Artificial Intelligence Deep Learning Models to Predict Spaceflight Associated Neuro-Ocular Syndrome.

Purpose: To create deep learning artificial intelligence (AI) models for predicting the development of Spaceflight Associated Neuro-Ocular Syndrome (SANS) using optical coherence tomography (OCT) imaging of the optic nerve head.

Design: Retrospective analysis.

Methods: AI deep learning models were trained to predict SANS onset by using two OCT datasets: pre- and inflight OCT images acquired from astronauts (flight data) and pre- and in-bedrest images from research participants undergoing head-down tilt bedrest as an Earth-bound model of SANS (ground data). Both datasets were partitioned by participant into training and testing data. Resnet50-based models were trained using exclusively flight data, exclusively ground data, and a combination of both. All models were evaluated based on their ability to predict SANS using only pre-flight or pre-bedrest imaging in both datasets. Performance was assessed using receiver operating characteristic areas under the curve (AUC). Class activation maps (CAMs) were generated to identify impactful image regions.

Results: The model trained on flight data achieved an AUC (95% CI) of 0.82 (0.54-1.0) on flight data and 0.67 (0.51-0.83) on ground data. The ground-trained model achieved an AUC of 0.71 (0.50-0.91) on ground data and 0.76 (0.51-0.91) on flight data. The combined model achieved an AUC of 0.81 (0.53-0.95) and 0.72 (0.52-0.92) on flight and ground data, respectively. CAMs identified peripapillary regions of the nerve fiber layer, retinal pigmented epithelium, and anterior lamina surface as most important for predictions.

Conclusions: AI models can predict SANS based on pre-flight OCT imaging with moderate-to-high performance even in this data-limited setting. The performance of cross-trained models and similarity in CAMs suggests similarity between SANS-related changes in flight and ground datasets, proving further support that head-down tilt bedrest is a reasonable Earth-bound model for SANS. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.