Deep Machine-Learning Methodology for Space Exploration Medical Diagnosis, Year 2 (DML MD)

Completed TRL 4 (started at 3, targeting 4)

Description

There is a need for autonomous medical decision support to help crewmembers accurately scan and assess kidney health in space. This technology will aid Exploration medical autonomy, have wide application in medical facilities across the United States (US) and abroad and be further tested and validated in ground analogs (i.e. Baylor Global Initiatives (BGI) Smart Portable On Demand (POD), Human Exploration Research Analog (HERA), NASA Extreme Environment Mission Operations (NEEMO)) followed by the International Space Station (ISS), cis-lunar and lunar outpost in preparation for Mars.

NASA is maturing Artificial Intelligence (AI) technology in collaboration with industry and hospital labs to provide both procedural guidance and diagnostic support for US kidney studies. Our goal is to develop an effective Deep Machine Learning (DML) tool that assists astronauts in autonomously diagnosing kidneys on extended missions.

Benefits

Medical ultrasound is an important imaging modality that requires significant operator skill to acquire quality images, that then need expert interpretation for clinical decisions. This project will be important to Human Spaceflight Programs (i.e. Human Research Program (HRP), Advanced Exploration Systems (AES), Space Technology Mission Directorate (STMD)) as well as terrestrial health care. Spiral development approach will be used to mature the Deep Machine Learning (DML) Medical Diagnostics software application for generic use with clinical ultrasound devices. Prototype demonstration system will be developed that clearly illustrates the concepts and the value proposition to all the NASA programs in a lab environment. Validation testing will be performed in terrestrial analog environments including (Baylor Global Initiatives (BGI) Smart Portable On Demand (POD), Human Exploration Research Analog (HERA), NASA Extreme Environment Mission Operations (NEEMO)) followed by a DML software app demonstration on the International Space Station (ISS).

Details

Technology area	Human Health, Life Support, and Habitation Systems > Human Health and Performance > Medical Diagnosis and Prognosis
Program	Center Innovation Fund: JSC CIF (JSC CIF)
Lead organization	Johnson Space Center, Houston, TX
Start date	2020-10-01
End date	2021-09-30

Project contacts

Listed on TechPort itself — the most direct way to ask about this specific project.

James P Locke — james.p.locke@nasa.gov

How to get involved

This is early/mid-stage (TRL 4) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.