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Artificial Intelligence for Personalised Ophthalmology Residency Training
Residency training in medicine lays the foundation for future medical doctors. In real-world settings, training centers face challenges in trying to create balanced residency programs, with cases encountered by residents not always being fairly distributed among them. In recent years, there has been...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10002549/ https://www.ncbi.nlm.nih.gov/pubmed/36902612 http://dx.doi.org/10.3390/jcm12051825 |
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author | Muntean, George Adrian Groza, Adrian Marginean, Anca Slavescu, Radu Razvan Steiu, Mihnea Gabriel Muntean, Valentin Nicoara, Simona Delia |
author_facet | Muntean, George Adrian Groza, Adrian Marginean, Anca Slavescu, Radu Razvan Steiu, Mihnea Gabriel Muntean, Valentin Nicoara, Simona Delia |
author_sort | Muntean, George Adrian |
collection | PubMed |
description | Residency training in medicine lays the foundation for future medical doctors. In real-world settings, training centers face challenges in trying to create balanced residency programs, with cases encountered by residents not always being fairly distributed among them. In recent years, there has been a tremendous advancement in developing artificial intelligence (AI)-based algorithms with human expert guidance for medical imaging segmentation, classification, and prediction. In this paper, we turned our attention from training machines to letting them train us and developed an AI framework for personalised case-based ophthalmology residency training. The framework is built on two components: (1) a deep learning (DL) model and (2) an expert-system-powered case allocation algorithm. The DL model is trained on publicly available datasets by means of contrastive learning and can classify retinal diseases from color fundus photographs (CFPs). Patients visiting the retina clinic will have a CFP performed and afterward, the image will be interpreted by the DL model, which will give a presumptive diagnosis. This diagnosis is then passed to a case allocation algorithm which selects the resident who would most benefit from the specific case, based on their case history and performance. At the end of each case, the attending expert physician assesses the resident’s performance based on standardised examination files, and the results are immediately updated in their portfolio. Our approach provides a structure for future precision medical education in ophthalmology. |
format | Online Article Text |
id | pubmed-10002549 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100025492023-03-11 Artificial Intelligence for Personalised Ophthalmology Residency Training Muntean, George Adrian Groza, Adrian Marginean, Anca Slavescu, Radu Razvan Steiu, Mihnea Gabriel Muntean, Valentin Nicoara, Simona Delia J Clin Med Article Residency training in medicine lays the foundation for future medical doctors. In real-world settings, training centers face challenges in trying to create balanced residency programs, with cases encountered by residents not always being fairly distributed among them. In recent years, there has been a tremendous advancement in developing artificial intelligence (AI)-based algorithms with human expert guidance for medical imaging segmentation, classification, and prediction. In this paper, we turned our attention from training machines to letting them train us and developed an AI framework for personalised case-based ophthalmology residency training. The framework is built on two components: (1) a deep learning (DL) model and (2) an expert-system-powered case allocation algorithm. The DL model is trained on publicly available datasets by means of contrastive learning and can classify retinal diseases from color fundus photographs (CFPs). Patients visiting the retina clinic will have a CFP performed and afterward, the image will be interpreted by the DL model, which will give a presumptive diagnosis. This diagnosis is then passed to a case allocation algorithm which selects the resident who would most benefit from the specific case, based on their case history and performance. At the end of each case, the attending expert physician assesses the resident’s performance based on standardised examination files, and the results are immediately updated in their portfolio. Our approach provides a structure for future precision medical education in ophthalmology. MDPI 2023-02-24 /pmc/articles/PMC10002549/ /pubmed/36902612 http://dx.doi.org/10.3390/jcm12051825 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Muntean, George Adrian Groza, Adrian Marginean, Anca Slavescu, Radu Razvan Steiu, Mihnea Gabriel Muntean, Valentin Nicoara, Simona Delia Artificial Intelligence for Personalised Ophthalmology Residency Training |
title | Artificial Intelligence for Personalised Ophthalmology Residency Training |
title_full | Artificial Intelligence for Personalised Ophthalmology Residency Training |
title_fullStr | Artificial Intelligence for Personalised Ophthalmology Residency Training |
title_full_unstemmed | Artificial Intelligence for Personalised Ophthalmology Residency Training |
title_short | Artificial Intelligence for Personalised Ophthalmology Residency Training |
title_sort | artificial intelligence for personalised ophthalmology residency training |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10002549/ https://www.ncbi.nlm.nih.gov/pubmed/36902612 http://dx.doi.org/10.3390/jcm12051825 |
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