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Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence.
Sorrentino, Francesco Saverio; Gardini, Lorenzo; Fontana, Luigi; Musa, Mutali; Gabai, Andrea; Maniaci, Antonino; Lavalle, Salvatore; D'Esposito, Fabiana; Russo, Andrea; Longo, Antonio; Surico, Pier Luigi; Gagliano, Caterina; Zeppieri, Marco.
Affiliation
  • 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.
  • Russo A; Department of Neurosciences, Reproductive Sciences and Dentistry, University of Naples Federico II, Via Pansini 5, 80131 Napoli, Italy.
  • Longo A; Department of Ophthalmology, University of Catania, 95123 Catania, Italy.
  • Surico PL; Department of Ophthalmology, University of Catania, 95123 Catania, Italy.
  • Gagliano C; Schepens Eye Research Institute of Mass Eye and Ear, Harvard Medical School, Boston, MA 02114, USA.
  • Zeppieri M; Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy.
J Pers Med ; 14(7)2024 Jun 26.
Article in En | MEDLINE | ID: mdl-39063944
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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: J Pers Med Year: 2024 Document type: Article Affiliation country: Italia Country of publication: Suiza

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: J Pers Med Year: 2024 Document type: Article Affiliation country: Italia Country of publication: Suiza