{"id":5954,"date":"2021-01-21T17:59:28","date_gmt":"2021-01-21T17:59:28","guid":{"rendered":"http:\/\/blogs.lshtm.ac.uk\/iceh-new\/?page_id=5954"},"modified":"2023-04-21T15:11:21","modified_gmt":"2023-04-21T15:11:21","slug":"artificial-intelligence-supported-diabetic-retinopathy-screening-in-tanzania-a-randomised-controlled-trial","status":"publish","type":"page","link":"https:\/\/blogs.lshtm.ac.uk\/iceh-new\/artificial-intelligence-supported-diabetic-retinopathy-screening-in-tanzania-a-randomised-controlled-trial\/","title":{"rendered":"Artificial Intelligence-Supported Diabetic Retinopathy Screening in Tanzania: A Randomised Controlled Trial"},"content":{"rendered":"

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Artificial Intelligence-Supported Diabetic Retinopathy Screening in Tanzania: A Randomised Controlled Trial<\/strong><\/h1>\n

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The early diagnosis and treatment of diabetic retinopathy (DR) is known to preserve vision; this is done successfully in the UK through the national DR screening programme. However, there are few DR screening programmes in Africa, which have little quality control and high rates of loss to follow-up.<\/p>\n

This project will randomise persons eligible for DR screening to either the standard service model or an artificial intelligence (AI) supported screening model which provides an immediate diagnosis and therefore allows point of screening patient counselling.<\/p>\n

We will assess whether AI-supported DR screening can lead to improved patient outcomes within two real-world screening programmes in Tanzania.<\/p>\n

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Project Aims<\/strong><\/h4>\n

The aim of this research project is to assess whether AI-supported diabetic retinopathy screening increases the proportion of persons with true referable diabetic retinopathy who attend the central ophthalmology clinic through a \u201cdouble-boost\u201d effect by<\/p>\n