Using artificial neural networks for early diagnosis of glaucoma
Purpose: to summarize the experience of the development and application of artificial neural networks (ANW) in early diagnosis of primary open-angle glaucoma (POAG).Material and methods. A total of 690 patients (918 eyes) were tested. The training clinical group consisted of 459 clinical examples (4...
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| Format: | Article |
| Language: | Russian |
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Real Time Ltd
2023-07-01
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| Series: | Российский офтальмологический журнал |
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| Online Access: | https://roj.igb.ru/jour/article/view/1229 |
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| author | E. N. Komarovskikh E. V. Podtynnykh |
| author_facet | E. N. Komarovskikh E. V. Podtynnykh |
| author_sort | E. N. Komarovskikh |
| collection | DOAJ |
| description | Purpose: to summarize the experience of the development and application of artificial neural networks (ANW) in early diagnosis of primary open-angle glaucoma (POAG).Material and methods. A total of 690 patients (918 eyes) were tested. The training clinical group consisted of 459 clinical examples (459 eyes), of which 369 eyes had an initial stage of POAG and 90 eyes had no glaucoma. The testing clinical group was represented by 131 examples (131 eyes), of which 110 eyes belonged to patients with POAG and 21 eyes were without glaucoma. The final diagnostic testing using ANW was conducted on 328 eyes with the diagnosis unknown to the researchers, which belonged to people with suspected POAG. The diagnostic complex included an optimally necessary set of research techniques.Results. ANW identified glaucoma in 198 eyes out of those with suspected glaucoma (60.4 %) with 100 % certainty. 76 eyes (23.2 %) were classified as non-glaucoma, or “healthy”; 54 eyes of the suspected glaucoma patients were identified as “doubtful”, whereupon they were retested by a neural network pool consisting of 5 neural networks. According to the results of the retesting, 28 eyes, or 51.9 % of the “doubtful” ones were identified as having glaucoma, whereas 26 eyes (48.1 %) were identified as non-glaucomatous, i. e. healthy.Conclusion. Our experience suggests that artificial neural networks pose no danger to the doctor or the patient and can be viewed as a very convenient tool for early POAG diagnostics. |
| format | Article |
| id | doaj-art-e0ccd00e45294a5aa6616eafc6b5cca2 |
| institution | DOAJ |
| issn | 2072-0076 2587-5760 |
| language | Russian |
| publishDate | 2023-07-01 |
| publisher | Real Time Ltd |
| record_format | Article |
| series | Российский офтальмологический журнал |
| spelling | doaj-art-e0ccd00e45294a5aa6616eafc6b5cca22025-08-20T03:19:24ZrusReal Time LtdРоссийский офтальмологический журнал2072-00762587-57602023-07-01162283210.21516/2072-0076-2023-16-2-28-32575Using artificial neural networks for early diagnosis of glaucomaE. N. Komarovskikh0E. V. Podtynnykh1Kuban State Medical UniversityS.N. Fedorov Eye Microsurgery National Medical Research Center, Krasnodar branchPurpose: to summarize the experience of the development and application of artificial neural networks (ANW) in early diagnosis of primary open-angle glaucoma (POAG).Material and methods. A total of 690 patients (918 eyes) were tested. The training clinical group consisted of 459 clinical examples (459 eyes), of which 369 eyes had an initial stage of POAG and 90 eyes had no glaucoma. The testing clinical group was represented by 131 examples (131 eyes), of which 110 eyes belonged to patients with POAG and 21 eyes were without glaucoma. The final diagnostic testing using ANW was conducted on 328 eyes with the diagnosis unknown to the researchers, which belonged to people with suspected POAG. The diagnostic complex included an optimally necessary set of research techniques.Results. ANW identified glaucoma in 198 eyes out of those with suspected glaucoma (60.4 %) with 100 % certainty. 76 eyes (23.2 %) were classified as non-glaucoma, or “healthy”; 54 eyes of the suspected glaucoma patients were identified as “doubtful”, whereupon they were retested by a neural network pool consisting of 5 neural networks. According to the results of the retesting, 28 eyes, or 51.9 % of the “doubtful” ones were identified as having glaucoma, whereas 26 eyes (48.1 %) were identified as non-glaucomatous, i. e. healthy.Conclusion. Our experience suggests that artificial neural networks pose no danger to the doctor or the patient and can be viewed as a very convenient tool for early POAG diagnostics.https://roj.igb.ru/jour/article/view/1229primary open-angle glaucomaartificial neural networksearly diagnosis |
| spellingShingle | E. N. Komarovskikh E. V. Podtynnykh Using artificial neural networks for early diagnosis of glaucoma Российский офтальмологический журнал primary open-angle glaucoma artificial neural networks early diagnosis |
| title | Using artificial neural networks for early diagnosis of glaucoma |
| title_full | Using artificial neural networks for early diagnosis of glaucoma |
| title_fullStr | Using artificial neural networks for early diagnosis of glaucoma |
| title_full_unstemmed | Using artificial neural networks for early diagnosis of glaucoma |
| title_short | Using artificial neural networks for early diagnosis of glaucoma |
| title_sort | using artificial neural networks for early diagnosis of glaucoma |
| topic | primary open-angle glaucoma artificial neural networks early diagnosis |
| url | https://roj.igb.ru/jour/article/view/1229 |
| work_keys_str_mv | AT enkomarovskikh usingartificialneuralnetworksforearlydiagnosisofglaucoma AT evpodtynnykh usingartificialneuralnetworksforearlydiagnosisofglaucoma |