PREDICTING VISUAL OUTCOME AFTER SURGERY IN PATIENTS WITH IDIOPATHIC EPIRETINAL MEMBRANE USING A NOVEL CONVOLUTIONAL NEURAL NETWORK.

PURPOSE: To develop a deep convolutional neural network that enables the prediction of postoperative visual outcomes after epiretinal membrane surgery based on preoperative optical coherence tomography images and clinical parameters to refine surgical decision making. METHODS: A total of 529 patients with idiopathic epiretinal membrane who underwent standard vitrectomy with epiretinal membrane peeling surgery by two surgeons between January 1, 2014, and June 1, 2020, were enrolled. The newly developed Heterogeneous Data Fusion Net was introduced to predict postoperative visual acuity outcomes (improvement ≥2 lines in Snellen chart) 12 months after surgery based on preoperative cross-sectional optical coherence tomography images and clinical factors, including age, sex, and preoperative visual acuity. The predictive accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the convolutional neural network model were evaluated. RESULTS: The developed model demonstrated an overall accuracy for visual outcome prediction of 88.68% (95% CI, 79.0%-95.7%) with an area under the receiver operating characteristic curve of 97.8% (95% CI, 86.8%-98.0%), sensitivity of 87.0% (95% CI, 67.9%-95.5%), specificity of 92.9% (95% CI, 77.4%-98.0%), precision of 0.909, recall of 0.870, and F1 score of 0.889. The heatmaps identified the critical area for prediction as the ellipsoid zone of photoreceptors and the superficial retina, which was subjected to tangential traction of the proliferative membrane. CONCLUSION: The novel Heterogeneous Data Fusion Net demonstrated high accuracy in the automated prediction of visual outcomes after weighing and leveraging multiple clinical parameters, including optical coherence tomography images. This approach may be helpful in establishing personalized therapeutic strategies for epiretinal membrane management.

Prediction of treatment outcome in neovascular age-related macular degeneration using a novel convolutional neural network.

While prognosis and risk of progression are crucial in developing precise therapeutic strategy in neovascular age-related macular degeneration (nAMD), limited predictive tools are available. We proposed a novel deep convolutional neural network that enables feature extraction through image and non-image data integration to seize imperative information and achieve highly accurate outcome prediction. The Heterogeneous Data Fusion Net (HDF-Net) was designed to predict visual acuity (VA) outcome (improvement ≥ 2 line or not) at 12th months after anti-VEGF treatment. A set of pre-treatment optical coherence tomography (OCT) image and non-image demographic features were employed as input data and the corresponding 12th-month post-treatment VA as the target data to train, validate, and test the HDF-Net. This newly designed HDF-Net demonstrated an AUC of 0.989 (95% CI 0.970-0.999), accuracy of 0.936 [95% confidence interval (CI) 0.889-0.964], sensitivity of 0.933 (95% CI 0.841-0.974), and specificity of 0.938 (95% CI 0.877-0.969). By simulating the clinical decision process with mixed pre-treatment information from raw OCT images and numeric data, HDF-Net demonstrated promising performance in predicting individualized treatment outcome. The results highlight the potential of deep learning to simultaneously process a broad range of clinical data to weigh and leverage the complete information of the patient. This novel approach is an important step toward real-world personalized therapeutic strategy for typical nAMD.