Hi, @lilmisscupcake
I strongly agree with your input. Having a well-structured output schema is essential for defining a standardized ground truth.
Regarding the explainability of the decision-support system, we should also determine how to report the evidence and inputs that each system relied on when generating its output.
It would be great if you could explore possible solutions for each pathway, including the rule-based, Bayesian, LLM/RAG, and hybrid approaches.
Hi all @AIMLawg ,
We’ll see you in next 15 minutes ,
Link Zoom: Launch Meeting - Zoom
It was a great meeting. Thank you, everyone, for the insightful discussion. I’m looking forward to contributing to the project and collaborating with the team.
Hey Sir, sorry for the inconvenience as I was unable to speak out during the meeting today due to some technical issues but I messaged at the chat regarding joining the subgroup project. I want to join your project and learn from you.
Please look into this.
Manat Raina
Date: 31 July 2026
This session continued the previous roadmap, with the primary focus on product development and software architecture.
The overall pathway will be:
Research → Validation → Development → Product
Publications will be a by-product of this development journey.
The main goal is not limited to SANS. The broader vision is an AI-based ophthalmic triage and predictive decision-support system for detection, risk prediction, and clinical recommendations. SANS will serve as one clinical scenario for testing and validating the framework.
The initial phase will focus on creating realistic simulated clinical scenarios.
Approach:
Literature review to identify:
Relevant clinical variables
Measurement methods
Normal ranges and abnormal thresholds
Develop 4–5 astronaut baseline profiles.
Generate time-point-based clinical scenarios representing disease progression or clinical events.
This step is feasible and provides a foundation for system development and validation.
A group of 4–5 ophthalmology experts will be involved to:
Define and verify clinical scenarios
Provide expert labels and ground truth
Validate system outputs and compare performance against clinical judgment
Suggested workflow:
2 experts: scenario labeling and ground truth generation
2–3 experts: validation and performance assessment
Clinical experts will work closely with programmers and data scientists to ensure simulations are clinically meaningful.
Clinical Lead: Alireza + invited ophthalmologists
Data Science & Engineering Team Leads: Rob Reynolds, Elisabeth
Computational Team: Soujanya, Suryanash
The goal is to generate synthetic clinical datasets based on:
Published literature
Expert-defined variables
Physiological ranges
Clinical relationships
Workflow:
Clinical experts define:
Variables
Measurement approaches
Expected ranges
Computational team generates simulated populations and longitudinal clinical data.
Lead: Rob Reynolds, with support from Elisabeth, Soujanya, and Suryanash
The system will include multiple intelligence approaches for different objectives:
Rule-based CDSS
Bayesian CDSS using Directed Acyclic Graphs (DAGs)
LLM/RAG-based CDSS
Hybrid approaches combining:
Clinical rules
Bayesian inference
Evidence retrieval
Expert knowledge
Each approach will be evaluated using a predefined framework including:
Accuracy
Reliability
Interpretability
False alarm rate
Clinical usefulness
Computational efficiency
Speed and feasibility for edge-computing environments
Clinical and computational teams will collaboratively design the evaluation framework based on literature and clinical requirements.
Literature review for:
Clinical variables
Scenario definitions
Evaluation metrics
AI methodology selection
Establish ophthalmology and clinical expert team.
Create formal GitHub repository under AWG/OSDR for:
Dataset development
Scenario generation
Documentation
Begin clinical scenario and synthetic dataset creation.
Hello! Is it still able to join this subgroup?
Hi ,
Yes of course ,
we are at beginning of building something very cool,
send your email in messages for me, I’ll put you in email list 
Hi @AliReza-H,
I am new to this subgroup. I am willing to join this project and contribute. My email ID: swathigadevic@vt.edu
Thank you. Looking forward!
Hi @Swathiga ,
That’s great , i will add you to email list
See you this Friday !
Hi everyone,
Thank you all for the great discussion and contributions.
The main goal of the CDSS is to detect problems, suggest next steps, and support clinical decision-making, ultimately contributing to greater mission independence and medical autonomy during long-duration spaceflight.
We also suggested Systems Medicine for Human Spaceflight as an important reference for the group.
A major limitation of many current spaceflight datasets, including several OSDR datasets, is that they are mainly focused on multi-omics and research questions rather than practical longitudinal clinical scenarios. Therefore, we need to build a dedicated synthetic astronaut clinical dataset for development and evaluation of the CDSS.
The first major task will be to create realistic longitudinal astronaut profiles.
Each astronaut will have baseline characteristics such as:
Age and sex
Medical history
Anatomy and physiology
Ophthalmic measurements
OCT variables such as RNFL and choroidal thickness
Refraction
Other relevant clinical and mission-related variables
Each simulated astronaut will then be followed across multiple mission timepoints, approximately:
Preflight → Launch → In-flight → Post-flight
For example:
L-30 → L → L+30 → L+90 → L+120
The exact number and timing of measurements should be extracted from the literature. A possible structure may include approximately three preflight, three in-flight, and three post-flight assessments.
For every astronaut, different longitudinal clinical scenarios can then be generated, including:
Normal/stable state
Progressive changes
Persistent/steady abnormalities
Missing modality
Poor-quality data
Incomplete information
Other clinically relevant scenarios
This will create many combinations of astronauts, timepoints, and clinical states for testing the CDSS.
The simulation should be transparent, reproducible, and evidence-based.
Literature review → Structured spreadsheet → LLM-assisted extraction → Paper-by-paper verification → Simulation → Computational validation → Clinical expert validation
From the literature, we will extract variables such as:
Clinical and physiological variables
Ophthalmic and OCT variables
Demographics
Health conditions
Measurement methods
Normal and abnormal ranges
Longitudinal changes
Relationships between variables
These will first be recorded in a shared spreadsheet.
LLMs such as ChatGPT or Claude can help extract and organize the information, but every extracted value should remain linked to its source and then be rechecked against the original paper. This is important to prevent unsupported or hallucinated values and make the AI-assisted process scientifically reproducible.
After defining variables and ranges, we can compare different methods of patient simulation rather than relying only on simple random generation.
Clinical experts, particularly ophthalmologists for the SANS scenarios, will then assess whether the generated longitudinal cases are realistic and clinically meaningful.
We should remain open to using real and analogue datasets.
If we obtain a well-structured clinical dataset, even if incomplete, an important question will be how to combine it with our simulation framework.
Examples include bed-rest studies and other terrestrial spaceflight analogues.
The goal is not to replace real data with simulation, but to use simulation to extend existing datasets, generate missing clinical trajectories, and create rare or difficult-to-observe scenarios.
The initial scientific question is:
How safely and accurately can a CDSS detect clinical changes and suggest the appropriate next steps during spaceflight?
SANS can be the first focused use case.
Different CDSS approaches can then be compared:
Rule-based systems
LLM-based systems
Hybrid approaches
Other probabilistic or causal methods as the project develops
The same simulated scenarios should be presented to each system, and their outputs compared against expert-defined reference standards using a structured scoring framework.
The longer-term pathway is:
Astronaut simulation → CDSS validation → Software development → Deployment-oriented architecture
The software should eventually consider real mission constraints such as:
Edge computing
Fast response
Limited connectivity
Computing resources
Appropriate programming architecture
Mission independence
The first broader product can become an Eye Triage CDSS, with SANS as one important scenario. The framework could later expand toward other human-system risks such as thrombosis, embolism, renal stones, radiation-related problems, and other classes defined within NASA’s Human System Risk framework.
A shared spreadsheet should be available to interested members so that literature extraction and dataset development can happen collaboratively.
Supervision / Advising: @ElisabethAslinger, @RobertReynolds
Volunteer: Soujanya Pittala
Collect all relevant papers in one shared folder.
Prepare the structured variable-extraction spreadsheet.
Begin evidence-based extraction of variables, ranges, timepoints, and clinical relationships.
Explore computational methods for validating synthetic patient/astronaut data.
Establish an official CDSS repository within the AWG GitHub for code, data structures, documentation, and version tracking.