Evaluating real-world performance of an automated offline glaucoma AI on a smartphone fundus camera across glaucoma severity stages.
<h4>Purpose</h4>Leveraging an artificial intelligence system (AI) for glaucoma screening can mitigate the current challenges and provide prompt detection and management crucial in averting irreversible blindness. The study reports the real-world performance of a glaucoma AI system deploy...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0324883 |
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| author | Sirisha Senthil Divya Parthasarathy Rao Florian M Savoy Kalpa Negiloni Shreya Bhandary Raghava Chary Garudadri Chandrashekar |
| author_facet | Sirisha Senthil Divya Parthasarathy Rao Florian M Savoy Kalpa Negiloni Shreya Bhandary Raghava Chary Garudadri Chandrashekar |
| author_sort | Sirisha Senthil |
| collection | DOAJ |
| description | <h4>Purpose</h4>Leveraging an artificial intelligence system (AI) for glaucoma screening can mitigate the current challenges and provide prompt detection and management crucial in averting irreversible blindness. The study reports the real-world performance of a glaucoma AI system deployed on a smartphone-based fundus camera across various severities of glaucoma.<h4>Methods</h4>In this prospective comparative study at a tertiary care glaucoma clinic, consecutive patients were evaluated by a glaucoma specialist using clinical assessment, visual field tests, and SD-OCT, and categorized as definite glaucoma, glaucoma suspect, or no glaucoma. For glaucoma patients, severity was determined using Hoddap-Parrish-Anderson criteria based on visual field mean deviation (MD). A disc-centered image per eye was captured using a validated portable non-mydriatic fundus camera. The AI tool's ability to detect referral-warranted glaucoma (glaucoma and glaucoma suspects) versus no glaucoma was compared to the specialist's diagnosis.<h4>Results</h4>We included 213 participants with a mean age of 55 ± 14.7 years (18, 88). The glaucoma specialist diagnosed 129 subjects as definite glaucoma (early-23, moderate-31, severe-75), 33-disc suspects and 51 as no-glaucoma. The automated AI system based on fundus images achieved an overall diagnostic accuracy of 92.02%, sensitivity of 91.36% (95%CI 85.93% to 95.19%) and specificity of 94.12% (83.76% to 98.77%) for referral warranted glaucoma. The 14 false negatives included 5-disc suspects and 9 definite glaucoma (3-early, 3-moderate and 3-advanced glaucoma). The sensitivity of AI for detecting early, moderate and advanced glaucoma was 86.9% (95%CI 66.4-97.2), 90.3% (95%CI 74.3-97.96), and 96% (88.75% to 99.17%) respectively.<h4>Conclusion</h4>In a real-world setting, the AI-based offline tool integrated on a smartphone fundus camera showed a promising performance in detecting referral-warranted glaucoma compared to a glaucoma specialist's diagnosis. The AI showed higher accuracy in detecting advanced glaucoma followed by moderate and early glaucoma. |
| format | Article |
| id | doaj-art-2bf99264d1054ead9bd0efeff9ead138 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-2bf99264d1054ead9bd0efeff9ead1382025-08-20T03:16:00ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01206e032488310.1371/journal.pone.0324883Evaluating real-world performance of an automated offline glaucoma AI on a smartphone fundus camera across glaucoma severity stages.Sirisha SenthilDivya Parthasarathy RaoFlorian M SavoyKalpa NegiloniShreya BhandaryRaghava CharyGarudadri Chandrashekar<h4>Purpose</h4>Leveraging an artificial intelligence system (AI) for glaucoma screening can mitigate the current challenges and provide prompt detection and management crucial in averting irreversible blindness. The study reports the real-world performance of a glaucoma AI system deployed on a smartphone-based fundus camera across various severities of glaucoma.<h4>Methods</h4>In this prospective comparative study at a tertiary care glaucoma clinic, consecutive patients were evaluated by a glaucoma specialist using clinical assessment, visual field tests, and SD-OCT, and categorized as definite glaucoma, glaucoma suspect, or no glaucoma. For glaucoma patients, severity was determined using Hoddap-Parrish-Anderson criteria based on visual field mean deviation (MD). A disc-centered image per eye was captured using a validated portable non-mydriatic fundus camera. The AI tool's ability to detect referral-warranted glaucoma (glaucoma and glaucoma suspects) versus no glaucoma was compared to the specialist's diagnosis.<h4>Results</h4>We included 213 participants with a mean age of 55 ± 14.7 years (18, 88). The glaucoma specialist diagnosed 129 subjects as definite glaucoma (early-23, moderate-31, severe-75), 33-disc suspects and 51 as no-glaucoma. The automated AI system based on fundus images achieved an overall diagnostic accuracy of 92.02%, sensitivity of 91.36% (95%CI 85.93% to 95.19%) and specificity of 94.12% (83.76% to 98.77%) for referral warranted glaucoma. The 14 false negatives included 5-disc suspects and 9 definite glaucoma (3-early, 3-moderate and 3-advanced glaucoma). The sensitivity of AI for detecting early, moderate and advanced glaucoma was 86.9% (95%CI 66.4-97.2), 90.3% (95%CI 74.3-97.96), and 96% (88.75% to 99.17%) respectively.<h4>Conclusion</h4>In a real-world setting, the AI-based offline tool integrated on a smartphone fundus camera showed a promising performance in detecting referral-warranted glaucoma compared to a glaucoma specialist's diagnosis. The AI showed higher accuracy in detecting advanced glaucoma followed by moderate and early glaucoma.https://doi.org/10.1371/journal.pone.0324883 |
| spellingShingle | Sirisha Senthil Divya Parthasarathy Rao Florian M Savoy Kalpa Negiloni Shreya Bhandary Raghava Chary Garudadri Chandrashekar Evaluating real-world performance of an automated offline glaucoma AI on a smartphone fundus camera across glaucoma severity stages. PLoS ONE |
| title | Evaluating real-world performance of an automated offline glaucoma AI on a smartphone fundus camera across glaucoma severity stages. |
| title_full | Evaluating real-world performance of an automated offline glaucoma AI on a smartphone fundus camera across glaucoma severity stages. |
| title_fullStr | Evaluating real-world performance of an automated offline glaucoma AI on a smartphone fundus camera across glaucoma severity stages. |
| title_full_unstemmed | Evaluating real-world performance of an automated offline glaucoma AI on a smartphone fundus camera across glaucoma severity stages. |
| title_short | Evaluating real-world performance of an automated offline glaucoma AI on a smartphone fundus camera across glaucoma severity stages. |
| title_sort | evaluating real world performance of an automated offline glaucoma ai on a smartphone fundus camera across glaucoma severity stages |
| url | https://doi.org/10.1371/journal.pone.0324883 |
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