Deep Learning Algorithm for Keratoconus Detection from Tomographic Maps and Corneal Biomechanics: A Diagnostic Study
Purpose To develop an artificial intelligence (AI) approach for differentiating between normal cornea, subclinical, and keratoconus (KC) using tomographic maps from Pentacam (Oculus) and corneal biomechanics from Corvis ST (Oculus). Methods A total of 1,668 tomographic (769 patients) and 611 biomech...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2024-10-01
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| Series: | Journal of Current Ophthalmology |
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| Online Access: | https://journals.lww.com/10.4103/joco.joco_18_24 |
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| author | Wiyada Quanchareonsap Ngamjit Kasetsuwan Usanee Reinprayoon Yonrawee Piyacomn Thitima Wungcharoen Monthira Jermjutitham |
| author_facet | Wiyada Quanchareonsap Ngamjit Kasetsuwan Usanee Reinprayoon Yonrawee Piyacomn Thitima Wungcharoen Monthira Jermjutitham |
| author_sort | Wiyada Quanchareonsap |
| collection | DOAJ |
| description | Purpose
To develop an artificial intelligence (AI) approach for differentiating between normal cornea, subclinical, and keratoconus (KC) using tomographic maps from Pentacam (Oculus) and corneal biomechanics from Corvis ST (Oculus).
Methods
A total of 1,668 tomographic (769 patients) and 611 biomechanical (307 patients) images from the Chula Refractive Surgery Center, King Chulalongkorn Memorial Hospital were included. The sample size was divided into the Pentacam and combined Pentacam-Corvis groups. Different convolutional neural network approaches were used to enhance the KC and subclinical KC detection performance.
Results
AI model 1, which obtained refractive maps from Pentacam, achieved an area under the receiver operating characteristic curve (AUC) of 0.938 and accuracy of 0.947 (sensitivity, 90.8% and specificity, 96.9%). AI model 2, which added dynamic corneal response and the Vinciguerra screening report from Corvis ST to AI Model 1, achieved an AUC of 0.985 and accuracy of 0.956 (sensitivity, 93.0% and specificity, 94.3%). AI model 3, which added the corneal biomechanical index to AI Model 2, reached an AUC of 0.991 and accuracy of 0.956 (sensitivity, 93.0% and specificity, 94.3%).
Conclusions
Our study showed that AI models using either anterior corneal curvature alone or combined with corneal biomechanics could help classify normal and keratoconic corneas, which would make diagnosis more accurate and would be helpful in decision-making for the treatment. |
| format | Article |
| id | doaj-art-e748f6f10fef4c5f93cf7cb456de3062 |
| institution | OA Journals |
| issn | 2452-2325 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Current Ophthalmology |
| spelling | doaj-art-e748f6f10fef4c5f93cf7cb456de30622025-08-20T02:12:34ZengWolters Kluwer Medknow PublicationsJournal of Current Ophthalmology2452-23252024-10-01361465310.4103/joco.joco_18_24Deep Learning Algorithm for Keratoconus Detection from Tomographic Maps and Corneal Biomechanics: A Diagnostic StudyWiyada QuanchareonsapNgamjit KasetsuwanUsanee ReinprayoonYonrawee PiyacomnThitima WungcharoenMonthira JermjutithamPurpose To develop an artificial intelligence (AI) approach for differentiating between normal cornea, subclinical, and keratoconus (KC) using tomographic maps from Pentacam (Oculus) and corneal biomechanics from Corvis ST (Oculus). Methods A total of 1,668 tomographic (769 patients) and 611 biomechanical (307 patients) images from the Chula Refractive Surgery Center, King Chulalongkorn Memorial Hospital were included. The sample size was divided into the Pentacam and combined Pentacam-Corvis groups. Different convolutional neural network approaches were used to enhance the KC and subclinical KC detection performance. Results AI model 1, which obtained refractive maps from Pentacam, achieved an area under the receiver operating characteristic curve (AUC) of 0.938 and accuracy of 0.947 (sensitivity, 90.8% and specificity, 96.9%). AI model 2, which added dynamic corneal response and the Vinciguerra screening report from Corvis ST to AI Model 1, achieved an AUC of 0.985 and accuracy of 0.956 (sensitivity, 93.0% and specificity, 94.3%). AI model 3, which added the corneal biomechanical index to AI Model 2, reached an AUC of 0.991 and accuracy of 0.956 (sensitivity, 93.0% and specificity, 94.3%). Conclusions Our study showed that AI models using either anterior corneal curvature alone or combined with corneal biomechanics could help classify normal and keratoconic corneas, which would make diagnosis more accurate and would be helpful in decision-making for the treatment.https://journals.lww.com/10.4103/joco.joco_18_24artificial intelligenceforme fruste keratoconuskeratoconuskeratoconus suspectmachine learningsubclinical keratoconus |
| spellingShingle | Wiyada Quanchareonsap Ngamjit Kasetsuwan Usanee Reinprayoon Yonrawee Piyacomn Thitima Wungcharoen Monthira Jermjutitham Deep Learning Algorithm for Keratoconus Detection from Tomographic Maps and Corneal Biomechanics: A Diagnostic Study Journal of Current Ophthalmology artificial intelligence forme fruste keratoconus keratoconus keratoconus suspect machine learning subclinical keratoconus |
| title | Deep Learning Algorithm for Keratoconus Detection from Tomographic Maps and Corneal Biomechanics: A Diagnostic Study |
| title_full | Deep Learning Algorithm for Keratoconus Detection from Tomographic Maps and Corneal Biomechanics: A Diagnostic Study |
| title_fullStr | Deep Learning Algorithm for Keratoconus Detection from Tomographic Maps and Corneal Biomechanics: A Diagnostic Study |
| title_full_unstemmed | Deep Learning Algorithm for Keratoconus Detection from Tomographic Maps and Corneal Biomechanics: A Diagnostic Study |
| title_short | Deep Learning Algorithm for Keratoconus Detection from Tomographic Maps and Corneal Biomechanics: A Diagnostic Study |
| title_sort | deep learning algorithm for keratoconus detection from tomographic maps and corneal biomechanics a diagnostic study |
| topic | artificial intelligence forme fruste keratoconus keratoconus keratoconus suspect machine learning subclinical keratoconus |
| url | https://journals.lww.com/10.4103/joco.joco_18_24 |
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