With the increasing prevalence of neurodegenerative diseases driven by population aging, imaging-based biomarkers are needed to quantify brain aging at an early stage. Brain age, which estimates structural brain aging relative to chronological age, has emerged as a useful indicator. Prior work has mainly used T1-weighted MRI with deep learning models such as convolutional neural networks (CNNs) or transformers; however, many approaches insufficiently capture three-dimensional structural continuity and localized anatomical patterns, and tissue-specific aging in gray matter (GM) and white matter (WM) is often treated as auxiliary. To address these limitations, we propose a 3D Global–Patch Transformer framework for brain age prediction that directly processes volumetric data while jointly learning global brain structure and local anatomical features. Our model runs global and patch pathways in parallel and explicitly incorporates GM and WM structural maps alongside T1-weighted MRI to encode tissue-specific aging signals. Experiments on multiple public datasets, including IXI and OASIS, show that the proposed method reduces mean absolute error (MAE) by approximately 10–15% compared with CNN-based and single-input transformer baselines, with notably improved performance in older populations, highlighting the value of tissue-level structural information for brain age estimation.

Accurate QT-interval measurement requires reliable identification of QRS onset and T-wave offset. We present QTANet, a single-lead framework that classifies ECG samples as baseline, QRS or T wave and derives QT from the detected landmarks. We used 179 recordings from the Lobachevsky University Electrocardiography Database, with a fixed test set and patient-wise cross-validation, and external evaluation without fine-tuning on the QT Database (QTDB) and 33 clinical recordings. On the expert-annotated QTDB interval subset of 101 recordings, QTANet achieved a mean absolute error of 34.94 ms and measurement coverage of 95.2%, compared with 40.55 ms and 88.2% for the control without attention and 52.41 ms and 68.7% for NeuroKit2. In a separate expert-annotated subset permitting frame-level evaluation, attention increased per-recording T-wave F1 by 0.061, with improvement in 30 of 41 recordings. Attention most consistently reduced T waves misread as QRS complexes. QTANet outperformed an identically trained published U-Net externally at frame level but had higher internal QT error; a larger modern U-Net outperformed QTANet at frame level and in QTDB QT error. Internal interval-level gains were small and clinical results varied across training seeds. These findings support boundary-derived QT measurement with jointly reported error and coverage; clinical utility requires further validation.