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TRUVACE RECORD VERSION
record: TRV-2026-1244
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-02T06:55:31.869445Z
status: published
lens: trace
sector: health
headline: A review on diffusion tensor imaging-based comprehensive intelligent diagnosis of Alzheimer's disease
dek: AI-assisted early diagnosis of Alzheimer's disease (AD) has substantial clinical value, and diffusion tensor imaging (DTI), which captures white matter microstructural alterations, has considerable potential across the AD continuum. However, major barriers to clinical translation remain. This review systematically evaluated the evolution of algorithmic paradigms, the effectiveness of multimodal fusion, and barriers to clinical generalization in DTI-based AD diagnosis. Using an "Input-Model-Fusion" evaluation fra…
gain_title: AI models using diffusion tensor imaging to detect white matter changes show potential for early Alzheimer's diagnosis with clinical value.
problem_title: DTI-based AI models for Alzheimer's diagnosis suffer substantial performance degradation in multicenter or external validation, limiting clinical translation.
trace_subject: DTI-based AI diagnosis of Alzheimer's disease across the AD continuum
gain_reading: AI models using diffusion tensor imaging to detect white matter changes show potential for early Alzheimer's diagnosis with clinical value.
gain_evidence: AI-assisted early diagnosis of Alzheimer's disease (AD) has substantial clinical value | diffusion tensor imaging (DTI), which captures white matter microstructural alterations, has considerable potential across the AD continuum
problem_reading: DTI-based AI models for Alzheimer's diagnosis suffer substantial performance degradation in multicenter or external validation, limiting clinical translation.
problem_evidence: major barriers to clinical translation remain | Substantial performance degradation in multicenter or external validation highlighted persistent limitations in clinical generalizability
quick_read: A 2026 review in Reviews in the Neurosciences examined 98 studies from 2010 to 2026 on diffusion tensor imaging-based AI for Alzheimer's disease, using an Input-Model-Fusion framework to assess modality choice, algorithm paradigms, and fusion strategies.

The synthesis matters because it shows promising discriminative regions and task-specific utility but also documents degraded performance outside single-center settings, leaving uncertainty about standardized acquisition, representative sampling, and interpretable models needed for clinical adoption.
limitation: Findings are constrained by heterogeneity in data sources, acquisition protocols, and processing pipelines that limit comparability and generalizability, with performance dropping in multicenter validation.
tag: Dual reading
key_points: Review analyzed 98 studies from 2010 to 2026 using an Input-Model-Fusion framework comparing sample size, task, and validation design. | Conventional machine learning like support vector machines dominated small-sample studies while CNNs, graph convolutional networks, and Transformers were used with large datasets. | Hippocampal pathways, corpus callosum, fornix, and cingulum showed relatively stable discriminative value across studies.
rundown: The review systematically evaluated 98 DTI-based studies from 2010 to 2026, finding modality selection should be task-driven and that algorithm choice tracked sample size, with conventional methods in small studies and deep learning in large ones.

It identified persistent translation barriers including heterogeneous acquisition protocols, software pipelines, and validation strategies, and called for future work to prioritize standardized DTI processing, external validation, and neurobiological interpretability over accuracy competition.
sources:
- peer_reviewed | Reviews in the Neurosciences | https://doi.org/10.1515/revneuro-2026-0088 | 2026-10-01
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