{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T21:19:52Z","timestamp":1793481592460,"version":"4.5.0"},"reference-count":41,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Background: If left untreated, glaucoma\u2014the second most common cause of blindness worldwide\u2014causes irreversible visual loss due to a gradual neurodegeneration of the retinal ganglion cells. Conventional techniques for identifying glaucoma, like optical coherence tomography (OCT) and visual field exams, are frequently laborious and dependent on subjective interpretation. Through the fast and accurate analysis of massive amounts of imaging data, artificial intelligence (AI), in particular machine learning (ML) and deep learning (DL), has emerged as a promising method to improve the early detection and management of glaucoma. Aims: The purpose of this study is to examine the current uses of AI in the early diagnosis, treatment, and detection of glaucoma while highlighting the advantages and drawbacks of different AI models and algorithms. In addition, it aims to determine how AI technologies might transform glaucoma treatment and suggest future lines of inquiry for this area of study. Methods: A thorough search of databases, including Web of Science, PubMed, and Scopus, was carried out to find pertinent papers released until August 2024. The inclusion criteria were limited to research published in English in peer-reviewed publications that used AI, ML, or DL to diagnose or treat glaucoma in human subjects. Articles were chosen and vetted according to their quality, contribution to the field, and relevancy. Results: Convolutional neural networks (CNNs) and other deep learning algorithms are among the AI models included in this paper that have been shown to have excellent sensitivity and specificity in identifying glaucomatous alterations in fundus photos, OCT scans, and visual field tests. By automating standard screening procedures, these models have demonstrated promise in distinguishing between glaucomatous and healthy eyes, forecasting the course of the disease, and possibly lessening the workload of physicians. Nonetheless, several significant obstacles remain, such as the requirement for various training datasets, outside validation, decision-making transparency, and handling moral and legal issues. Conclusions: Artificial intelligence (AI) holds great promise for improving the diagnosis and treatment of glaucoma by facilitating prompt and precise interpretation of imaging data and assisting in clinical decision making. To guarantee wider accessibility and better patient results, future research should create strong generalizable AI models validated in various populations, address ethical and legal matters, and incorporate AI into clinical practice.<\/jats:p>","DOI":"10.3390\/life14111386","type":"journal-article","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T07:04:04Z","timestamp":1730099044000},"page":"1386","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0999-5545","authenticated-orcid":false,"given":"Marco","family":"Zeppieri","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, University Hospital of Udine, 33100 Udine, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorenzo","family":"Gardini","sequence":"additional","affiliation":[{"name":"Unit of Ophthalmology, Department of Surgical Sciences, Ospedale Maggiore, 40100 Bologna, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3221-3791","authenticated-orcid":false,"given":"Carola","family":"Culiersi","sequence":"additional","affiliation":[{"name":"Unit of Ophthalmology, Department of Surgical Sciences, Ospedale Maggiore, 40100 Bologna, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luigi","family":"Fontana","sequence":"additional","affiliation":[{"name":"Ophthalmology Unit, Department of Surgical Sciences, IRCCS Azienda Ospedaliero, Alma Mater Studiorum University of Bologna, 40100 Bologna, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7486-8361","authenticated-orcid":false,"given":"Mutali","family":"Musa","sequence":"additional","affiliation":[{"name":"Department of Optometry, University of Benin, Benin City 300238, Nigeria"},{"name":"Africa Eye Laser Centre, Km 7, Benin City 300105, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7938-876X","authenticated-orcid":false,"given":"Fabiana","family":"D\u2019Esposito","sequence":"additional","affiliation":[{"name":"Imperial College Ophthalmic Research Group (ICORG) Unit, Imperial College, 153-173 Marylebone Rd, London NW15QH, UK"},{"name":"Department of Neurosciences, Reproductive Sciences and Dentistry, University of Naples Federico II, Via Pansini 5, 80131 Napoli, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7721-4694","authenticated-orcid":false,"given":"Pier Luigi","family":"Surico","sequence":"additional","affiliation":[{"name":"Schepens Eye Research Institute of Mass Eye and Ear, Harvard Medical School, Boston, MA 02114, USA"},{"name":"Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8424-0068","authenticated-orcid":false,"given":"Caterina","family":"Gagliano","sequence":"additional","affiliation":[{"name":"Department of Medicine and Surgery, University of Enna \u201cKore\u201d, Piazza dell\u2019Universit\u00e0, 94100 Enna, Italy"},{"name":"Mediterranean Foundation \u201cG.B. Morgagni\u201d, 95125 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7691-8980","authenticated-orcid":false,"given":"Francesco Saverio","family":"Sorrentino","sequence":"additional","affiliation":[{"name":"Unit of Ophthalmology, Department of Surgical Sciences, Ospedale Maggiore, 40100 Bologna, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.ajo.2019.07.005","article-title":"Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data from Electronic Health Records","volume":"208","author":"Baxter","year":"2019","journal-title":"Am. J. 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