{"doi":"10.1038/s41526-024-00364-w","title":"SANS-CNN: An automated machine learning technique for spaceflight associated neuro-ocular syndrome with astronaut imaging data","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.","journal":"npj Microgravity","year":2024,"id":432876,"datarank":0.40620753016533157,"base_score":2.70805020110221,"endowment":2.70805020110221,"self_citation_contribution":0.40620753016533157,"citation_network_contribution":0.0,"self_endowment_contribution":0.40620753016533157,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":14,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9573,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":914633,"name":"Khondker Fariha Hossain","orcid":"0009-0002-1641-2205","position":1,"is_corresponding":false},{"id":646616,"name":"Joshua Ong","orcid":"0000-0003-4860-827X","position":2,"is_corresponding":false},{"id":1053084,"name":"Nasif Zaman","orcid":"0000-0003-0120-0939","position":3,"is_corresponding":false},{"id":1218095,"name":"Ethan Waisberg","orcid":"0000-0001-8999-0212","position":4,"is_corresponding":false},{"id":1238195,"name":"Phani Paladugu","orcid":null,"position":5,"is_corresponding":false},{"id":646617,"name":"Andrew G. Lee","orcid":"0000-0002-2473-299X","position":6,"is_corresponding":false},{"id":400624,"name":"Alireza Tavakkoli","orcid":"0000-0001-9460-1269","position":7,"is_corresponding":false},{"id":400623,"name":"Sharif Amit Kamran","orcid":"0000-0002-1681-9438","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:59:39.937122Z","pmid":"38548790","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}