Retinal revelations: Seeing beyond the eye with artificial intelligence
Artificial intelligence (AI) has revolutionized ophthalmology by aiding in the diagnosis, prognosis, and treatment planning of various eye diseases. However, AI’s potential extends beyond ocular conditions. By analyzing eye-related biomarkers, AI can utilize the eye as a window into the body’s syste...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2024-12-01
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| Series: | Kerala Journal of Ophthalmology |
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| Online Access: | https://journals.lww.com/10.4103/kjo.kjo_124_24 |
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| author | John Davis Akkara |
| author_facet | John Davis Akkara |
| author_sort | John Davis Akkara |
| collection | DOAJ |
| description | Artificial intelligence (AI) has revolutionized ophthalmology by aiding in the diagnosis, prognosis, and treatment planning of various eye diseases. However, AI’s potential extends beyond ocular conditions. By analyzing eye-related biomarkers, AI can utilize the eye as a window into the body’s systemic health. This field, known as oculomics, leverages AI and deep learning algorithms to process vast amounts of data from imaging techniques such as fundus photography, optical coherence tomography (OCT), OCT angiography, infrared iris imaging, slit-lamp photography, and external eye photography. AI-powered analysis of these images can predict systemic diseases such as Alzheimer’s, Parkinson’s, cardiovascular disease, cerebrovascular disease, chronic kidney disease, and liver disease. Retinal changes —including alterations in the retinal nerve fiber layer, ganglion cell layer, and retinal vessels —serve as valuable indicators of these conditions. Additionally, AI can estimate age, sex, body composition, and other health parameters from eye images. While the potential of AI in oculomics is promising, challenges such as access to ophthalmic imaging, data quality, and the need for rigorous validation must be addressed to ensure its widespread adoption and clinical utility. Nevertheless, AI holds the potential to transform healthcare by enabling early detection, noninvasive screening, and personalized treatment for a wide range of systemic diseases. |
| format | Article |
| id | doaj-art-c71df14c39c143f2b914b980127128b8 |
| institution | Kabale University |
| issn | 0976-6677 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Kerala Journal of Ophthalmology |
| spelling | doaj-art-c71df14c39c143f2b914b980127128b82025-01-07T07:34:36ZengWolters Kluwer Medknow PublicationsKerala Journal of Ophthalmology0976-66772024-12-0136329529810.4103/kjo.kjo_124_24Retinal revelations: Seeing beyond the eye with artificial intelligenceJohn Davis AkkaraArtificial intelligence (AI) has revolutionized ophthalmology by aiding in the diagnosis, prognosis, and treatment planning of various eye diseases. However, AI’s potential extends beyond ocular conditions. By analyzing eye-related biomarkers, AI can utilize the eye as a window into the body’s systemic health. This field, known as oculomics, leverages AI and deep learning algorithms to process vast amounts of data from imaging techniques such as fundus photography, optical coherence tomography (OCT), OCT angiography, infrared iris imaging, slit-lamp photography, and external eye photography. AI-powered analysis of these images can predict systemic diseases such as Alzheimer’s, Parkinson’s, cardiovascular disease, cerebrovascular disease, chronic kidney disease, and liver disease. Retinal changes —including alterations in the retinal nerve fiber layer, ganglion cell layer, and retinal vessels —serve as valuable indicators of these conditions. Additionally, AI can estimate age, sex, body composition, and other health parameters from eye images. While the potential of AI in oculomics is promising, challenges such as access to ophthalmic imaging, data quality, and the need for rigorous validation must be addressed to ensure its widespread adoption and clinical utility. Nevertheless, AI holds the potential to transform healthcare by enabling early detection, noninvasive screening, and personalized treatment for a wide range of systemic diseases.https://journals.lww.com/10.4103/kjo.kjo_124_24agealzheimersartificial intelligencecardiovascular diseasecerebrovascular diseasecognitive impairmentdiabetesgenderkidney diseaseliver diseasemachine learningoctparkinsonssystemic diseases |
| spellingShingle | John Davis Akkara Retinal revelations: Seeing beyond the eye with artificial intelligence Kerala Journal of Ophthalmology age alzheimers artificial intelligence cardiovascular disease cerebrovascular disease cognitive impairment diabetes gender kidney disease liver disease machine learning oct parkinsons systemic diseases |
| title | Retinal revelations: Seeing beyond the eye with artificial intelligence |
| title_full | Retinal revelations: Seeing beyond the eye with artificial intelligence |
| title_fullStr | Retinal revelations: Seeing beyond the eye with artificial intelligence |
| title_full_unstemmed | Retinal revelations: Seeing beyond the eye with artificial intelligence |
| title_short | Retinal revelations: Seeing beyond the eye with artificial intelligence |
| title_sort | retinal revelations seeing beyond the eye with artificial intelligence |
| topic | age alzheimers artificial intelligence cardiovascular disease cerebrovascular disease cognitive impairment diabetes gender kidney disease liver disease machine learning oct parkinsons systemic diseases |
| url | https://journals.lww.com/10.4103/kjo.kjo_124_24 |
| work_keys_str_mv | AT johndavisakkara retinalrevelationsseeingbeyondtheeyewithartificialintelligence |