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Musetti D, Cutolo CA, Bonetto M, Giacomini M, Maggi D, Viviani GL, Gandin I, Traverso CE, Nicolò M. Autonomous artificial intelligence versus teleophthalmology for diabetic retinopathy. Eur J Ophthalmol 2024:11206721241248856. [PMID: 38656241 DOI: 10.1177/11206721241248856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Purpose: To assess the role of artificial intelligence (AI) based automated software for detection of Diabetic Retinopathy (DR) compared with the evaluation of digital retinography by two double masked retina specialists. Methods: Two-hundred one patients (mean age 65 ± 13 years) with type 1 diabetes mellitus or type 2 diabetes mellitus were included. All patients were undergoing a retinography and spectral domain optical coherence tomography (SD-OCT, DRI 3D OCT-2000, Topcon) of the macula. The retinal photographs were graded using two validated AI DR screening software (Eye Art TM and IDx-DR) designed to identify more than mild DR. Results: Retinal images of 201 patients were graded. DR (more than mild DR) was detected by the ophthalmologists in 38 (18.9%) patients and by the AI-algorithms in 36 patients (with 30 eyes diagnosed by both algorithms). Ungradable patients by the AI software were 13 (6.5%) and 16 (8%) for the Eye Art and IDx-DR, respectively. Both AI software strategies showed a high sensitivity and specificity for detecting any more than mild DR without showing any statistically significant difference between them. Conclusions: The comparison between the diagnosis provided by artificial intelligence based automated software and the reference clinical diagnosis showed that they can work at a level of sensitivity that is similar to that achieved by experts.
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Affiliation(s)
- Donatella Musetti
- Clinica Oculistica DiNOGMI, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
| | - Carlo Alberto Cutolo
- Clinica Oculistica DiNOGMI, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
| | | | | | - Davide Maggi
- Clinica Diabetologica, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
| | - Giorgio Luciano Viviani
- Clinica Diabetologica, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
| | - Ilaria Gandin
- Sciences, Biostatistic Unit, University of Trieste, Italy
| | - Carlo Enrico Traverso
- Clinica Oculistica DiNOGMI, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
| | - Massimo Nicolò
- Clinica Oculistica DiNOGMI, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
- Fondazione per la Macula onlus, Genova, Italy
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Pucchio A, Krance SH, Pur DR, Bhatti J, Bassi A, Manichavagan K, Brahmbhatt S, Aggarwal I, Singh P, Virani A, Stanley M, Miranda RN, Felfeli T. Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review. Graefes Arch Clin Exp Ophthalmol 2024; 262:1041-1091. [PMID: 37421481 DOI: 10.1007/s00417-023-06100-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 04/25/2023] [Accepted: 05/06/2023] [Indexed: 07/10/2023] Open
Abstract
PURPOSE This scoping review summarizes the applications of artificial intelligence (AI) and bioinformatics methodologies in analysis of ocular biofluid markers. The secondary objective was to explore supervised and unsupervised AI techniques and their predictive accuracies. We also evaluate the integration of bioinformatics with AI tools. METHODS This scoping review was conducted across five electronic databases including EMBASE, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics were included. RESULTS A total of 10,262 articles were retrieved from all databases and 177 studies met the inclusion criteria. The most commonly studied ocular diseases were diabetic eye diseases, with 50 papers (28%), while glaucoma was explored in 25 studies (14%), age-related macular degeneration in 20 (11%), dry eye disease in 10 (6%), and uveitis in 9 (5%). Supervised learning was used in 91 papers (51%), unsupervised AI in 83 (46%), and bioinformatics in 85 (48%). Ninety-eight papers (55%) used more than one class of AI (e.g. > 1 of supervised, unsupervised, bioinformatics, or statistical techniques), while 79 (45%) used only one. Supervised learning techniques were often used to predict disease status or prognosis, and demonstrated strong accuracy. Unsupervised AI algorithms were used to bolster the accuracy of other algorithms, identify molecularly distinct subgroups, or cluster cases into distinct subgroups that are useful for prediction of the disease course. Finally, bioinformatic tools were used to translate complex biomarker profiles or findings into interpretable data. CONCLUSION AI analysis of biofluid markers displayed diagnostic accuracy, provided insight into mechanisms of molecular etiologies, and had the ability to provide individualized targeted therapeutic treatment for patients. Given the progression of AI towards use in both research and the clinic, ophthalmologists should be broadly aware of the commonly used algorithms and their applications. Future research may be aimed at validating algorithms and integrating them in clinical practice.
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Affiliation(s)
- Aidan Pucchio
- Department of Ophthalmology, Queen's University, Kingston, ON, Canada
- Queens School of Medicine, Kingston, ON, Canada
| | - Saffire H Krance
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Daiana R Pur
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Jasmine Bhatti
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Arshpreet Bassi
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | | | - Shaily Brahmbhatt
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | | | - Priyanka Singh
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Aleena Virani
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | | | - Rafael N Miranda
- The Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Tina Felfeli
- The Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
- Department of Ophthalmology and Vision Sciences, University of Toronto, 340 College Street, Suite 400, Toronto, ON, M5T 3A9, Canada.
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Wang Z, Li Z, Li K, Mu S, Zhou X, Di Y. Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies. Front Endocrinol (Lausanne) 2023; 14:1197783. [PMID: 37383397 PMCID: PMC10296189 DOI: 10.3389/fendo.2023.1197783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/23/2023] [Indexed: 06/30/2023] Open
Abstract
Aims To systematically evaluate the diagnostic value of an artificial intelligence (AI) algorithm model for various types of diabetic retinopathy (DR) in prospective studies over the previous five years, and to explore the factors affecting its diagnostic effectiveness. Materials and methods A search was conducted in Cochrane Library, Embase, Web of Science, PubMed, and IEEE databases to collect prospective studies on AI models for the diagnosis of DR from January 2017 to December 2022. We used QUADAS-2 to evaluate the risk of bias in the included studies. Meta-analysis was performed using MetaDiSc and STATA 14.0 software to calculate the combined sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of various types of DR. Diagnostic odds ratios, summary receiver operating characteristic (SROC) plots, coupled forest plots, and subgroup analysis were performed according to the DR categories, patient source, region of study, and quality of literature, image, and algorithm. Results Finally, 21 studies were included. Meta-analysis showed that the pooled sensitivity, specificity, pooled positive likelihood ratio, pooled negative likelihood ratio, area under the curve, Cochrane Q index, and pooled diagnostic odds ratio of AI model for the diagnosis of DR were 0.880 (0.875-0.884), 0.912 (0.99-0.913), 13.021 (10.738-15.789), 0.083 (0.061-0.112), 0.9798, 0.9388, and 206.80 (124.82-342.63), respectively. The DR categories, patient source, region of study, sample size, quality of literature, image, and algorithm may affect the diagnostic efficiency of AI for DR. Conclusion AI model has a clear diagnostic value for DR, but it is influenced by many factors that deserve further study. Systematic review registration https://www.crd.york.ac.uk/prospero/, identifier CRD42023389687.
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Rabhi S, Blanchard F, Diallo AM, Zeghlache D, Lukas C, Berot A, Delemer B, Barraud S. Temporal deep learning framework for retinopathy prediction in patients with type 1 diabetes. Artif Intell Med 2022; 133:102408. [PMID: 36328668 DOI: 10.1016/j.artmed.2022.102408] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 09/17/2022] [Accepted: 09/21/2022] [Indexed: 12/13/2022]
Abstract
The adoption of electronic health records in hospitals has ensured the availability of large datasets that can be used to predict medical complications. The trajectories of patients in real-world settings are highly variable, making longitudinal data modeling challenging. In recent years, significant progress has been made in the study of deep learning models applied to time series; however, the application of these models to irregular medical time series (IMTS) remains limited. To address this issue, we developed a generic deep-learning-based framework for modeling IMTS that facilitates the comparative studies of sequential neural networks (transformers and long short-term memory) and irregular time representation techniques. A validation study to predict retinopathy complications was conducted on 1207 patients with type 1 diabetes in a French database using their historical glycosylated hemoglobin measurements, without any data aggregation or imputation. The transformer-based model combined with the soft one-hot representation of time gaps achieved the highest score: an area under the receiver operating characteristic curve of 88.65%, specificity of 85.56%, sensitivity of 83.33% and an improvement of 11.7% over the same architecture without time information. This is the first attempt to predict retinopathy complications in patients with type 1 diabetes using deep learning and longitudinal data collected from patient visits. This study highlighted the significance of modeling time gaps between medical records to improve prediction performance and the utility of a generic framework for conducting extensive comparative studies.
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Affiliation(s)
- Sara Rabhi
- Department RS2M, Télécom SudParis, 9 rue Charles Fourier, Evry, 91000, France.
| | - Frédéric Blanchard
- CRESTIC EA 3804, Université Reims Champagne-Ardenne, UFR Sciences Exactes et Naturelles, Moulin de la Housse, 51687, Reims, France
| | - Alpha Mamadou Diallo
- CHU de Reims - Hôpital Robert Debré, Service d'Endocrinologie - Diabète - Nutrition, Avenue du Général Koenig, 51092, Reims, France; Laboratoire de recherche en Santé Publique, Vieillissement, Qualité de vie et Réadaptation des Sujets Fragiles, EA 3797, Université Reims Champagne-Ardenne, 51092, Reims, France
| | - Djamal Zeghlache
- Department RS2M, Télécom SudParis, 9 rue Charles Fourier, Evry, 91000, France
| | - Céline Lukas
- CHU de Reims - Hôpital Robert Debré, Service d'Endocrinologie - Diabète - Nutrition, Avenue du Général Koenig, 51092, Reims, France; Laboratoire de recherche en Santé Publique, Vieillissement, Qualité de vie et Réadaptation des Sujets Fragiles, EA 3797, Université Reims Champagne-Ardenne, 51092, Reims, France
| | - Aurélie Berot
- CHU de Reims - American Memorial Hospital - Service de Pédiatrie, 47 rue Cognac Jay, 51092, Reims, France; Laboratoire d'Education et Pratiques de Santé, EA 3412, Université Sorbonne Paris Nord, 74 rue Marcel Cachin, 93017, Bobigny, France
| | - Brigitte Delemer
- CRESTIC EA 3804, Université Reims Champagne-Ardenne, UFR Sciences Exactes et Naturelles, Moulin de la Housse, 51687, Reims, France; CHU de Reims - Hôpital Robert Debré, Service d'Endocrinologie - Diabète - Nutrition, Avenue du Général Koenig, 51092, Reims, France
| | - Sara Barraud
- CRESTIC EA 3804, Université Reims Champagne-Ardenne, UFR Sciences Exactes et Naturelles, Moulin de la Housse, 51687, Reims, France; CHU de Reims - Hôpital Robert Debré, Service d'Endocrinologie - Diabète - Nutrition, Avenue du Général Koenig, 51092, Reims, France
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