Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence.
| Title: | Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence. |
|---|---|
| Authors: | Sorrentino FS; Unit of Ophthalmology, Department of Surgical Sciences, Ospedale Maggiore, 40100 Bologna, Italy.; Gardini L; Unit of Ophthalmology, Department of Surgical Sciences, Ospedale Maggiore, 40100 Bologna, Italy.; Fontana L; Ophthalmology Unit, Department of Surgical Sciences, Alma Mater Studiorum University of Bologna, IRCCS Azienda Ospedaliero-Universitaria Bologna, 40100 Bologna, Italy.; Musa M; Department of Optometry, University of Benin, Benin City 300238, Edo State, Nigeria.; Gabai A; Department of Ophthalmology, Humanitas-San Pio X, 20159 Milan, Italy.; Maniaci A; Department of Medicine and Surgery, University of Enna 'Kore', Piazza dell'Università, 94100 Enna, Italy.; Lavalle S; Department of Medicine and Surgery, University of Enna 'Kore', Piazza dell'Università, 94100 Enna, Italy.; D'Esposito F; Imperial College Ophthalmic Research Group (ICORG) Unit, Imperial College, 153-173 Marylebone Rd, London NW15QH, UK.; Department of Neurosciences, Reproductive Sciences and Dentistry, University of Naples Federico II, Via Pansini 5, 80131 Napoli, Italy.; Russo A; Department of Ophthalmology, University of Catania, 95123 Catania, Italy.; Longo A; Department of Ophthalmology, University of Catania, 95123 Catania, Italy.; Surico PL; Schepens Eye Research Institute of Mass Eye and Ear, Harvard Medical School, Boston, MA 02114, USA.; Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy.; Gagliano C; Department of Medicine and Surgery, University of Enna 'Kore', Piazza dell'Università, 94100 Enna, Italy.; Eye Clinic, Catania University, San Marco Hospital, Viale Carlo Azeglio Ciampi, 95121 Catania, Italy.; Zeppieri M; Department of Ophthalmology, University Hospital of Udine, 33100 Udine, Italy. |
| Source: | Journal of personalized medicine [J Pers Med] 2024 Jun 26; Vol. 14 (7). Date of Electronic Publication: 2024 Jun 26. |
| Publication Type: | Journal Article; Review |
| Language: | English |
| Journal Info: | Publisher: MDPI AG Country of Publication: Switzerland NLM ID: 101602269 Publication Model: Electronic Cited Medium: Print ISSN: 2075-4426 (Print) Linking ISSN: 20754426 NLM ISO Abbreviation: J Pers Med Subsets: PubMed not MEDLINE |
| Imprint Name(s): | Original Publication: Basel, Switzerland : MDPI AG, 2011- |
| Abstract: | Background: An increasing amount of people are globally affected by retinal diseases, such as diabetes, vascular occlusions, maculopathy, alterations of systemic circulation, and metabolic syndrome.; Aim: This review will discuss novel technologies in and potential approaches to the detection and diagnosis of retinal diseases with the support of cutting-edge machines and artificial intelligence (AI).; Methods: The demand for retinal diagnostic imaging exams has increased, but the number of eye physicians or technicians is too little to meet the request. Thus, algorithms based on AI have been used, representing valid support for early detection and helping doctors to give diagnoses and make differential diagnosis. AI helps patients living far from hub centers to have tests and quick initial diagnosis, allowing them not to waste time in movements and waiting time for medical reply.; Results: Highly automated systems for screening, early diagnosis, grading and tailored therapy will facilitate the care of people, even in remote lands or countries.; Conclusion: A potential massive and extensive use of AI might optimize the automated detection of tiny retinal alterations, allowing eye doctors to perform their best clinical assistance and to set the best options for the treatment of retinal diseases. |
| Competing Interests: | The authors declare no conflicts of interest. |
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| Contributed Indexing: | Keywords: artificial intelligence; deep learning; machine learning; macular edema; maculopathy; retinal imaging; retinopathy |
| Entry Date(s): | Date Created: 20240727 Latest Revision: 20240729 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC11278069 |
| DOI: | 10.3390/jpm14070690 |
| PMID: | 39063944 |
| Database: | MEDLINE |
Journal Article; Review