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An X-ray of a knee with osteoarthritis looks like a road map where the roads have narrowed and bumps have grown along the shoulders. Doctors look at this map and assign a grade from zero to four — how worn out the joint is. The problem is that two experienced specialists often see the map differently: one gives a three, the other a four. And that changes the treatment: either exercise and pills, or injections right into the joint, or surgery.

Then these measurements are combined into a conclusion that makes sense to a clinician. First, the program finds the bone contours in the image and calculates the gap width to fractions of a millimeter, like a surveyor measuring the distance between riverbanks. Then it evaluates each bone spur individually, like a gardener inspecting each bush. Finally, it checks for areas where the bone has become too dense, like a packed-down path. All these signs are gathered into a vector of fifty numbers — a sort of passport for the joint.

On 8260 images from the US OAI study database, the program showed high accuracy: in 84% of cases, its grade matched the opinion of experienced radiologists. The main sign turned out to be joint space narrowing: it has the strongest influence on the final grade. Bone spurs add important details, while bone sclerosis plays a minor role. When the program was deprived of gap width data, it could no longer distinguish the most severe stages of osteoarthritis, like a cartographer without road measurements who cannot tell a dirt track from a highway.

This approach restores trust in artificial intelligence in medicine: doctors see not a black box but a clear chain of measurements. A patient can ask, "Why did I get stage three?" and the doctor can show: the gap narrowed by two millimeters, the spur grew by five millimeters. This changes the conversation with the patient and makes treatment decisions more grounded.