A method for measuring intraocular pressure using artificial intelligence technology and fixed-force applanation tonometry
Purpose. To estimate the accuracy of IOP measurement using artificial intelligence (AI) technologies and applanation tonometry with fixed strength. Material and methods. 290 patients (576 eyes) underwent applanation tonometry according to Maklakov with tonometer weights of 5, 10, and 15 g using a mo...
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| Format: | Article |
| Language: | Russian |
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Real Time Ltd
2022-06-01
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| Series: | Российский офтальмологический журнал |
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| Online Access: | https://roj.igb.ru/jour/article/view/960 |
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| author | D. A. Dorofeev A. A. Antonov D. Yu. Vasilenko A. V. Gorobets K. A. Efimova E. V. Kanafin E. V. Karlova E. V. Kirilik I. V. Kozlova E. R. Orlova A. Z. Tsyganov |
| author_facet | D. A. Dorofeev A. A. Antonov D. Yu. Vasilenko A. V. Gorobets K. A. Efimova E. V. Kanafin E. V. Karlova E. V. Kirilik I. V. Kozlova E. R. Orlova A. Z. Tsyganov |
| author_sort | D. A. Dorofeev |
| collection | DOAJ |
| description | Purpose. To estimate the accuracy of IOP measurement using artificial intelligence (AI) technologies and applanation tonometry with fixed strength. Material and methods. 290 patients (576 eyes) underwent applanation tonometry according to Maklakov with tonometer weights of 5, 10, and 15 g using a modified elastotonometry technique followed by an analysis of impression quality and diameter measurements by three independent ophthalmologist experts. The prints were then fed into a neural network to check the repeatability and reproducibility of the measurements. Results. The comparison of the diameters of the Maklakov tonometer prints determined by AI based on the neural network with the measurements data provided by three experts showed that neural network underestimates the measurement results by an average of 0.27 (-3.81; 4.35) mm Hg. At the same time, the intraclass correlation coefficient for all prints was 98.3%. The accuracy of diameter measurements of prints by neural network differs for tonometers of different weights, e.g. for a 5 g tonometer the difference was 0.06 (-3.38; 3.49) mm Hg, for 10 g and 15 g tonometers was 0.14 (-3.8; 3.51) and 0.95 (-3.84; 5.74) mm Hg, respectively. Conclusion. High accuracy and reproducibility of the measurements by the neural network, was shown to surpass the reproducibility of human-implemented measurements. |
| format | Article |
| id | doaj-art-face31591e58450db069ddd60c9b6a7e |
| institution | DOAJ |
| issn | 2072-0076 2587-5760 |
| language | Russian |
| publishDate | 2022-06-01 |
| publisher | Real Time Ltd |
| record_format | Article |
| series | Российский офтальмологический журнал |
| spelling | doaj-art-face31591e58450db069ddd60c9b6a7e2025-08-20T03:19:24ZrusReal Time LtdРоссийский офтальмологический журнал2072-00762587-57602022-06-01152 (Прил)495610.21516/2072-0076-2022-15-2-supplement-49-56449A method for measuring intraocular pressure using artificial intelligence technology and fixed-force applanation tonometryD. A. Dorofeev0A. A. Antonov1D. Yu. Vasilenko2A. V. Gorobets3K. A. Efimova4E. V. Kanafin5E. V. Karlova6E. V. Kirilik7I. V. Kozlova8E. R. Orlova9A. Z. Tsyganov10City Clinical Hospital No. 2, Clinic No. 1Research Institute of Eye DiseasesLTD AplitCenter of additional Education; South Ural State University (National Research University)City Clinical Hospital No. 2, Clinic No. 1South Ural State University (National Research University)Eroshevsky Regional Clinical Eye HospitalCity Clinical Hospital No. 2, Clinic No. 1Research Institute of Eye DiseasesChelyabinsk State UniversityS.N. Fedorov Eye Microsurgery ComplexPurpose. To estimate the accuracy of IOP measurement using artificial intelligence (AI) technologies and applanation tonometry with fixed strength. Material and methods. 290 patients (576 eyes) underwent applanation tonometry according to Maklakov with tonometer weights of 5, 10, and 15 g using a modified elastotonometry technique followed by an analysis of impression quality and diameter measurements by three independent ophthalmologist experts. The prints were then fed into a neural network to check the repeatability and reproducibility of the measurements. Results. The comparison of the diameters of the Maklakov tonometer prints determined by AI based on the neural network with the measurements data provided by three experts showed that neural network underestimates the measurement results by an average of 0.27 (-3.81; 4.35) mm Hg. At the same time, the intraclass correlation coefficient for all prints was 98.3%. The accuracy of diameter measurements of prints by neural network differs for tonometers of different weights, e.g. for a 5 g tonometer the difference was 0.06 (-3.38; 3.49) mm Hg, for 10 g and 15 g tonometers was 0.14 (-3.8; 3.51) and 0.95 (-3.84; 5.74) mm Hg, respectively. Conclusion. High accuracy and reproducibility of the measurements by the neural network, was shown to surpass the reproducibility of human-implemented measurements.https://roj.igb.ru/jour/article/view/960glaucomaapplanation tonometryintraocular pressureophthalmotonometryartificial intelligencefixed force tonometry |
| spellingShingle | D. A. Dorofeev A. A. Antonov D. Yu. Vasilenko A. V. Gorobets K. A. Efimova E. V. Kanafin E. V. Karlova E. V. Kirilik I. V. Kozlova E. R. Orlova A. Z. Tsyganov A method for measuring intraocular pressure using artificial intelligence technology and fixed-force applanation tonometry Российский офтальмологический журнал glaucoma applanation tonometry intraocular pressure ophthalmotonometry artificial intelligence fixed force tonometry |
| title | A method for measuring intraocular pressure using artificial intelligence technology and fixed-force applanation tonometry |
| title_full | A method for measuring intraocular pressure using artificial intelligence technology and fixed-force applanation tonometry |
| title_fullStr | A method for measuring intraocular pressure using artificial intelligence technology and fixed-force applanation tonometry |
| title_full_unstemmed | A method for measuring intraocular pressure using artificial intelligence technology and fixed-force applanation tonometry |
| title_short | A method for measuring intraocular pressure using artificial intelligence technology and fixed-force applanation tonometry |
| title_sort | method for measuring intraocular pressure using artificial intelligence technology and fixed force applanation tonometry |
| topic | glaucoma applanation tonometry intraocular pressure ophthalmotonometry artificial intelligence fixed force tonometry |
| url | https://roj.igb.ru/jour/article/view/960 |
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