Uncertainty-aware multi-path framework with dynamic arbitration for Alzheimer's disease MRI classification

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: Accuracy 98.85%, Sensitivity 99.18%, Specificity 97.06%, AUC 99.61%

• Primary Outcome: Classification of AD vs. cognitively normal on the ADNI dataset using sMRI with TriPathNet with Arbiter

• Nnt Or Sensitivity: Sensitivity: 99.18% (no CI reported); Specificity: 97.06% (no CI reported); Estimated positive likelihood ratio: ~33.7; Estimated negative likelihood ratio: ~0.009 — all figures require confidence intervals and external validation before clinical interpretation

• Confidence Interval: Not reported — a critical methodological omission

Clinical Application

sMRI is widely available in Australian tertiary and many secondary care settings. However, the computational infrastructure required for TriPathNet with Arbiter, integration with clinical PACS systems, and regulatory approval pathways represent substantial implementation barriers. The framework has not been tested prospectively or in a clinical workflow. In Australia, structural MRI for dementia workup is accessible via Medicare Benefits Schedule (MBS) item numbers for specialist-referred investigations. The RACGP and Dementia Australia guidelines emphasise multidisciplinary assessment including neuropsychological testing, biomarkers, and clinical history — sMRI alone is not a standalone diagnostic tool. The TGA would require prospective clinical validation, safety data, and a conformity assessment before any AI-based diagnostic tool could be approved as a medical device (Software as a Medical Device, SaMD) under the Therapeutic Goods Act 1989. PBS implications are not relevant at this stage. This study does not meet the evidentiary threshold for influencing Australian clinical practice guidelines. Currently applicable only to research settings using ADNI-equivalent data. Not validated for clinical application to memory clinic patients, primary care populations, or individuals with MCI. Not validated across scanner types, field strengths, or non-ADNI acquisition protocols.

Abstract

Objective.As a progressive neurodegenerative disorder, Alzheimer's disease (AD) requires early and accurate diagnosis to delay pathological progression and improve clinical outcomes. Recently, Deep-learning methods based on structural MRI (sMRI) have been widely used for computer-aided AD diagnosis. However, reliable deployment remains challenging due to its sample-specific heterogeneity and the lack of explicit mechanisms for handling prediction uncertainty. In addition, common test-time perturbations and augmentation-based inference strategies could introduce anatomical inconsistency in medical images, leading to unstable decisions for hard cases.Approach.Accordingly, TriPathNet with Arbiter, as an uncertainty-aware multi-path framework, is proposed in the study. The framework learns spatially complementary representations via multi-view parallel encoding with metric-guided coordination. In addition, an augmentation-aware arbitration mechanism is introduced to adaptively correct predictions for high-uncertainty samples during inference.Main results.On the ADNI dataset for the AD vs. cognitively normal task, the proposed method achieves 98.85% accuracy, 99.18% sensitivity, 97.06% specificity, and 99.61% AUC, yielding satisfactory performance.Significance.The results indicate that TriPathNet with Arbiter provides an accurate and robust solution for sMRI-based computer-aided AD diagnosis, maintaining stable performance under test-time perturbations.

References

1. 1.Du, S., Zhu, B., Yu, M., & Liu, J. (2026). Uncertainty-aware multi-path framework with dynamic arbitration for Alzheimer's disease MRI classification. Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/ae7905