Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 2 appraisals
PloS one
A late fusion multi-task learning for respiratory waveform and rate estimation from photoplethysmography
Continuous respiratory monitoring enables early detection of physiological deterioration, yet conventional capnography remains impractical for prolonged use. Photoplethysmography (PPG) offers a non-invasive alternative that encodes respiratory information through baseline wander (respiratory-induced intensity variation; RIIV), amplitude modulation (respiratory-induced amplitude variation; RIAV), and frequency modulation (respiratory-induced frequency variation; RIFV) of the pulsatile waveform. Existing PPG-based deep learning approaches, whether operating on the raw signal or on these physiological modulations, are limited to single-task architectures that estimate either respiratory rate or reconstruct the respiratory waveform in isolation, without jointly addressing both outputs. We propose a late fusion multi-task framework in which dedicated encoder branches independently process each modulation before fusion, and dual decoders simultaneously reconstruct the respiratory waveform and estimate the respiratory rate. The framework was evaluated on the CapnoBase (n = 42) and BIDMC (n = 52) benchmarks across multiple training strategies. For respiratory-rate estimation, the best transfer-learning configurations achieved a mean absolute error (MAE) of 2.27 bpm on CapnoBase and 1.33 bpm on BIDMC. For waveform reconstruction, the corresponding MAE values were 19.00% and 20.90%, with moderate correlations (r = 0.662 and r = 0.591, respectively). Sequential transfer learning consistently outperformed all other strategies, whereas pooled training degraded both outputs, demonstrating that capnography-derived and impedance-derived waveforms are not interchangeable training targets. These findings establish that short-window PPG can simultaneously support respiratory-rate estimation and waveform reconstruction, when reference signal compatibility is explicitly addressed in multi-task training.
12 July 2026
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Deep Learning-Based Surrogate Model of Subject-Specific Finite-Element Analysis for Vertebrae
Subject-specific finite-element analysis (FEA) models enable accurate simulation of vertebral biomechanics but are often time-consuming to construct and solve under varying conditions. This study presents a novel deep learning (DL)/machine learning (ML)-based surrogate model that predicts stress distributions in vertebral bodies with high efficiency. The model integrates vertebral shape encoding and employs separate decoding branches for surface and internal nodes. It was trained on 3,960 synthetic L1 vertebrae generated via data augmentation from 42 real computed tomography (CT) scans. Evaluation on independent test samples yielded a mean absolute error (MAE) of 0.0596 MPa and an $\mathrm{R}^{2}$ of 0.864 for von Mises stress. Visualization results confirm strong agreement between predicted and FEA-computed stress patterns, with localized discrepancies observed at the anteroinferior margin and pedicles. Moreover, an end-to-end automated pipeline was established based on the developed model, reducing the total processing time from 90-120min to approximately 134-154s per subject. These findings highlight the potential of the proposed surrogate model to facilitate rapid, subject-specific biomechanical assessments in clinical workflows.
3 July 2026
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