Radiology is drowning in data
A single modern CT study can contain hundreds to thousands of image slices. A multi-sequence MRI exam can contain even more. Imaging volumes have grown steadily for years, while the number of radiologists has not kept pace.
The result is longer reporting backlogs, heavier workloads and greater risk of fatigue. Small, early-stage findings — the ones that matter most for outcomes — are the easiest to miss when a specialist is reading their hundredth study of the day.
Machine vision changes the equation. An AI model reviews every voxel of every slice with the same consistent attention, at any hour, in seconds.
Teaching computers to see like specialists
Machine vision uses deep neural networks — convolutional neural networks and, more recently, vision transformers — to learn visual patterns directly from data. Instead of being programmed with rules, the model is trained on large sets of expertly annotated scans until it learns what disease looks like at every stage.
Dinmar Labs' models work in three dimensions, analyzing the full scan volume rather than isolated 2D images. This lets them understand shape, texture, density and spatial relationships the way an experienced radiologist does — and pick up subtle signals spread across multiple slices.
Because AI models can be retrained as new data arrives, performance continues to improve over time.
