
Dr. Riel Castro-Zunti PhD
Electrical and Computer EngineeringPostdoctoral Fellow in the Ko Lab. His research applies deep learning to medical image analysis and to grassland genomics for greenhouse gas mitigation, from automated rib-fracture diagnosis and the early detection of disease from radiological images to estimating soil carbon across Western Canada.
Research
Riel applies deep learning to medical image analysis, working with radiologists on systems that catch disease earlier and take repetitive reading work off clinicians.
Current project
Grassland Genomics for Greenhouse Gas Mitigation (GG4GHG)
Riel has both a research and a supervisory role in Grassland Genomics for Greenhouse Gas Mitigation, sponsored by Genome Canada. This large-scale machine learning project aims to estimate the amount of carbon stored in soil across Western Canada based on the presence and abundance of certain taxa residing therein, along with other factors such as soil composition and climate variables.
Canadian grasslands cover more than 16 million hectares and play an important role in carbon storage, biodiversity and greenhouse gas mitigation. Soil organic carbon is a key indicator of soil health and of how effectively those grasslands retain carbon.
- Combines microbial sequencing and environmental data with machine learning and feature selection
- Explores graph-based models to represent interactions among microbes, plants, soil and climate
Publications
Early diagnosis of ankylosing spondylitis
R. Castro-Zunti, E. H. Park, A. Satsangi, Y. Choi, G. Y. Jin, H. S. Chae, S.-B. Ko. Machine Intelligence Research, 2025; and Journal of Imaging Informatics in Medicine, 38(6), 3665-3683, 2025.
- A YOLOv5 detector locates the sacroiliac joints, then a classifier grades erosion
- 2,025 joints from 1,042 patient frames; 91% accuracy with 100% sensitivity on early-stage cases
- A parallel MRI system outperforms a radiologist with ten years' experience by 13% accuracy
RibFractureSys: rib tracking and fracture classification
R. Castro-Zunti, K. Li, A. Vardhan, Y. Choi, G. Y. Jin, S.-B. Ko. Computerized Medical Imaging and Graphics, 117, 102429, 2024.
- A U-Net segments the ribs and custom multi-object tracking follows each one through the CT stack
- Regions are classified as acute fracture, healed fracture or normal
- Validated over 1,000 fracture and 1,000 control scans
Reducing false positives in gout diagnosis
R. Castro-Zunti, Y. Choi, Y. Choi, H. S. Chae, G. Y. Jin, E. H. Park, S.-B. Ko. Journal of Imaging Informatics in Medicine, 2025.
- A machine vision pipeline identifies and classifies crystal tophi in dual-energy CT
Also
- Tumour-feeding vessel visualisation in cone-beam CT hepatic arteriography (Clinical Radiology, 2026)
- HAZMAT placard detection at highway check stops using synthetic images and YOLOv5-Small (IEEE ISCAS, 2025)