This paper is in the following e-collection/theme issue:

Development and Evaluation of Research Methods, Instruments and Tools (452) Diabetes Health Services and System Innovations (108) Imaging Informatics (192) Diabetic Retinopathy (9) Computer-Aided Diagnosis (109) Machine Learning (1440)

Published on in Vol 5, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25290, first published .
Screening Diabetic Retinopathy Using an Automated Retinal Image Analysis System in Independent and Assistive Use Cases in Mexico: Randomized Controlled Trial

Screening Diabetic Retinopathy Using an Automated Retinal Image Analysis System in Independent and Assistive Use Cases in Mexico: Randomized Controlled Trial

Screening Diabetic Retinopathy Using an Automated Retinal Image Analysis System in Independent and Assistive Use Cases in Mexico: Randomized Controlled Trial

Authors of this article:

Alejandro Noriega1, 2 Author Orcid Image ;   Daniela Meizner3 Author Orcid Image ;   Dalia Camacho2, 4 Author Orcid Image ;   Jennifer Enciso2, 5 Author Orcid Image ;   Hugo Quiroz-Mercado3 Author Orcid Image ;   Virgilio Morales-Canton3 Author Orcid Image ;   Abdullah Almaatouq6 Author Orcid Image ;   Alex Pentland1 Author Orcid Image
Article Authors Cited by (9) Tweetations (3) Metrics

Journals

  1. Uppamma P, Bhattacharya S, Geman O. Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends. Journal of Healthcare Engineering 2023;2023(1) View
  2. Starovoitov V, Golub Y, Lukashevich M. A Universal Retinal Image Template for Automated Screening of Diabetic Retinopathy. Pattern Recognition and Image Analysis 2022;32(2):322 View
  3. Dow E, Chen K, Zhao C, Knapp A, Phadke A, Weng K, Do D, Mahajan V, Mruthyunjaya P, Leng T, Myung D. Artificial Intelligence Improves Patient Follow-Up in a Diabetic Retinopathy Screening Program. Clinical Ophthalmology 2023;Volume 17:3323 View
  4. Martindale A, Llewellyn C, de Visser R, Ng B, Ngai V, Kale A, di Ruffano L, Golub R, Collins G, Moher D, McCradden M, Oakden-Rayner L, Rivera S, Calvert M, Kelly C, Lee C, Yau C, Chan A, Keane P, Beam A, Denniston A, Liu X. Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines. Nature Communications 2024;15(1) View
  5. Pattathil N, Zhao J, Sam-Oyerinde O, Felfeli T. Adherence of randomised controlled trials using artificial intelligence in ophthalmology to CONSORT-AI guidelines: a systematic review and critical appraisal. BMJ Health & Care Informatics 2023;30(1):e100757 View
  6. Cleland C, Rwiza J, Evans J, Gordon I, MacLeod D, Burton M, Bascaran C. Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: a scoping review. BMJ Open Diabetes Research & Care 2023;11(4):e003424 View
  7. Nguyen T, Ong J, Masalkhi M, Waisberg E, Zaman N, Sarker P, Aman S, Lin H, Luo M, Ambrosio R, Machado A, Ting D, Mehta J, Tavakkoli A, Lee A. Artificial intelligence in corneal diseases: A narrative review. Contact Lens and Anterior Eye 2024:102284 View

Books/Policy Documents

  1. Juneja S, Bali A, Bali N. AI Applications for Disease Diagnosis and Treatment. View
  2. Chandankhede M, Zade A. Computational Intelligence in Machine Learning. View