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Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: a scoping review.
Cleland, Charles R; Rwiza, Justus; Evans, Jennifer R; Gordon, Iris; MacLeod, David; Burton, Matthew J; Bascaran, Covadonga.
Afiliação
  • Cleland CR; International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK charles.cleland@lshtm.ac.uk.
  • Rwiza J; Eye Department, Kilimanjaro Christian Medical Centre, Moshi, United Republic of Tanzania.
  • Evans JR; Eye Department, Kilimanjaro Christian Medical Centre, Moshi, United Republic of Tanzania.
  • Gordon I; International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK.
  • MacLeod D; International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK.
  • Burton MJ; Tropical Epidemiology Group, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
  • Bascaran C; International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK.
Article em En | MEDLINE | ID: mdl-37532460
ABSTRACT
Diabetic retinopathy (DR) is a leading cause of blindness globally. There is growing evidence to support the use of artificial intelligence (AI) in diabetic eye care, particularly for screening populations at risk of sight loss from DR in low-income and middle-income countries (LMICs) where resources are most stretched. However, implementation into clinical practice remains limited. We conducted a scoping review to identify what AI tools have been used for DR in LMICs and to report their performance and relevant characteristics. 81 articles were included. The reported sensitivities and specificities were generally high providing evidence to support use in clinical practice. However, the majority of studies focused on sensitivity and specificity only and there was limited information on cost, regulatory approvals and whether the use of AI improved health outcomes. Further research that goes beyond reporting sensitivities and specificities is needed prior to wider implementation.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Diabetes Mellitus / Retinopatia Diabética Tipo de estudo: Diagnostic_studies / Prognostic_studies / Screening_studies / Systematic_reviews Limite: Humans Idioma: En Revista: BMJ Open Diabetes Res Care Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Diabetes Mellitus / Retinopatia Diabética Tipo de estudo: Diagnostic_studies / Prognostic_studies / Screening_studies / Systematic_reviews Limite: Humans Idioma: En Revista: BMJ Open Diabetes Res Care Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Reino Unido