Document Type
Article
Publication Date
3-28-2024
Abstract
Spaceflight associated neuro-ocular syndrome (SANS) is one of the largest physiologic barriers to spaceflight and requires evaluation and mitigation for future planetary missions. As the spaceflight environment is a clinically limited environment, the purpose of this research is to provide automated, early detection and prognosis of SANS with a machine learning model trained and validated on astronaut SANS optical coherence tomography (OCT) images. In this study, we present a lightweight convolutional neural network (CNN) incorporating an EfficientNet encoder for detecting SANS from OCT images titled "SANS-CNN." We used 6303 OCT B-scan images for training/validation (80%/20% split) and 945 for testing with a combination of terrestrial images and astronaut SANS images for both testing and validation. SANS-CNN was validated with SANS images labeled by NASA to evaluate accuracy, specificity, and sensitivity. To evaluate real-world outcomes, two state-of-the-art pre-trained architectures were also employed on this dataset. We use GRAD-CAM to visualize activation maps of intermediate layers to test the interpretability of SANS-CNN's prediction. SANS-CNN achieved 84.2% accuracy on the test set with an 85.6% specificity, 82.8% sensitivity, and 84.1% F1-score. Moreover, SANS-CNN outperforms two other state-of-the-art pre-trained architectures, ResNet50-v2 and MobileNet-v2, in accuracy by 21.4% and 13.1%, respectively. We also apply two class-activation map techniques to visualize critical SANS features perceived by the model. SANS-CNN represents a CNN model trained and validated with real astronaut OCT images, enabling fast and efficient prediction of SANS-like conditions for spaceflight missions beyond Earth's orbit in which clinical and computational resources are extremely limited.
Recommended Citation
Kamran, Sharif Amit; Hossain, Khondker Fariha; Ong, Joshua; Zaman, Nasif; Waisberg, Ethan; Paladugu, Phani; Lee, Andrew; and Tavakkoli, Alireza, "SANS-CNN: An Automated Machine Learning Technique for Spaceflight Associated Neuro-Ocular Syndrome With Astronaut Imaging Data" (2024). SKMC Student Presentations and Publications. Paper 8.
https://jdc.jefferson.edu/skmcstudentworks/8
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Language
English
Comments
This article is the author's final published version in npj Microgravity, Volume 10, Issue 1, 2024, Article number 40.
The published version is available at https://doi.org/10.1038/s41526-024-00364-w.
Copyright © The Author(s) 2024