Rahul Kumar , Ethan Waisberg , Joshua Ong , Phani Paladugu , Kyle Sporn , Karsten Chima , Dylan Amiri , Nasif Zaman , Alireza Tavakkoli
{"title":"结合下体负压和先进的大语言模型人工智能的航天健康精密监测","authors":"Rahul Kumar , Ethan Waisberg , Joshua Ong , Phani Paladugu , Kyle Sporn , Karsten Chima , Dylan Amiri , Nasif Zaman , Alireza Tavakkoli","doi":"10.1016/j.lssr.2025.05.010","DOIUrl":null,"url":null,"abstract":"<div><div>Long-term exposure to microgravity influences musculoskeletal health and enhances the likelihood of sustaining orthopedic injuries while on a microgravity mission and upon return to Earth. Although countermeasures are being investigated to alleviate some risks of injury, such as resistive (or weight) exercise and Lower Body Negative Pressure (LBNP), evidence is accumulating that current paradigms do not ensure the safety or health of astronauts because of a lack of in-flight diagnostic methods, in which load/diagnostic metrics can be assessed over time. Here, we suggest the integration of a new vision-language large language model (DeepSeek-VL) as a potential autonomous diagnostic agent for monitoring musculoskeletal health in a microgravity environment. DeepSeek-VL will autonomously analyze radiographic data and biomechanical data streamed from a LBNP device. Determinations will be made based on lost or compromised density in bone, lost joint-centered stability, or ineffective loading patterns - providing personalized and specific feedback regarding musculoskeletal health with the astronaut as the primary user. Unlike conventional reporting approaches that rely on cross-institutional analysis by household experts, DeepSeek-VL allows for real-time, and autonomous interpretation of musculoskeletal imaging metrics (and physiological metrics) for on-time personalized countermeasure development. Here, we review architectural adaptations including microgravity specific samplings of data, training protocols and implications of deployment in the ISS. We anticipate DeepSeek's timely development of flight-ready diagnostic reporting will facilitate in-flight/systematic monitoring of musculoskeletal health and safety, especially for astronauts undergoing load management training (e.g., LBNP) and ensure effectiveness of countermeasures, their outputs. We will address methods to circumvent limitations and barriers to risk, and establish the importance of a federated, adaptive, and resilient AI-based platform to mitigate risk for astronaut musculoskeletal health during extended missions. Finally, we address some considerations for terrestrial model and a healthcare authority within a current context of growing importance for effective orthopedic healthcare.</div></div>","PeriodicalId":18029,"journal":{"name":"Life Sciences in Space Research","volume":"47 ","pages":"Pages 57-60"},"PeriodicalIF":2.9000,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Precision health monitoring in spaceflight with integration of lower body negative pressure and advanced large language model artificial intelligence\",\"authors\":\"Rahul Kumar , Ethan Waisberg , Joshua Ong , Phani Paladugu , Kyle Sporn , Karsten Chima , Dylan Amiri , Nasif Zaman , Alireza Tavakkoli\",\"doi\":\"10.1016/j.lssr.2025.05.010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Long-term exposure to microgravity influences musculoskeletal health and enhances the likelihood of sustaining orthopedic injuries while on a microgravity mission and upon return to Earth. Although countermeasures are being investigated to alleviate some risks of injury, such as resistive (or weight) exercise and Lower Body Negative Pressure (LBNP), evidence is accumulating that current paradigms do not ensure the safety or health of astronauts because of a lack of in-flight diagnostic methods, in which load/diagnostic metrics can be assessed over time. Here, we suggest the integration of a new vision-language large language model (DeepSeek-VL) as a potential autonomous diagnostic agent for monitoring musculoskeletal health in a microgravity environment. DeepSeek-VL will autonomously analyze radiographic data and biomechanical data streamed from a LBNP device. Determinations will be made based on lost or compromised density in bone, lost joint-centered stability, or ineffective loading patterns - providing personalized and specific feedback regarding musculoskeletal health with the astronaut as the primary user. Unlike conventional reporting approaches that rely on cross-institutional analysis by household experts, DeepSeek-VL allows for real-time, and autonomous interpretation of musculoskeletal imaging metrics (and physiological metrics) for on-time personalized countermeasure development. Here, we review architectural adaptations including microgravity specific samplings of data, training protocols and implications of deployment in the ISS. We anticipate DeepSeek's timely development of flight-ready diagnostic reporting will facilitate in-flight/systematic monitoring of musculoskeletal health and safety, especially for astronauts undergoing load management training (e.g., LBNP) and ensure effectiveness of countermeasures, their outputs. We will address methods to circumvent limitations and barriers to risk, and establish the importance of a federated, adaptive, and resilient AI-based platform to mitigate risk for astronaut musculoskeletal health during extended missions. Finally, we address some considerations for terrestrial model and a healthcare authority within a current context of growing importance for effective orthopedic healthcare.</div></div>\",\"PeriodicalId\":18029,\"journal\":{\"name\":\"Life Sciences in Space Research\",\"volume\":\"47 \",\"pages\":\"Pages 57-60\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-05-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Life Sciences in Space Research\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214552425000690\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ASTRONOMY & ASTROPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Life Sciences in Space Research","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214552425000690","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
Precision health monitoring in spaceflight with integration of lower body negative pressure and advanced large language model artificial intelligence
Long-term exposure to microgravity influences musculoskeletal health and enhances the likelihood of sustaining orthopedic injuries while on a microgravity mission and upon return to Earth. Although countermeasures are being investigated to alleviate some risks of injury, such as resistive (or weight) exercise and Lower Body Negative Pressure (LBNP), evidence is accumulating that current paradigms do not ensure the safety or health of astronauts because of a lack of in-flight diagnostic methods, in which load/diagnostic metrics can be assessed over time. Here, we suggest the integration of a new vision-language large language model (DeepSeek-VL) as a potential autonomous diagnostic agent for monitoring musculoskeletal health in a microgravity environment. DeepSeek-VL will autonomously analyze radiographic data and biomechanical data streamed from a LBNP device. Determinations will be made based on lost or compromised density in bone, lost joint-centered stability, or ineffective loading patterns - providing personalized and specific feedback regarding musculoskeletal health with the astronaut as the primary user. Unlike conventional reporting approaches that rely on cross-institutional analysis by household experts, DeepSeek-VL allows for real-time, and autonomous interpretation of musculoskeletal imaging metrics (and physiological metrics) for on-time personalized countermeasure development. Here, we review architectural adaptations including microgravity specific samplings of data, training protocols and implications of deployment in the ISS. We anticipate DeepSeek's timely development of flight-ready diagnostic reporting will facilitate in-flight/systematic monitoring of musculoskeletal health and safety, especially for astronauts undergoing load management training (e.g., LBNP) and ensure effectiveness of countermeasures, their outputs. We will address methods to circumvent limitations and barriers to risk, and establish the importance of a federated, adaptive, and resilient AI-based platform to mitigate risk for astronaut musculoskeletal health during extended missions. Finally, we address some considerations for terrestrial model and a healthcare authority within a current context of growing importance for effective orthopedic healthcare.
期刊介绍:
Life Sciences in Space Research publishes high quality original research and review articles in areas previously covered by the Life Sciences section of COSPAR''s other society journal Advances in Space Research.
Life Sciences in Space Research features an editorial team of top scientists in the space radiation field and guarantees a fast turnaround time from submission to editorial decision.