Abstract
BackgroundThe purpose of this study was to implement and evaluate a deep learning (DL) approach for automatically detecting shallow anterior chamber depth (ACD) from two-dimensional (2D) overview anterior segment photographs.MethodsWe trained a DL model using a dataset of anterior segment photographs collected from Shanghai Aier Eye Hospital from June 2018 to December 2019. A Pentacam HR system was used to capture a 2D overview eye image and measure the ACD. Shallow ACD was defined as ACD less than 2.4 mm. The DL model was evaluated by a five-fold cross-validation test in a hold-out testing dataset. We also evaluated the DL model by testing it against two glaucoma specialists. The performance of the DL model was calculated by metrics, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).ResultsA total of 3753 photographs (1720 shallow AC and 2033 deep AC images) were assigned to the training dataset, and 1302 photographs (509 shallow AC and 793 deep AC images) were held out for two internal testing datasets. In detecting shallow ACD in the internal hold-out testing dataset, the DL model achieved an AUC of 0.86 (95% CI, 0.83–0.90) with 80% sensitivity and 79% specificity. In the same testing dataset, the DL model also achieved better performance than the two glaucoma specialists (accuracy of 80% vs. accuracy of 74 and 69%).ConclusionsWe proposed a high-performing DL model to automatically detect shallow ACD from overview anterior segment photographs. Our DL model has potential applications in detecting and monitoring shallow ACD in the real world.Trial registrationhttp://clinicaltrials.gov, NCT04340635, retrospectively registered on 29 March 2020.
Highlights
Anterior chamber depth (ACD) is an important biometry parameter for the diagnosis and therapy of ocular disease
anterior chamber (AC) depth measurements have several important applications, such as screening primary angle-closure glaucoma (PACG), calculating the power of intraocular lenses to be implanted after cataract extraction, and identifying the association with systemic parameters [1,2,3]
Development of the deep learning (DL) model To detect shallow or deep AC from anterior segment images, we proposed the use of transfer learning based on the pre-trained Inception-V3 (Google, Inc.) architecture [20]
Summary
Anterior chamber depth (ACD) is an important biometry parameter for the diagnosis and therapy of ocular disease. AC depth measurements have several important applications, such as screening primary angle-closure glaucoma (PACG), calculating the power of intraocular lenses to be implanted after cataract extraction, and identifying the association with systemic parameters [1,2,3]. The traditional methods for ACD measurement include slit-lamp biomicroscopy, IOLMaster, A-Scan ultrasound, and Pentacam [1, 6,7,8]. All these techniques are time consuming and require trained and experienced technicians. The purpose of this study was to implement and evaluate a deep learning (DL) approach for automatically detecting shallow anterior chamber depth (ACD) from two-dimensional (2D) overview anterior segment photographs
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