ArticleCyborg and bionic systems (Washington, D.C.)2024
A Respiratory Motion Prediction Method Based on LSTM-AE with Attention Mechanism for Spine Surgery.
Article in Cyborg and bionic systems (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 24 citations in OpenAlex.
- Comprehensive Stiffness Modeling and Evaluation of an Orthopedic Surgical Robot for Enhanced Cutting Operation Performance.Biomimetics (Basel, Switzerland) · 2025Article
- A Novel 3D Magnetic Resonance Imaging Registration Framework Based on the Swin-Transformer UNet+ Model with 3D Dynamic Snake Convolution Scheme.Journal of imaging · 2025Article
- Regional Variations in Mechanical Properties of Porcine Leptomeninges.Cyborg and bionic systems (Washington, D.C.) · 2025Article
- Improved Generalizability in Medical Computer Vision: Hyperbolic Deep Learning in Multi-Modality Neuroimaging.Journal of imaging · 2024Article
- Advances of surgical robotics: image-guided classification and application.National science review · 2024Review
- Robot-Assisted Augmented Reality (AR)-Guided Surgical Navigation for Periacetabular Osteotomy.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Respiratory motion-induced vertebral movements can adversely impact intraoperative spine surgery, resulting in inaccurate positional information of the target region and unexpected damage during the operation. In this paper, we propose a novel deep learning architecture for respiratory motion prediction, which can adapt to different patients. The proposed method utilizes an LSTM-AE with attention mechanism network that can be trained using few-shot datasets during operation. To ensure real-time performance, a dimension reduction method based on the respiration-induced physical movement of spine vertebral bodies is introduced. The experiment collected data from prone-positioned patients under general anaesthesia to validate the prediction accuracy and time efficiency of the LSTM-AE-based motion prediction method. The experimental results demonstrate that the presented method (RMSE: 4.39%) outperforms other methods in terms of accuracy within a learning time of 2 min. The maximum predictive errors under the latency of 333 ms with respect to the
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.