, Nesrin, Ozkara, Selvinaz, Banz, Yara, Odening, Katja E., Mahmood, Faisal

Issue&Volume: 2022-03-21

Abstract: Endomyocardial biopsy (EMB) screening represents the standard of care for detecting allograft rejections after heart transplant. Manual interpretation of EMBs is affected by substantial interobserver and intraobserver variability, which often leads to inappropriate treatment with immunosuppressive drugs, unnecessary follow-up biopsies and poor transplant outcomes. Here we present a deeplearning-based artificial intelligence (AI) system for automated assessment of gigapixel whole-slide images obtained from EMBs, which simultaneously addresses detection, subtyping and grading of allograft rejection. To assess model performance, we curated a large dataset from the United States, as well as independent test cohorts from Turkey and Switzerland, which includes large-scale variability across populations, sample preparations and slide scanning instrumentation. The model detects allograft rejection with an area under the receiver operating characteristic curve (AUC) of 0.962; assesses the cellular and antibody-mediated rejection type with AUCs of 0.958 and 0.874, respectively; detects QuiltyB lesions, benign mimics of rejection, with an AUC of 0.939; and differentiates between low-grade and high-grade rejections with an AUC of 0.833. In a human reader study, the AI system showed non-inferior performance to conventional assessment and reduced interobserver variability and assessment time. This robust evaluation of cardiac allograft rejection paves the way for clinical trials to establish the efficacy of AI-assisted EMB assessment and its potential for improving heart transplant outcomes.

DOI: 10.1038/s41591-022-01709-2

Source: https://www.nature.com/articles/s41591-022-01709-2

期刊信息

Nature Medicine:《自然—医学》,创刊于1995年。隶属于施普林格·自然出版集团,最新IF:30.641
官方网址:https://www.nature.com/nm/
投稿链接:https://mts-nmed.nature.com/cgi-bin/main.plex