A web-based tool for real-time adequacy assessment of kidney biopsies

Meysam Ahangaran, Emily Sun, Khang Le, Jiawei Sun, William Wang, Tian Herng Tan, Lyle Burdine, Zeljko Dvanajscak, Clarissa Cassol, Shree Sharma, Vijaya B Kolachalama
{"title":"A web-based tool for real-time adequacy assessment of kidney biopsies","authors":"Meysam Ahangaran, Emily Sun, Khang Le, Jiawei Sun, William Wang, Tian Herng Tan, Lyle Burdine, Zeljko Dvanajscak, Clarissa Cassol, Shree Sharma, Vijaya B Kolachalama","doi":"10.1101/2024.02.01.24302147","DOIUrl":null,"url":null,"abstract":"The escalating incidence of kidney biopsies providing insufficient tissue for diagnosis poses a dual challenge, straining the healthcare system and jeopardizing patients who may require re-biopsy or face the prospect of an inaccurate diagnosis due to an unsampled disease. Here, we introduce a web-based tool that can provide real-time, quantitative assessment of kidney biopsy adequacy directly from photographs taken with a smartphone camera. The software tool was developed using a deep learning-driven automated segmentation technique, trained on a dataset comprising nephropathologist-confirmed annotations of the kidney cortex on digital biopsy images. Our framework demonstrated favorable performance in segmenting the cortex via 5-fold cross-validation (Dice coefficient: 0.788+/-0.130) (n=100). Offering a bedside tool for kidney biopsy adequacy assessment has the potential to provide real-time guidance to the physicians performing medical kidney biopsies, reducing the necessity for re-biopsies. Our tool can be accessed through our web-based platform: http://www.biopsyadequacy.org.","PeriodicalId":501513,"journal":{"name":"medRxiv - Nephrology","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv - Nephrology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.02.01.24302147","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The escalating incidence of kidney biopsies providing insufficient tissue for diagnosis poses a dual challenge, straining the healthcare system and jeopardizing patients who may require re-biopsy or face the prospect of an inaccurate diagnosis due to an unsampled disease. Here, we introduce a web-based tool that can provide real-time, quantitative assessment of kidney biopsy adequacy directly from photographs taken with a smartphone camera. The software tool was developed using a deep learning-driven automated segmentation technique, trained on a dataset comprising nephropathologist-confirmed annotations of the kidney cortex on digital biopsy images. Our framework demonstrated favorable performance in segmenting the cortex via 5-fold cross-validation (Dice coefficient: 0.788+/-0.130) (n=100). Offering a bedside tool for kidney biopsy adequacy assessment has the potential to provide real-time guidance to the physicians performing medical kidney biopsies, reducing the necessity for re-biopsies. Our tool can be accessed through our web-based platform: http://www.biopsyadequacy.org.
用于实时评估肾活检是否充分的网络工具
肾活检提供的组织不足以进行诊断的发生率不断攀升,这带来了双重挑战,既给医疗系统带来压力,又危及患者,他们可能需要重新进行活检,或面临因未取样疾病而导致诊断不准确的前景。在此,我们介绍一种基于网络的工具,它可以直接通过智能手机摄像头拍摄的照片对肾脏活检的充分性进行实时、定量评估。该软件工具是利用深度学习驱动的自动分割技术开发的,并在一个数据集上进行了训练,该数据集包括数字活检图像上经肾病病理学家确认的肾脏皮质注释。通过 5 倍交叉验证(骰子系数:0.788+/-0.130)(n=100),我们的框架在分割肾皮质方面表现出色。提供肾活检充分性评估床旁工具有可能为进行医学肾活检的医生提供实时指导,减少再次活检的必要性。您可以通过我们的网络平台 http://www.biopsyadequacy.org 访问我们的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信