Article ; Online: Deep learning of HIV field-based rapid tests.
2021 Volume 27, Issue 7, Page(s) 1165–1170
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 ... ...
| 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, 60 fieldworkers routinely collected images of HIV lateral flow tests. From a library of 11,374 images, 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 diagnostics |
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| MeSH term(s) | AIDS Serodiagnosis/methods ; Algorithms ; Deep Learning ; HIV Infections/diagnosis ; Humans ; Rural Health Services/organization & administration ; Sensitivity and Specificity ; South Africa ; Time and Motion Studies |
| Language | English |
| Publishing date | 2021-06-17 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 1220066-9 |
| ISSN | 1546-170X ; 1078-8956 |
| ISSN (online) | 1546-170X |
| ISSN | 1078-8956 |
| DOI | 10.1038/s41591-021-01384-9 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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