Spinal MRI Segmentation AI

The problem

Medical imaging analysis for spinal conditions requires a specialist to manually review and annotate MRI and X-ray scans — a process that is time-consuming, expensive, and dependent on specialist availability that is often constrained.

The client needed a proprietary AI segmentation model that could identify spinal structures, flag anomalies, and produce annotated outputs with clinical-grade accuracy — fast enough to be practically useful in a diagnostic workflow.

What we built

A computer vision solution based on ML segmentation models trained on X-ray and MRI scan datasets. The system identifies spinal structures, segments regions of interest, and outputs annotated results for clinical review.

• ML model trained on curated X-ray and MRI datasets for spinal anatomy recognition
• AI segmentation identifying vertebrae, discs, and structural anomalies
• Annotation output integrated into existing diagnostic review workflow
• Cost-effective architecture designed for scalable per-scan processing
• Error rate validated below 10% across the test dataset

The outcome

Error margin below 10% validated across the full test dataset — a threshold that makes the model clinically meaningful rather than just technically interesting.

Diagnostic review time cut by 3× by automating the initial segmentation and annotation step, allowing specialists to focus on interpretation rather than manual marking.

Tech stack

Amit Nigam

CTO & Co-founder · Jointworks Solutions

Engineering and architecture. Owns the technical bar across every engagement that ships.