{"id":17738,"date":"2021-07-23T08:07:00","date_gmt":"2021-07-23T13:07:00","guid":{"rendered":"https:\/\/saluddigital.com\/?p=17738"},"modified":"2025-10-21T12:22:53","modified_gmt":"2025-10-21T18:22:53","slug":"aplicacion-medica-basada-en-inteligencia-artificial-mejora-la-precision-de-pruebas-de-vih","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/aplicacion-medica-basada-en-inteligencia-artificial-mejora-la-precision-de-pruebas-de-vih\/","title":{"rendered":"Medical application based on Artificial Intelligence improves the accuracy of HIV tests"},"content":{"rendered":"
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Scientists have developed a new mobile tool based on Artificial Intelligence (AI), which would improve the interpretation of HIV test results.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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It is an AI-based mobile application that has conducted a pilot test in KwaZulu-Natal, South Africa, whose results have been favorable in identifying 97.8% of HIV positives and 100% of negatives.<\/p>

"The findings demonstrate the potential of deep learning for accurate classification, whether positive or negative, of rapid diagnostic tests," said Rachel McKendry, co-author of the study and professor of biomedicine and nanotechnology at University College London.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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McKendry also explained that the overall performance of 98.9% is better than traditional visual renderings. The co-author explained that the study began in 2017, and the objective was to develop a low-cost, easy-to-use HIV diagnostic tool connected to mobile phones.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The research process required a lot of work and collaboration between those involved. about 60 field workers from the Africa Health Research Institute<\/em>, built a library of more than 11 thousand images of HIV tests. Thanks to these data, the algorithm was trained to classify the tests as positive or negative.<\/p>

\u201cWe believe our real-world image library of 11,000 field-acquired images is the first of its kind at this scale, and our study demonstrates that deep learning models can be deployed with mobile devices in the field, without the need for other accessories,\u201d explained McKendry.<\/p>

However, he also recognized the importance of expanding the image library to include more regions of sub-Saharan Africa, in order to achieve an HIV control program in that region of the continent.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t BIBLIOGRAPHY<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

AI MEAS<\/strong><\/p>

https:\/\/ai-med.io\/more-news\/ai-app-improves-accuracy-of-hiv-testing\/<\/a><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Cient\u00edficos han desarrollado una nueva herramienta m\u00f3vil basada en Inteligencia Artificial (IA), que mejorar\u00eda la interpretaci\u00f3n de resultados de pruebas de VIH. Se trata de una aplicaci\u00f3n m\u00f3vil basada en IA que ha conducido una prueba piloto en KwaZulu-Natal, Sud\u00e1frica, cuyos resultados han sido favorables al identificar al 97.8% de los VIH positivos y 100% de los negativos. \u201cLos hallazgos demuestran el potencial del aprendizaje profundo para una clasificaci\u00f3n precisa, ya sea positiva o negativa, de las pruebas de diagn\u00f3stico r\u00e1pido\u201d, explic\u00f3 Rachel McKendry, coautora del estudio y profesora de biomedicina y nanotecnolog\u00eda en la University College London. McKendry explic\u00f3 tambi\u00e9n que el rendimiento general de 98.9% es mejor que las interpretaciones visuales tradicionales. La coautora detall\u00f3 que el estudio comenz\u00f3 a realizarse en 2017, y el objetivo fue desarrollar una herramienta de diagn\u00f3stico de VIH de bajo costo, f\u00e1cil de usar y conectada a tel\u00e9fonos m\u00f3viles. El proceso de investigaci\u00f3n requiri\u00f3 de mucho trabajo y colaboraci\u00f3n entre los involucrados. alrededor de 60 trabajadores de campo del \u00c1frica Health Research Institute, construyeron una biblioteca de m\u00e1s de 11 mil im\u00e1genes de pruebas de VIH. Gracias a estos datos el algoritmo fue entrenado para clasificar las pruebas como positivas o negativas. \u201cCreemos que nuestra biblioteca de im\u00e1genes del mundo real de 11.000 im\u00e1genes adquiridas en el campo es la primera de su tipo a esta escala y nuestro estudio demuestra que los modelos de aprendizaje profundo se pueden implementar con dispositivos m\u00f3viles en el campo, sin la necesidad de otros accesorios\u201d, explic\u00f3 McKendry. Sin embargo, tambi\u00e9n reconoci\u00f3 la importancia de ampliar la biblioteca de im\u00e1genes e incluir m\u00e1s regiones de \u00c1frica subsahariana, para lograr un programa de control del VIH en esa regi\u00f3n del continente. BIBLIOGRAF\u00cdA AI MED https:\/\/ai-med.io\/more-news\/ai-app-improves-accuracy-of-hiv-testing\/<\/p>","protected":false},"author":1,"featured_media":17740,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156,3393,160],"tags":[145],"class_list":["post-17738","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","category-inteligencia-artificial-y-ciencia","category-noticias","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/17738","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/comments?post=17738"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/17738\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/17740"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=17738"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=17738"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=17738"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}