Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence

doi:10.3390/jpm14070690

Review

. 2024 Jun 26;14(7):690.

doi: 10.3390/jpm14070690.

Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence

Affiliations

¹ Unit of Ophthalmology, Department of Surgical Sciences, Ospedale Maggiore, 40100 Bologna, Italy.
² Ophthalmology Unit, Department of Surgical Sciences, Alma Mater Studiorum University of Bologna, IRCCS Azienda Ospedaliero-Universitaria Bologna, 40100 Bologna, Italy.
³ Department of Optometry, University of Benin, Benin City 300238, Edo State, Nigeria.
⁴ Department of Ophthalmology, Humanitas-San Pio X, 20159 Milan, Italy.
⁵ Department of Medicine and Surgery, University of Enna "Kore", Piazza dell'Università, 94100 Enna, Italy.
⁶ Imperial College Ophthalmic Research Group (ICORG) Unit, Imperial College, 153-173 Marylebone Rd, London NW15QH, UK.
⁷ Department of Neurosciences, Reproductive Sciences and Dentistry, University of Naples Federico II, Via Pansini 5, 80131 Napoli, Italy.
⁸ Department of Ophthalmology, University of Catania, 95123 Catania, Italy.
⁹ Schepens Eye Research Institute of Mass Eye and Ear, Harvard Medical School, Boston, MA 02114, USA.
¹⁰ Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy.
¹¹ Eye Clinic, Catania University, San Marco Hospital, Viale Carlo Azeglio Ciampi, 95121 Catania, Italy.
¹² Department of Ophthalmology, University Hospital of Udine, 33100 Udine, Italy.

PMID: 39063944
PMCID: PMC11278069
DOI: 10.3390/jpm14070690

Review

Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence

Francesco Saverio Sorrentino et al. J Pers Med. 2024.

. 2024 Jun 26;14(7):690.

doi: 10.3390/jpm14070690.

Authors

Affiliations

¹ Unit of Ophthalmology, Department of Surgical Sciences, Ospedale Maggiore, 40100 Bologna, Italy.
² Ophthalmology Unit, Department of Surgical Sciences, Alma Mater Studiorum University of Bologna, IRCCS Azienda Ospedaliero-Universitaria Bologna, 40100 Bologna, Italy.
³ Department of Optometry, University of Benin, Benin City 300238, Edo State, Nigeria.
⁴ Department of Ophthalmology, Humanitas-San Pio X, 20159 Milan, Italy.
⁵ Department of Medicine and Surgery, University of Enna "Kore", Piazza dell'Università, 94100 Enna, Italy.
⁶ Imperial College Ophthalmic Research Group (ICORG) Unit, Imperial College, 153-173 Marylebone Rd, London NW15QH, UK.
⁷ Department of Neurosciences, Reproductive Sciences and Dentistry, University of Naples Federico II, Via Pansini 5, 80131 Napoli, Italy.
⁸ Department of Ophthalmology, University of Catania, 95123 Catania, Italy.
⁹ Schepens Eye Research Institute of Mass Eye and Ear, Harvard Medical School, Boston, MA 02114, USA.
¹⁰ Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy.
¹¹ Eye Clinic, Catania University, San Marco Hospital, Viale Carlo Azeglio Ciampi, 95121 Catania, Italy.
¹² Department of Ophthalmology, University Hospital of Udine, 33100 Udine, Italy.

PMID: 39063944
PMCID: PMC11278069
DOI: 10.3390/jpm14070690

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.

Keywords: artificial intelligence; deep learning; machine learning; macular edema; maculopathy; retinal imaging; retinopathy.

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Conflict of interest statement

The authors declare no conflicts of interest.

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References

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1. Liu X., Zhao C., Wang L., Wang G., Lv B., Lv C., Xie G., Wang F. Evaluation of an OCT-AI-based telemedicine platform for retinal disease screening and referral in a primary care setting. Transl. Vis. Sci. Technol. 2022;11:4. doi: 10.1167/tvst.11.3.4. - DOI - PMC - PubMed
1. Jacoba C.M.P., Celi L.A., Lorch A.C., Fickweiler W., Sobrin L., Gichoya J.W., Aiello L.P., Silva P.S. Bias and non-diversity of big data in artificial intelligence: Focus on retinal diseases. Semin. Ophthalmol. 2023;38:433–441. doi: 10.1080/08820538.2023.2168486. - DOI - PubMed
1. Nguyen H.V., Tan G.S., Tapp R.J., Mital S., Ting D.S., Wong H.T., Tan C.S., Laude A., Tai E.S., Tan N.C., et al. Cost-effectiveness of a National Telemedicine Diabetic Retinopathy Screening Program in Singapore. Ophthalmology. 2016;123:2571–2580. doi: 10.1016/j.ophtha.2016.08.021. - DOI - PubMed
1. Hopkins J., Keane P.A., Balaskas K. Delivering personalized medicine in retinal care: From artificial intelligence algorithms to clinical application. Curr. Opin. Ophthalmol. 2020;31:329–336. doi: 10.1097/ICU.0000000000000677. - DOI - PubMed

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[1] Flaxman S.R., Bourne R.R.A., Resnikoff S., Ackland P., Braithwaite T., Cicinelli M.V., Das A., Jonas J.B., Keeffe J., Kempen J.H., et al. Global causes of blindness and distance vision impairment 1990–2020: A systematic review and meta-analysis. Lancet Glob. Health. 2017;5:e1221–e1234. doi: 10.1016/S2214-109X(17)30393-5. - DOI - PubMed

[2] Flaxman S.R., Bourne R.R.A., Resnikoff S., Ackland P., Braithwaite T., Cicinelli M.V., Das A., Jonas J.B., Keeffe J., Kempen J.H., et al. Global causes of blindness and distance vision impairment 1990–2020: A systematic review and meta-analysis. Lancet Glob. Health. 2017;5:e1221–e1234. doi: 10.1016/S2214-109X(17)30393-5. - DOI - PubMed

[3] Liu X., Zhao C., Wang L., Wang G., Lv B., Lv C., Xie G., Wang F. Evaluation of an OCT-AI-based telemedicine platform for retinal disease screening and referral in a primary care setting. Transl. Vis. Sci. Technol. 2022;11:4. doi: 10.1167/tvst.11.3.4. - DOI - PMC - PubMed

[4] Liu X., Zhao C., Wang L., Wang G., Lv B., Lv C., Xie G., Wang F. Evaluation of an OCT-AI-based telemedicine platform for retinal disease screening and referral in a primary care setting. Transl. Vis. Sci. Technol. 2022;11:4. doi: 10.1167/tvst.11.3.4. - DOI - PMC - PubMed

[5] Jacoba C.M.P., Celi L.A., Lorch A.C., Fickweiler W., Sobrin L., Gichoya J.W., Aiello L.P., Silva P.S. Bias and non-diversity of big data in artificial intelligence: Focus on retinal diseases. Semin. Ophthalmol. 2023;38:433–441. doi: 10.1080/08820538.2023.2168486. - DOI - PubMed

[6] Jacoba C.M.P., Celi L.A., Lorch A.C., Fickweiler W., Sobrin L., Gichoya J.W., Aiello L.P., Silva P.S. Bias and non-diversity of big data in artificial intelligence: Focus on retinal diseases. Semin. Ophthalmol. 2023;38:433–441. doi: 10.1080/08820538.2023.2168486. - DOI - PubMed

[7] Nguyen H.V., Tan G.S., Tapp R.J., Mital S., Ting D.S., Wong H.T., Tan C.S., Laude A., Tai E.S., Tan N.C., et al. Cost-effectiveness of a National Telemedicine Diabetic Retinopathy Screening Program in Singapore. Ophthalmology. 2016;123:2571–2580. doi: 10.1016/j.ophtha.2016.08.021. - DOI - PubMed

[8] Nguyen H.V., Tan G.S., Tapp R.J., Mital S., Ting D.S., Wong H.T., Tan C.S., Laude A., Tai E.S., Tan N.C., et al. Cost-effectiveness of a National Telemedicine Diabetic Retinopathy Screening Program in Singapore. Ophthalmology. 2016;123:2571–2580. doi: 10.1016/j.ophtha.2016.08.021. - DOI - PubMed

[9] Hopkins J., Keane P.A., Balaskas K. Delivering personalized medicine in retinal care: From artificial intelligence algorithms to clinical application. Curr. Opin. Ophthalmol. 2020;31:329–336. doi: 10.1097/ICU.0000000000000677. - DOI - PubMed

[10] Hopkins J., Keane P.A., Balaskas K. Delivering personalized medicine in retinal care: From artificial intelligence algorithms to clinical application. Curr. Opin. Ophthalmol. 2020;31:329–336. doi: 10.1097/ICU.0000000000000677. - DOI - PubMed

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Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence

Affiliations

Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence

Authors

Affiliations

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Conflict of interest statement

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Abstract

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