AI-powered biodosimetry to rapidly triage radiation exposure
Official title: Deep Learning-Driven Biodosimetry for High-Throughput Radiation Triage via Chromosomal Aberration Detection
Funded at Georgia Institute of Technology · NIH-11388629 · Data updated 2026-11-12
A fast AI-based system uses chromosome images to quickly estimate radiation dose for large groups after a radiological incident.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Georgia Institute of Technology NIH-funded |
| Lab location | 1 site (Atlanta, United States) |
| Project start | August 1, 2025 |
| Project end | July 31, 2027 |
| Funding year | 2026 |
| Award (this year) | $157,400 |
| NIH project number | 5R21AI193884-02 |
| Project ID | NIH-11388629 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates are individuals who may be exposed to ionizing radiation in emergencies and could participate through sample donation or use of de-identified data to develop the system.
✕Less likely to help
Individuals not exposed to radiation or whose data do not contribute to model development may not directly benefit.
What this research studies
This project from Georgia Tech aims to create a high-throughput, AI-driven platform that detects chromosomal aberrations like dicentrics and rings from cytogenetic data to estimate radiation dose quickly. It plans to use YOLOv9, a state-of-the-art deep learning model, to identify relevant chromosomal features and then classify the dose across different radiation types and energies. By reducing processing time from days to hours or minutes, the platform could enable faster triage and medical decision-making after nuclear or radiological events. The work focuses on translating advanced computer vision into a practical tool for emergency responders and clinicians handling large populations. The research will address accuracy, speed, and robustness across dose ranges and radiation modalities.
Why it matters
Potential benefit: If successful, it could dramatically shorten the time needed to triage exposed individuals and guide timely treatment decisions.
How similar studies have gone: Previous biodosimetry efforts have shown promise with rapid AI-based detectors, but this project targets higher efficiency and broader anomaly detection with YOLOv9.
Where this research is happening
-
Georgia Institute of Technology
Atlanta, United States Active
Researchers
- Principal investigator: Dewji, Shaheen Azim — Georgia Institute of Technology
- Study coordinator: Dewji, Shaheen Azim
Related research
Other NIH-funded projects on the same topics.
-
Integrative histo-genomic analysis to guide cancer risk and targeted therapies with interpretable AI
Researchers are developing an advanced AI-powered approach to map genetic and cellular patterns across breast and cervical cancers to help tailor treatments.
-
Predicting how antimalarial drug resistance spreads with AI
A powerful AI model will be built to forecast how resistance to antimalarial drugs might spread in Africa and beyond, helping clinicians and public health teams stay ahead of outbreaks.
-
Self-supervised deep learning for biological sequences
A deep learning project uses self-supervised methods to better understand biological sequences and related data, with the goal of advancing biology and medicine.
-
Mobile phone imaging and AI to screen oral cancers in low-resource settings
A low-cost mobile phone device with artificial intelligence is being developed to help frontline health workers screen for oral cancer early in low-resource areas.
-
Linking Genetic Variants to Real-Life Health Effects with Smart AI
A new AI-powered approach will predict how genetic differences affect health across multiple levels to help doctors understand diseases and tailor care.
-
Integrative AI tools to map protein sequences to structures and functions
A scientist team will develop advanced AI methods to better connect protein sequences, their 3D shapes, and how they work, using large biological data from sequencing and structures.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.