Deep learning models now match the accuracy of eye doctors in diagnosing eye diseases. But these models work like black boxes. This lack of openness is a big problem for use in clinics. Clinics need clear reasons for decisions. We introduce a new model called the Temporal-Multimodal Concept Bottleneck Model, or TM-CBM. This model fits glaucoma progression prediction. We built it using data from a large group of 87,342 patients. These patients had suspected or confirmed glaucoma. The data came from the BioArc system across many clinics in the country. Our model differs from usual end-to-end systems. It first predicts middle steps that doctors understand. Examples include trends in eye pressure, the cup-to-disc ratio, and defects in the retinal nerve fiber layer. Then it gives the final diagnosis. The model uses a shared goal for training. This goal mixes data over time on eye pressure, scans from optical coherence tomography, and basic patient facts. It reaches state-of-the-art accuracy. At the same time, it lets doctors step in during use. We show how doctors can fix the model's middle predictions. They make changes by hand. This fixes wrong final answers. In this way, the model joins the sharp eye of data methods with the skill of human doctors.
