Advancing Knowledge
Publications from our team of researchers and collaborators
Our Publications
Unmasking biases and navigating pitfalls in the ophthalmic artificial intelligence lifecycle: A narrative review
Authors: Nakayama LF, Matos J, Quion J, Novaes F, Mitchell WG, Mwavu R, Hung CJJ, Santiago APD, Phanphruk W, Cardoso JS, Celi LA
Journal: PLOS Digit Health (3(10), 2024)
Pages: e0000618
DOI: 10.1371/journal.pdig.0000618
Over the past 2 decades, exponential growth in data availability, computational power, and newly available modeling techniques has led to an expansion in interest, investment, and research in Artificial Intelligence (AI) applications. Ophthalmology is one of many fields that seek to benefit from AI given the advent of telemedicine screening programs and the use of ancillary imaging. However, before AI can be widely deployed, further work must be done to avoid the pitfalls within the AI lifecycle.
Assessment of Clinical Metadata on the Accuracy of Retinal Fundus Image Labels in Diabetic Retinopathy in Uganda: Case-Crossover Study Using the Multimodal Database of Retinal Images in Africa
Authors: Arunga S, Morley KE, Kwaga T, Morley MG, Nakayama LF, Mwavu R, Kaggwa F, Ssempiira J, Celi LA, Haberer JE, Obua C
Journal: JMIR Form Res (8, 2024)
Pages: e59914
DOI: 10.2196/59914
Labeling color fundus photos (CFP) is an important step in the development of artificial intelligence screening algorithms for the detection of diabetic retinopathy (DR). Most studies use the International Classification of Diabetic Retinopathy (ICDR) to assign labels to CFP, plus the presence or absence of macular edema (ME).
Ophthalmology Optical Coherence Tomography Databases for Artificial Intelligence Algorithm: A Review
Authors: Restrepo D, Quion JM, Do Carmo Novaes F, Azevedo Costa ID, Vasquez C, Bautista AN, Quiminiano E, Lim PA, Mwavu R, Celi LA, Nakayama LF
Journal: Semin Ophthalmol (39(3), 2024)
Pages: 200-210
DOI: 10.1080/08820538.2024.2312464
This review examines optical coherence tomography (OCT) databases used for AI algorithm development in ophthalmology, highlighting the importance of diverse datasets for accurate AI models.
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Publication Timeline
Two publications in PLOS Digital Health
Publication in Seminars in Ophthalmology
Publication in Seminars in Ophthalmology
Publication in Lancet Digital Health
Publication in JMIR Formative Research
Publication in PLOS Digital Health