Eyal Cohen1,2, Dor Bank3, Nir Sorkin4,5, Raja Giryes3, David Varssano4,5. 1. Department of Ophthalmology, Tel Aviv Sourasky Medical Center, 6 Weizmann Street, 64239, Tel Aviv, Israel. coheneyal123@gmail.com. 2. Faculty of Medicine, Tel Aviv University Sackler, Tel Aviv, Israel. coheneyal123@gmail.com. 3. Tel Aviv University School of Electrical Engineering, Tel Aviv, Israel. 4. Department of Ophthalmology, Tel Aviv Sourasky Medical Center, 6 Weizmann Street, 64239, Tel Aviv, Israel. 5. Faculty of Medicine, Tel Aviv University Sackler, Tel Aviv, Israel.
Abstract
PURPOSE: To construct an automatic machine-learning derived algorithm discriminating between normal corneas and suspect irregular or keratoconic corneas. METHODS: A total of 8526 corneal tomography images of 4904 eyes obtained between November 2010 and July 2017 using a combined Scheimpflug/Placido tomographer were retrospectively evaluated. Each image was evaluated for acquisition quality and was labeled as normal, suspect irregular or keratoconic by a cornea specialist. Two algorithms were built. The first was based on 94 instrument-derived output parameters, and the second integrated keratoconus prediction indices of the device with the 94 instrument-derived output parameters. Both models were compared with the tomographer's keratoconus detection algorithms. Out of the 8526 images evaluated, 7104 images of 3787 eyes had sufficient acquisition quality. Of those, 5904 examinations were randomly chosen for construction of the models using the random forest algorithm. The models were then validated using the remaining 1200 examinations. RESULTS: Both RF algorithms had a larger AUC compared with any of the tomographer's KC detection algorithms (p < 10-9). The first constructed model had 90.2% accuracy, sensitivity of 94.2%, and specificity of 89.6% (Youden 0.838). Calculated AUC was 0.964. The second model had 91.5% accuracy, sensitivity of 94.7%, and specificity of 89.8% (Youden 0.846). Calculated AUC was 0.969. CONCLUSION: Using the RF machine-learning algorithm, accuracy of discrimination between normal, suspect irregular and keratoconic corneas approximates that of an experienced corneal expert. Applying machine learning to corneal tomography can facilitate keratoconus screening in large populations as well as off-site screening of refractive surgery candidates.
PURPOSE: To construct an automatic machine-learning derived algorithm discriminating between normal corneas and suspect irregular or keratoconic corneas. METHODS: A total of 8526 corneal tomography images of 4904 eyes obtained between November 2010 and July 2017 using a combined Scheimpflug/Placido tomographer were retrospectively evaluated. Each image was evaluated for acquisition quality and was labeled as normal, suspect irregular or keratoconic by a cornea specialist. Two algorithms were built. The first was based on 94 instrument-derived output parameters, and the second integrated keratoconus prediction indices of the device with the 94 instrument-derived output parameters. Both models were compared with the tomographer's keratoconus detection algorithms. Out of the 8526 images evaluated, 7104 images of 3787 eyes had sufficient acquisition quality. Of those, 5904 examinations were randomly chosen for construction of the models using the random forest algorithm. The models were then validated using the remaining 1200 examinations. RESULTS: Both RF algorithms had a larger AUC compared with any of the tomographer's KC detection algorithms (p < 10-9). The first constructed model had 90.2% accuracy, sensitivity of 94.2%, and specificity of 89.6% (Youden 0.838). Calculated AUC was 0.964. The second model had 91.5% accuracy, sensitivity of 94.7%, and specificity of 89.8% (Youden 0.846). Calculated AUC was 0.969. CONCLUSION: Using the RF machine-learning algorithm, accuracy of discrimination between normal, suspect irregular and keratoconic corneas approximates that of an experienced corneal expert. Applying machine learning to corneal tomography can facilitate keratoconus screening in large populations as well as off-site screening of refractive surgery candidates.
Authors: Bernardo T Lopes; Isaac C Ramos; Marcella Q Salomão; Frederico P Guerra; Steve C Schallhorn; Julie M Schallhorn; Riccardo Vinciguerra; Paolo Vinciguerra; Francis W Price; Marianne O Price; Dan Z Reinstein; Timothy J Archer; Michael W Belin; Aydano P Machado; Renato Ambrósio Journal: Am J Ophthalmol Date: 2018-08-09 Impact factor: 5.258
Authors: David Smadja; David Touboul; Ayala Cohen; Etti Doveh; Marcony R Santhiago; Glauco R Mello; Ronald R Krueger; Joseph Colin Journal: Am J Ophthalmol Date: 2013-06-07 Impact factor: 5.258