小柯机器人

科学家开发出HIV快速检测的深度学习方法
2021-06-20 21:15

英国伦敦大学学院Rachel A. McKendry等研究人员开发出HIV快速检测的深度学习方法。该项研究成果于2021年6月17日在线发表在《自然—医学》杂志上。

研究人员使用深度学习来分类了在南非农村获取的快速人体免疫缺陷病毒(HIV)测试图像。使用具有三星SM-P585平板电脑的新开发图像捕获协议,60个现场工作者常规获取了HIV横向流量测试。从11,374个图像的文库中,深度学习算法得以训练,从而可将测试分类为阳性或阴性。作为移动应用部署算法的试验现场研究表明,与人类经验丰富的护士和新培训的社区卫生工作人员的传统视觉解释相比,其敏感度更高(97.8%)和特异性更强(100%) ,并减少了假阳性和假阴性。

这些研究结果为低收入和中等收入国家进行了深度学习的诊断新范式,称为REASSURED诊断。这些诊断有可能为劳动力培训、质量保证、决策支持和移动连接提供促进疾病控制策略,从而加强医疗保健系统效率,并在新兴感染方面改善患者结果和爆发管理。

据悉,虽然深度学习算法增强了疾病的诊断,但它们在该领域中进行的快速诊断测试的使用尚未得到广泛测试。

附:英文原文

Title: Deep learning of HIV field-based rapid tests

Author: Valrian Turb, Carina Herbst, Thobeka Mngomezulu, Sepehr Meshkinfamfard, Nondumiso Dlamini, Thembani Mhlongo, Theresa Smit, Valeriia Cherepanova, Koki Shimada, Jobie Budd, Nestor Arsenov, Steven Gray, Deenan Pillay, Kobus Herbst, Maryam Shahmanesh, Rachel A. McKendry

Issue&Volume: 2021-06-17

Abstract: Although deep learning algorithms show increasing promise for disease diagnosis, their use with rapid diagnostic tests performed in the field has not been extensively tested. Here we use deep learning to classify images of rapid human immunodeficiency virus (HIV) tests acquired in rural South Africa. Using newly developed image capture protocols with the Samsung SM-P585 tablet, 60fieldworkers routinely collected images of HIV lateral flow tests. From a library of 11,374images, deep learning algorithms were trained to classify tests as positive or negative. A pilot field study of the algorithms deployed as a mobile application demonstrated high levels of sensitivity (97.8%) and specificity (100%) compared with traditional visual interpretation by humans—experienced nurses and newly trained community health worker staff—and reduced the number of false positives and false negatives. Our findings lay the foundations for a new paradigm of deep learning–enabled diagnostics in low- and middle-income countries, termed REASSURED diagnostics1, an acronym for real-time connectivity, ease of specimen collection, affordable, sensitive, specific, user-friendly, rapid, equipment-free and deliverable. Such diagnostics have the potential to provide a platform for workforce training, quality assurance, decision support and mobile connectivity to inform disease control strategies, strengthen healthcare system efficiency and improve patient outcomes and outbreak management in emerging infections.

DOI: 10.1038/s41591-021-01384-9

Source: https://www.nature.com/articles/s41591-021-01384-9

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


本期文章:《自然—医学》:Online/在线发表

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