Deep Learning–Based Estimation of Implantable Collamer Lens Vault Using Optical Coherence Tomography

Assaf, Jad F.; Reinstein, Dan Z.; Zakka, Cyril; Arbelaez, Juan G.; Boufadel, Peter; Choufani, Mathieu; Archer, Timothy J; Ibrahim, Perla; Awwad, Shady T.

doi:10.1016/j.ajo.2023.04.008

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…These advancements have proven effective in a range of clinical applications, from automated measurements such as ICL vault estimation (3,4), to sophisticated disease detection (5-7), and even in the creation of synthetic yet realistic corneal OCT images using deep learning (8).…”

Section: Introductionmentioning

confidence: 99%

“…This integration marks a notable departure from the traditional focus on retinal OCT, expanding the scope of AI in ocular diagnostics. These advancements have proven effective in a range of clinical applications, from automated measurements such as ICL vault estimation (3,4), to sophisticated disease detection (5–7), and even in the creation of synthetic yet realistic corneal OCT images using deep learning (8).…”

Section: Introductionmentioning

confidence: 99%

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Automated Detection of Keratorefractive Laser Surgeries on Optical Coherence Tomography using Deep Learning

Assaf,

Yazbeck,

Reinstein

et al. 2024

Preprint

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PURPOSE: To report a deep learning neural network on anterior segment optical coherence tomography (AS-OCT) for automated detection of different keratorefractive laser surgeries, including Laser In-Situ Keratomileusis with femtosecond microkeratome (Femto-LASIK), LASIK with mechanical microkeratome, photorefractive keratectomy (PRK), keratorefractive lenticule extraction (KLEx), and non-operated eyes, while also distinguishing the targeted ametropias, such as myopic and hyperopic treatments, within these procedures. DESIGN: Cross-sectional retrospective study. METHODS: A total of 14,948 eye scans from 2,278 eyes of 1,166 subjects were used to develop a deep learning neural network algorithm with an 80/10/10 patient distribution for training, validation, and testing phases, respectively. The algorithm was evaluated for its accuracy, F1-scores, area under precision-recall curve (AUPRC), and area under receiver operating characteristic curve (AUROC). RESULTS: On the test dataset, the neural network was able to detect the different surgical classes with an accuracy of 96%, a weighted-average F1-score of 96% and a macro-average F1-score of 96%. The neural network was further able to detect hyperopic and myopic subclasses within each surgical class, with an accuracy of 90%, weighted-average F1 score of 90%, and macro-average F1-score of 83%. CONCLUSIONS: Determining a patient's keratorefractive laser history is vital for customizing treatments, performing precise intraocular lens (IOL) calculations, and enhancing ectasia risk assessments, especially when electronic health records are incomplete or unavailable. Neural networks can be used to accurately classify keratorefractive laser history from AS-OCT scans, a step in transforming the AS-OCT from a diagnostic to a screening tool in the refractive clinic.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Detection of Keratorefractive Laser Surgeries on Optical Coherence Tomography using Deep Learning

Assaf,

Yazbeck,

Reinstein

et al. 2024

Preprint

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“…Nevertheless, given the increasing number of ICL surgeries performed worldwide, evaluation of the safety of this operation remains a high priority. One of the important indicators to evaluate postoperative safety is vault, which is the vertical distance from the posterior surface of the ICL to the anterior surface of the eye’s crystalline lens [ 4 , 5 ]. The success of ICL surgery depends on the appropriate postoperative vault, with 250–750 µm or 0.5–1.5 times the corneal thickness [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of Data-Level Innovation Data-Balancing Machine Learning Models for Predicting Optimal Implantable Collamer Lens Size and Postoperative Vault

Zhao,

Tang,

et al. 2023

Ophthalmol Ther

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Introduction There are only four sizes of implantable collamer lens (ICL) available for selection, which cannot completely fit all patients as a result of the discontinuity of ICL sizes. Sizing an optimal ICL and predicting postoperative vault are still unresolved problems. This study aimed to develop and validate innovative data-level data-balancing machine learning-based models for predicting ICL size and postoperative vault. Methods The patients were randomly assigned to training and internal validation sets in a 4:1 ratio. Feature selection was performed using analysis of variance (ANOVA) and Kruskal–Wallis feature importance methods. Traditional linear regression model and machine learning-based models were used. The accuracy of models was assessed using the area under the curve (AUC) and confusion matrix. Results A total of 564 patients (1127 eyes) were eligible for this study, consisting of 808 eyes in the training set, 202 eyes in the internal validation set, and 117 eyes in the external validation set. Compared with the traditional linear regression method, the machine learning model bagging tree showed the best performance for ICL size selection, with an accuracy of 84.5% (95% confidence interval (CI) 83.2–85.8%), and the AUC ranged from 0.88 to 0.99; the prediction accuracy of 12.1 mm and 13.7 mm ICL sizes was improved by 49% and 59%, respectively. The bagging tree model achieved the best accuracy [90.2%, (95% CI 88.9–91.5%)] for predicting the postoperative vault, and the AUC ranged from 0.90 to 0.94. The prediction accuracies of internal and external validation dataset for ICL sizing were 82.2% (95% CI 81.1–83.3%) and 82.1% (95% CI 81.1–83.1%), respectively. Conclusions The innovative data-level data balancing-based machine learning model can be used to predict ICL size and postoperative vault more accurately, which can assist surgeons in choosing optimal ICL size, thus reducing risks of postoperative complications and secondary surgery. Supplementary Information The online version contains supplementary material available at 10.1007/s40123-023-00841-7.

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Enhancing the Automated Detection of Implantable Collamer Lens Vault Using Generative Adversarial Networks and Synthetic Data on Optical Coherence Tomography

Assaf,

Yazbeck,

Reinstein

et al. 2024

J Refract Surg

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Purpose: To investigate the efficacy of incorporating Generative Adversarial Network (GAN) and synthetic images in enhancing the performance of a convolutional neural network (CNN) for automated estimation of Implantable Collamer Lens (ICL) vault using anterior segment optical coherence tomography (AS-OCT). Methods: This study was a retrospective evaluation using synthetic data and real patient images in a deep learning framework. Synthetic ICL AS-OCT scans were generated using GANs and a secondary image editing algorithm, creating approximately 100,000 synthetic images. These were used alongside real patient scans to train a CNN for estimating ICL vault distance. The model's performance was evaluated using statistical metrics such as mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination ( R 2 ) for the estimation of ICL vault distance. Results: The study analyzed 4,557 AS-OCT B-scans from 138 eyes of 103 patients for training. An independent, retrospectively collected dataset of 2,454 AS-OCT images from 88 eyes of 56 patients, used prospectively for evaluation, served as the test set. When trained solely on real images, the CNN achieved a MAPE of 15.31%, MAE of 44.68 µm, and RMSE of 63.3 µm. However, with the inclusion of GAN-generated and algorithmically edited synthetic images, the performance significantly improved, achieving a MAPE of 8.09%, MAE of 24.83 µm, and RMSE of 32.26 µm. The R 2 value was +0.98, indicating a strong positive correlation between actual and predicted ICL vault distances ( P < .01). No statistically significant difference was observed between measured and predicted vault values ( P = .58). Conclusions: The integration of GAN-generated and edited synthetic images substantially enhanced ICL vault estimation, demonstrating the efficacy of GANs and synthetic data in enhancing OCT image analysis accuracy. This model not only shows potential for assisting postoperative ICL evaluations, but also for improving OCT automation when data paucity is an issue. [ J Refract Surg . 2024;40(4):e199–e207.]

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Deep Learning–Based Estimation of Implantable Collamer Lens Vault Using Optical Coherence Tomography

Cited by 3 publications

References 18 publications

Automated Detection of Keratorefractive Laser Surgeries on Optical Coherence Tomography using Deep Learning

Automated Detection of Keratorefractive Laser Surgeries on Optical Coherence Tomography using Deep Learning

Development and Validation of Data-Level Innovation Data-Balancing Machine Learning Models for Predicting Optimal Implantable Collamer Lens Size and Postoperative Vault

Enhancing the Automated Detection of Implantable Collamer Lens Vault Using Generative Adversarial Networks and Synthetic Data on Optical Coherence Tomography

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