Clinicians increasingly rely on medical imaging to guide diagnosis and treatment, but machine learning systems used to analyze these images can be unreliable and difficult to interpret. Small, heterogeneous datasets limit confidence in these tools, particularly for neurological disorders such as Alzheimer’s disease, while models that overlook anatomical structure can produce fragmented or unstable results. To improve reliability and transparency, researchers have developed structure-aware AI methods that incorporate anatomical information into image analysis.

Developed at Umeå University’s (Umeå, Sweden) Department of Computing Science, the approach integrates prior knowledge about image structure into machine learning models. The methods draw on mathematical morphology to encourage learned patterns to form connected and anatomically meaningful regions. By aligning model behavior with known properties of medical images, the system is designed to improve clinical interpretability and trust.

The technology emphasizes spatial coherence. It accounts for the tendency of neighboring areas in magnetic resonance imaging (MRI) to represent the same tissue type. This reduces the impact of random variations that can destabilize predictions and yields outputs that correspond more closely to real anatomical boundaries.

One application explored is the classification of Alzheimer’s disease. The models identify connected regions that reflect disease-related structural changes, such as volume loss in brain areas critical for memory and cognition. Coherent regional predictions make it easier to relate model outputs to recognizable pathological patterns, which can increase confidence in assessments.

The work also introduces uncertainty-awareness into predictions. In addition to a diagnostic output, the system indicates how confident the model is in each part of its analysis. These confidence signals help clinicians distinguish robust findings from areas where further review or complementary testing may be warranted.

“I have also developed methods that not only provide a prediction but also indicate how confident the model is in its prediction. This allows clinicians to see which parts of the analysis are based on robust calculations and which are more uncertain,” said Disi Lin, a doctoral student at the Department of Computing Science, Umeå University.

“These methods can also show how certain the predictions are. This is important in health care because obtaining a result is not enough; clinicians also need to know how confident that result is,” added Lin.

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