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In Situ Mineralogical Analysis of Planetary Materials Using X-Ray Diffraction and X-Ray Fluorescence

Remote observations of Mars have led scientists to believe that its early climate was similar to that of the early Earth, having had abundant liquid water and a dense atmosphere. One of the most fascinating questions of recent times is whether simple bacterial life developed on Mars (as it did on the Earth) during this early element period. Analyses of SNC meteorites have broadened considerably our knowledge of the chemistry of certain types of Martian rocks, underscoring the tantalizing possibility of early hydrothermal systems and even of ancient bacterial life. Detailed analyses of SNC meteorites in Terrestrial laboratories utilize the most sophisticated organic, isotopic and microscopic techniques in existence. Indeed; it is unlikely that the key biogenic indicators used in McKay et al (ibid) could be identified by a remote instrument on the surface of Mars. As a result, it is probable that any robotic search for evidence of an ancient Martian biosphere will have as its focus the identification of key minerals in likely host rocks rather than the direct detection of organic or isotopic biomarkers. Even on a sample return mission, mineralogical screening will be utilized to choose the most likely candidate rocks. X-ray diffraction (XRD) is the only technique that can provide a direct determination of the crystal structures of the phases present within a sample. When many different crystalline phases are present, quantitative analysis is better constrained if used in conjunction with a determination of elemental composition, obtainable by X-ray fluorescence (XRF) using the same X-ray source as for XRD. For planetary surface analysis, a remote instrument combining XRD and XRF could be used for mineralogical characterization of both soils and rocks. We are designing a remote XRD/XRF instrument with this objective in mind. The instrument concept pays specific attention to constraints in sample preparation, weight, volume, power, etc. Based on the geometry of a pinhole camera (transmission geometry, flat two-dimensional detector perpendicular to the direct beam), the instrument (which we call CHEMIN, for Chemistry and Mineralogy) uses an X-ray sensitive CCD detector which will allow concurrent positional and energy-dispersive analysis of collected photons. Thus XRF (energy) and XRD (geometry) analysis of transmitted X-rays will be performed at the same time. Tests performed with single minerals and simple mixtures give promising results. Refinements of the prototype promise interpretable results on complex samples.

Sarrazin, P.↗

Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions

BACKGROUND The medical capabilities necessary for long-duration exploration missions (LDEMs) will differ tremendously from those currently available to crew medical officers (CMOs) on the International Space Station (ISS). Ground support will be more challenging due to distance-related communication delays and data transmission, and resource utilization must be optimized given limited ability for resupply. Clinical decision support systems (CDSSs) can help mitigate these limitations. The recent launch of generative artificial intelligence (AI) tools based upon large language models (LLM) support the creation of a smart assistant for onboard triage, diagnosis, and guided treatment of medical conditions during these missions. The Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) tool can help predict which clinical problems and outcomes are likely to occur for a design reference mission (DRM) and assist Medical Operations and systems engineering teams in creating a medical system that may optimally mitigate the predicted risks. The purpose of this study was to identify AI tools currently available or in development for the assistive diagnosis and care of medical conditions predicted for an extended duration Lunar mission. METHODS The 119 medical conditions currently built into the IMPACT suite were categorized into systems, and these diagnoses were used as keywords for our literature search. Using PubMed and Google Scholar, we performed a literature survey of AI tools applicable to these conditions. Article inclusion criteria included publication between the years 2017-2023, as the sentinel paper discussing the “selective attention” driving ChatGPT and other generative transformer models was published in June 2017. Where applicable, we reviewed only the top 1000 research articles (based on relevance) for each of the keywords/phrases. AI tools whose training sets were exclusive to a pediatric patient population were excluded. We also excluded any medical diagnostic tools (such as CT, MRI, mass spectrometry) or procedures (such as endoscopy, surgery) that are unlikely to be available during LDEMs due to mass and volume constraints, CMO knowledge, skills, and abilities, and/or inherent procedural risks. RESULTS Our survey highlighted several AI-driven tools for the triage, diagnosis, and management of those medical conditions highlighted by IMPACT. Selected publications for each medical condition were then screened for inclusion within ten systems-based categories including: general diagnostic tools (25), tools to diagnose and manage respiratory (40), dermatologic (34), neurologic (28), auditory and vestibular (30), ophthalmic (34), musculoskeletal (104), infection-associated (92), and gynecologic (19) conditions, as well as tools that could be deployed in the setting of trauma and emergency (34). CONCLUSIONS Numerous AI-driven tools were highlighted within this literature survey, ranging from chatbot assistants that triage knee pain to vision transformer models for diagnosis of ophthalmic conditions using ocular surface images captured with a mobile phone. Remaining challenges include optimizing connectivity and integration of existing and developing systems into the vehicles or habitats. Notably, findings from this survey could help guide the initial design of an all-encompassing, onboard medical AI assistant for use during future LDEMs.

R A Lacinski↗