The eye is inflamed, the cornea clouding. The doctor needs to quickly determine the culprit — bacteria or fungus. The treatment depends on it. A mistake can cost sight. But the classic lab answer takes days, and in remote areas it may never arrive.

Researchers from the US and India propose a different approach: three images from a slit lamp — a device found in nearly every eye clinic. One shot in blue light, another in diffuse light, and a third in ordinary white light. These three "views" of the same opacity carry different information, like three interpreters listening to the same speech but catching different nuances. The model, named TPMKA, learns to reconcile these nuances. It first trains on pairs of images from the same patient to understand what they share and what is specific to a particular lighting. Then it fine-tunes for its task and finally merges all three data streams into a single decision.

The result: 86% accuracy. It may seem modest, but in a real clinic it's serious support. Crucially, the authors tested the model honestly: when evaluated on images from another country and another hospital, accuracy dropped. That's expected: cameras, settings, even the patient's skin tone affect the image. But even after rigorous cross-validation with data balancing, TPMKA remained the best among all tested approaches. Three images, three seconds, and the doctor gets a hint on how to start treatment while the lab is still preparing the culture.