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publications
The Application of Integrated Force and Temperature Sensors to Enhance Orthotic Treatment Monitoring in Adolescent Idiopathic Scoliosis: A Pilot Study
Published in Sensors (Basel), 2025
Orthosis-wearing compliance is crucial for achieving positive treatment outcomes in patients with adolescent idiopathic scoliosis (AIS), for whom 23 h of daily wear is typically prescribed. However, self-reported compliance is subjective and often based on patients’ memory, leading to inaccuracies. While portable electronic devices have been developed to objectively monitor compliance, relying solely on temperature or force data can be insufficient. This study introduced a novel method that integrated both force and temperature data to estimate orthosis-wearing compliance. Twelve patients (eight females and four males) diagnosed with moderate AIS were included. Each patient was prescribed a thoracic-lumbar-sacral orthosis equipped with an integrated force and temperature sensor system. After one month of orthotic treatment, self-reported wear time averaged 17.8 ± 6.2 h/day, while the sensor indicated an average wear time of 13.3 ± 5.0 h/day. Most patients overestimated their compliance. Nighttime was the most common period for orthosis wear (6.1 h/day), whereas compliance during school hours (2.8 h/day) and after-school hours (3.7 h/day) was lower. The integration of force and temperature sensors provides a more comprehensive understanding of orthosis compliance. Future studies with larger samples and longer monitoring periods are needed to investigate the correlation between compliance and treatment outcomes.
Recommended citation: Zou Y, Zhou L, Wang J, Lou E, Wong MS. The Application of Integrated Force and Temperature Sensors to Enhance Orthotic Treatment Monitoring in Adolescent Idiopathic Scoliosis: A Pilot Study. Sensors (Basel). 2025 Jan 23;25(3):686. doi: 10.3390/s25030686. PMID: 39943325; PMCID: PMC11819978.
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Veriserum: A dual-plane fluoroscopic dataset with knee implant phantoms for deep learning in medical imaging
Published in Medical Image Computing and Computer Assisted Intervention – MICCAI 2025, 2025
Veriserum is an open-source dataset designed to support the training of deep learning registration for dual-plane fluoroscopic analysis. It comprises approximately 110,000 X-ray images of 10 knee implant pair combinations (2 femur and 5 tibia implants) captured during 1,600 trials, incorporating poses associated with daily activities such as level gait and ramp descent. Each image is annotated with an automatically registered ground-truth pose, while 200 images include manually registered poses for benchmarking. Key features of Veriserum include dual-plane images and calibration tools. The dataset aims to support the development of applications such as 2D/3D image registration, image segmentation, X-ray distortion correction, and 3D reconstruction. Freely accessible, Veriserum aims to advance computer vision and medical imaging research by providing a reproducible benchmark for algorithm development and evaluation. The Veriserum dataset used in this study is publicly available via https://movement.ethz.ch/data-repository/veriserum.html, with the data stored at ETH Zürich Research Collections: https://doi.org/10.3929/ethz-b-000701146.
Recommended citation: Wang, J., Vogl, F., Schütz, P., Ćuković, S., Taylor, W.R. (2026). Veriserum: A Dual-Plane Fluoroscopic Dataset with Knee Implant Phantoms for Deep Learning in Medical Imaging. In: Gee, J.C., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025. MICCAI 2025. Lecture Notes in Computer Science, vol 15972. Springer, Cham. https://doi.org/10.1007/978-3-032-05169-1_62
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talks
PyTorch3D Differentiable DRR Renderer
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A PyTorch3D-based volumetric and surface renderer for digital reconstructed radiography (DRR).
Deep Auto Match (DAM)
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Fluoro2DRR Image Translation for Registration
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A deep learning-based framework to translate noisy fluoroscopic images into DRR-like representations to improve registration robustness.
Veriserum
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Dual-plane fluoroscopic dataset for knee joint analysis
Pose-aware Deep Similarity
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A pose-conditioned similarity learning framework for improving registration convergence in medical 2D/3D alignment tasks.
teaching
Vision Academy
Ox&Cam Elite Camp, Shanghai, 2021
Starlight Education
Lecturer, Beijing, 2025