Мethods of Machine Learning in Ophthalmology: Review
Machine learning is applied in every field of human activity using digital data. In recent years, many papers have been published concerning artificial intelligence use in classification, regression and segmentation purposes in medicine and in ophthalmology, in particular. Artificial intelligence is...
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| Format: | Article |
| Language: | Russian |
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Ophthalmology Publishing Group
2020-04-01
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| Series: | Oftalʹmologiâ |
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| Online Access: | https://www.ophthalmojournal.com/opht/article/view/1127 |
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| author | D. D. Garri S. V. Saakyan I. P. Khoroshilova-Maslova A. Yu. Tsygankov O. I. Nikitin G. Yu. Tarasov |
| author_facet | D. D. Garri S. V. Saakyan I. P. Khoroshilova-Maslova A. Yu. Tsygankov O. I. Nikitin G. Yu. Tarasov |
| author_sort | D. D. Garri |
| collection | DOAJ |
| description | Machine learning is applied in every field of human activity using digital data. In recent years, many papers have been published concerning artificial intelligence use in classification, regression and segmentation purposes in medicine and in ophthalmology, in particular. Artificial intelligence is a subsection of computer science and its principles, and concepts are often incomprehensible or used and interpreted by doctors incorrectly. Diagnostics of ophthalmology patients is associated with a significant amount of medical data that can be used for further software processing. By using of machine learning methods, it’s possible to find out, identify and count almost any pathological signs of diseases by analyzing medical images, clinical and laboratory data. Machine learning includes models and algorithms that mimic the architecture of biological neural networks. The greatest interest in the field is represented by artificial neural networks, in particular, networks based on deep learning due to the ability of the latter to work effectively with complex and multidimensional databases, coupled with the increasing availability of databases and performance of graphics processors. Artificial neural networks have the potential to be used in automated screening, determining the stage of diseases, predicting the therapeutic effect of treatment and the diseases outcome in the analysis of clinical data in patients with diabetic retinopathy, age-related macular degeneration, glaucoma, cataracts, ocular tumors and concomitant pathology. The main characteristics were the size of the training and validation datasets, accuracy, sensitivity, specificity, AUROC (Area Under Receiver Operating Characteristic Curve). A number of studies investigate the comparative characteristics of algorithms. Many of the articles presented in the review have shown the results in accuracy, sensitivity, specificity, AUROC, error values that exceed the corresponding indicators of an average ophthalmologist. Their introduction into routine clinical practice will increase the diagnostic, therapeutic and professional capabilities of a clinicians, which is especially important in the field of ophthalmic oncology, where there is a patient survival matter. |
| format | Article |
| id | doaj-art-b0c7ca305ee542e1a051b4581b7bfae9 |
| institution | Matheson Library |
| issn | 1816-5095 2500-0845 |
| language | Russian |
| publishDate | 2020-04-01 |
| publisher | Ophthalmology Publishing Group |
| record_format | Article |
| series | Oftalʹmologiâ |
| spelling | doaj-art-b0c7ca305ee542e1a051b4581b7bfae92025-08-04T14:32:21ZrusOphthalmology Publishing GroupOftalʹmologiâ1816-50952500-08452020-04-01171203110.18008/1816-5095-2020-1-20-31597Мethods of Machine Learning in Ophthalmology: ReviewD. D. Garri0S. V. Saakyan1I. P. Khoroshilova-Maslova2A. Yu. Tsygankov3O. I. Nikitin4G. Yu. Tarasov5Moscow State Medical Stomatological University; Limited Liability Company “Artificial networks and technologies”Moscow State Medical Stomatological University; Helmholtz National Medical Сenter of Eye DiseasesHelmholtz National Medical Сenter of Eye DiseasesMoscow State Medical Stomatological University; Helmholtz National Medical Сenter of Eye DiseasesLimited Liability Company “Artificial networks and technologies”Limited Liability Company “Artificial networks and technologies”Machine learning is applied in every field of human activity using digital data. In recent years, many papers have been published concerning artificial intelligence use in classification, regression and segmentation purposes in medicine and in ophthalmology, in particular. Artificial intelligence is a subsection of computer science and its principles, and concepts are often incomprehensible or used and interpreted by doctors incorrectly. Diagnostics of ophthalmology patients is associated with a significant amount of medical data that can be used for further software processing. By using of machine learning methods, it’s possible to find out, identify and count almost any pathological signs of diseases by analyzing medical images, clinical and laboratory data. Machine learning includes models and algorithms that mimic the architecture of biological neural networks. The greatest interest in the field is represented by artificial neural networks, in particular, networks based on deep learning due to the ability of the latter to work effectively with complex and multidimensional databases, coupled with the increasing availability of databases and performance of graphics processors. Artificial neural networks have the potential to be used in automated screening, determining the stage of diseases, predicting the therapeutic effect of treatment and the diseases outcome in the analysis of clinical data in patients with diabetic retinopathy, age-related macular degeneration, glaucoma, cataracts, ocular tumors and concomitant pathology. The main characteristics were the size of the training and validation datasets, accuracy, sensitivity, specificity, AUROC (Area Under Receiver Operating Characteristic Curve). A number of studies investigate the comparative characteristics of algorithms. Many of the articles presented in the review have shown the results in accuracy, sensitivity, specificity, AUROC, error values that exceed the corresponding indicators of an average ophthalmologist. Their introduction into routine clinical practice will increase the diagnostic, therapeutic and professional capabilities of a clinicians, which is especially important in the field of ophthalmic oncology, where there is a patient survival matter.https://www.ophthalmojournal.com/opht/article/view/1127artifitial intellegencemachine learningdeep learningneural networksophthalmologyophthalmic oncology |
| spellingShingle | D. D. Garri S. V. Saakyan I. P. Khoroshilova-Maslova A. Yu. Tsygankov O. I. Nikitin G. Yu. Tarasov Мethods of Machine Learning in Ophthalmology: Review Oftalʹmologiâ artifitial intellegence machine learning deep learning neural networks ophthalmology ophthalmic oncology |
| title | Мethods of Machine Learning in Ophthalmology: Review |
| title_full | Мethods of Machine Learning in Ophthalmology: Review |
| title_fullStr | Мethods of Machine Learning in Ophthalmology: Review |
| title_full_unstemmed | Мethods of Machine Learning in Ophthalmology: Review |
| title_short | Мethods of Machine Learning in Ophthalmology: Review |
| title_sort | мethods of machine learning in ophthalmology review |
| topic | artifitial intellegence machine learning deep learning neural networks ophthalmology ophthalmic oncology |
| url | https://www.ophthalmojournal.com/opht/article/view/1127 |
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