DeepLensNet: Deep learning automated diagnosis and quantitative classification of cataract type and severity
Purpose - To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs. - Design - DeepLensNet was trained by applying deep learning models to the Age-Related Eye Disease Study (AREDS) dataset. - Participants...
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
May 2022
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| In: |
Ophthalmology
Year: 2022, Jahrgang: 129, Heft: 5, Pages: 571-584 |
| ISSN: | 1549-4713 |
| DOI: | 10.1016/j.ophtha.2021.12.017 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.ophtha.2021.12.017 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S0161642021009672 
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| Verfasserangaben: | Tiarnan D.L. Keenan, Qingyu Chen, Elvira Agrón, Yih-Chung Tham, Jocelyn Hui Lin Goh, Xiaofeng Lei, Yi Pin Ng, Yong Liu, Xinxing Xu, Ching-Yu Cheng, Mukharram M. Bikbov, Jost B. Jonas, Sanjeeb Bhandari, Geoffrey K. Broadhead, Marcus H. Colyer, Jonathan Corsini, Chantal Cousineau-Krieger, William Gensheimer, David Grasic, Tania Lamba, M. Teresa Magone, Michele Maiberger, Arnold Oshinsky, Boonkit Purt, Soo Y. Shin, Alisa T. Thavikulwat, Zhiyong Lu, Emily Y. Chew, for the AREDS Deep Learning Research Group |
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| 245 | 1 | 0 | |a DeepLensNet: Deep learning automated diagnosis and quantitative classification of cataract type and severity |c Tiarnan D.L. Keenan, Qingyu Chen, Elvira Agrón, Yih-Chung Tham, Jocelyn Hui Lin Goh, Xiaofeng Lei, Yi Pin Ng, Yong Liu, Xinxing Xu, Ching-Yu Cheng, Mukharram M. Bikbov, Jost B. Jonas, Sanjeeb Bhandari, Geoffrey K. Broadhead, Marcus H. Colyer, Jonathan Corsini, Chantal Cousineau-Krieger, William Gensheimer, David Grasic, Tania Lamba, M. Teresa Magone, Michele Maiberger, Arnold Oshinsky, Boonkit Purt, Soo Y. Shin, Alisa T. Thavikulwat, Zhiyong Lu, Emily Y. Chew, for the AREDS Deep Learning Research Group |
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| 500 | |a Online verfügbar: 3 January 2022, Artikelversion: 20 April 2022 | ||
| 500 | |a AREDS Deep Learning Research Group: Priscilla Ajilore, Alex Akman, Nadim S. Azar, William S. Azar, Bryan Chan, Victor Cox, Amisha D. Dave, Rachna Dhanjal, Mary Donovan, Maureen Farrell, Francisca Finkel, Timothy Goblirsch, Wesley Ha, Christine Hill, Aman Kumar, Kristen Kent, Arielle Lee, Pujan Patel, David Peprah, Emma Piliponis, Evan Selzer, Benjamin Swaby, Stephen Tenney, and Alexander Zeleny | ||
| 500 | |a Gesehen am 26.02.2024 | ||
| 520 | |a Purpose - To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs. - Design - DeepLensNet was trained by applying deep learning models to the Age-Related Eye Disease Study (AREDS) dataset. - Participants - A total of 18 999 photographs (6333 triplets) from longitudinal follow-up of 1137 eyes (576 AREDS participants). - Methods - Deep learning models were trained to detect and quantify nuclear sclerosis (NS; scale 0.9-7.1) from 45-degree slit-lamp photographs and cortical lens opacity (CLO; scale 0%-100%) and posterior subcapsular cataract (PSC; scale 0%-100%) from retroillumination photographs. DeepLensNet performance was compared with that of 14 ophthalmologists and 24 medical students. - Main Outcome Measures - Mean squared error (MSE). - Results - On the full test set, mean MSE for DeepLensNet was 0.23 (standard deviation [SD], 0.01) for NS, 13.1 (SD, 1.6) for CLO, and 16.6 (SD, 2.4) for PSC. On a subset of the test set (substantially enriched for positive cases of CLO and PSC), for NS, mean MSE for DeepLensNet was 0.23 (SD, 0.02), compared with 0.98 (SD, 0.24; P = 0.000001) for the ophthalmologists and 1.24 (SD, 0.34; P = 0.000005) for the medical students. For CLO, mean MSE was 53.5 (SD, 14.8), compared with 134.9 (SD, 89.9; P = 0.003) for the ophthalmologists and 433.6 (SD, 962.1; P = 0.0007) for the medical students. For PSC, mean MSE was 171.9 (SD, 38.9), compared with 176.8 (SD, 98.0; P = 0.67) for the ophthalmologists and 398.2 (SD, 645.4; P = 0.18) for the medical students. In external validation on the Singapore Malay Eye Study (sampled to reflect the cataract severity distribution in AREDS), the MSE for DeepSeeNet was 1.27 for NS and 25.5 for PSC. - Conclusions - DeepLensNet performed automated and quantitative classification of cataract severity for all 3 types of age-related cataract. For the 2 most common types (NS and CLO), the accuracy was significantly superior to that of ophthalmologists; for the least common type (PSC), it was similar. DeepLensNet may have wide potential applications in both clinical and research domains. In the future, such approaches may increase the accessibility of cataract assessment globally. The code and models are available at https://github.com/ncbi/deeplensnet. | ||
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Automated diagnosis | |
| 650 | 4 | |a Cataract | |
| 650 | 4 | |a Cortical cataract | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Nuclear sclerosis | |
| 650 | 4 | |a Posterior subcapsular cataract | |
| 650 | 4 | |a Severity classification | |
| 650 | 4 | |a Telemedicine | |
| 650 | 4 | |a Teleophthalmology | |
| 700 | 1 | |a Chen, Qingyu |e VerfasserIn |4 aut | |
| 700 | 1 | |a Agrón, Elvira |e VerfasserIn |4 aut | |
| 700 | 1 | |a Tham, Yih-Chung |e VerfasserIn |4 aut | |
| 700 | 1 | |a Goh, Jocelyn Hui Lin |e VerfasserIn |4 aut | |
| 700 | 1 | |a Lei, Xiaofeng |e VerfasserIn |4 aut | |
| 700 | 1 | |a Ng, Yi Pin |e VerfasserIn |4 aut | |
| 700 | 1 | |a Liu, Yong |e VerfasserIn |4 aut | |
| 700 | 1 | |a Xu, Xinxing |e VerfasserIn |4 aut | |
| 700 | 1 | |a Cheng, Ching-Yu |e VerfasserIn |4 aut | |
| 700 | 1 | |a Bikbov, Mukharram M. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Jonas, Jost B. |d 1958- |e VerfasserIn |0 (DE-588)1028286732 |0 (DE-627)730536823 |0 (DE-576)37578537X |4 aut | |
| 700 | 1 | |a Bhandari, Sanjeeb |e VerfasserIn |4 aut | |
| 700 | 1 | |a Broadhead, Geoffrey K. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Colyer, Marcus H. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Corsini, Jonathan |e VerfasserIn |4 aut | |
| 700 | 1 | |a Cousineau-Krieger, Chantal |e VerfasserIn |4 aut | |
| 700 | 1 | |a Gensheimer, William |e VerfasserIn |4 aut | |
| 700 | 1 | |a Grasic, David |e VerfasserIn |4 aut | |
| 700 | 1 | |a Lamba, Tania |e VerfasserIn |4 aut | |
| 700 | 1 | |a Magone, M. Teresa |e VerfasserIn |4 aut | |
| 700 | 1 | |a Maiberger, Michele |e VerfasserIn |4 aut | |
| 700 | 1 | |a Oshinsky, Arnold |e VerfasserIn |4 aut | |
| 700 | 1 | |a Purt, Boonkit |e VerfasserIn |4 aut | |
| 700 | 1 | |a Shin, Soo Y. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Thavikulwat, Alisa T. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Lu, Zhiyong |e VerfasserIn |4 aut | |
| 700 | 1 | |a Chew, Emily Y. |e VerfasserIn |4 aut | |
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