Adaptive Temporal Mixture of Experts for Predicting Stiffness Metrics From the Ocular Response Analyzer and Identifying Keratoconus.

purposeTo develop a machine learning-based modeling approach for extracting elastic stiffness estimates from Ocular Response Analyzer (ORA) waveforms.

designProspective observational cohort study with model training and testing using a development dataset and validation with an independent validation dataset.

methodsParticipants were prospectively enrolled into 6 cohorts for the development dataset, including control subjects, individuals diagnosed with keratoconus, diabetes mellitus with retinopathy and without retinopathy, primary open-angle glaucoma, and ocular hypertension. An independent validation dataset comprised data for two cohorts, including healthy participants and individuals diagnosed with keratoconus. Intraocular pressure and biomechanical data were collected using the ORA and Corvis ST devices for the development dataset. An Adaptive Temporal Mixture of Experts (AT-MoE) model, incorporating long short-term memory (LSTM) networks with dynamic expert selection, was trained using ORA waveform parameters and raw signals to predict stiffness parameters (SP) from Corvis ST, including SP-A1 representing corneal stiffness, SP-HC representing scleral stiffness, and SSI representing deformation stiffness. Predicted ORA stiffness estimates were independently validated with a separate dataset of keratoconus and healthy eyes. The ORA waveform parameters, as well as applanation and pressure signals, were used as input to the machine learning models to classify eyes with keratoconus versus healthy eyes, which was evaluated with the area under the receiver operating characteristic (AUROC) curves. The primary outcome measures were the predicted ORA stiffness estimates, including corneal stiffness estimate, scleral stiffness estimate, and deformation stiffness estimate.

resultsThe AT-MoE model significantly outperformed the baseline LSTM in predicting ocular stiffness estimates using ORA input data with reduction of prediction error. The AT-MoE model also resulted in improvement of keratoconus detection performance in the independent validation dataset with AUROC=0.9512, which is similar to performance of tomographic detection approaches.

conclusionsThe AT-MoE model provides a novel and accurate framework for deriving elastic stiffness estimates from ORA waveforms, and offers improved diagnostic capability for keratoconus compared to traditional machine learning models. This method has the potential to expand the utility of the ORA device for biomechanical assessment in clinical settings.