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Khan A, Galarraga O, Garcia-Salicetti S, Vigneron V. Phase-Based Gait Prediction after Botulinum Toxin Treatment Using Deep Learning. SENSORS (BASEL, SWITZERLAND) 2024; 24:5343. [PMID: 39205037 PMCID: PMC11360713 DOI: 10.3390/s24165343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 08/14/2024] [Accepted: 08/17/2024] [Indexed: 09/04/2024]
Abstract
Gait disorders in neurological diseases are frequently associated with spasticity. Intramuscular injection of Botulinum Toxin Type A (BTX-A) can be used to treat spasticity. Providing optimal treatment with the highest possible benefit-risk ratio is a crucial consideration. This paper presents a novel approach for predicting knee and ankle kinematics after BTX-A treatment based on pre-treatment kinematics and treatment information. The proposed method is based on a Bidirectional Long Short-Term Memory (Bi-LSTM) deep learning architecture. Our study's objective is to investigate this approach's effectiveness in accurately predicting the kinematics of each phase of the gait cycle separately after BTX-A treatment. Two deep learning models are designed to incorporate categorical medical treatment data corresponding to the injected muscles: (1) within the hidden layers of the Bi-LSTM network, (2) through a gating mechanism. Since several muscles can be injected during the same session, the proposed architectures aim to model the interactions between the different treatment combinations. In this study, we conduct a comparative analysis of our prediction results with the current state of the art. The best results are obtained with the incorporation of the gating mechanism. The average prediction root mean squared error is 2.99° (R2 = 0.85) and 2.21° (R2 = 0.84) for the knee and the ankle kinematics, respectively. Our findings indicate that our approach outperforms the existing methods, yielding a significantly improved prediction accuracy.
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Affiliation(s)
- Adil Khan
- Informatique, Bio-Informatique et Systèmes Complexes (IBISC) EA 4526, Univ Evry, Université Paris-Saclay, 91020 Evry, France;
- Department of Computer Science, Sukkur IBA University, Sukkur 65200, Sindh, Pakistan
| | - Omar Galarraga
- UGECAM Ile-de-France, Movement Analysis Laboratory, 77170 Coubert, France;
| | | | - Vincent Vigneron
- Informatique, Bio-Informatique et Systèmes Complexes (IBISC) EA 4526, Univ Evry, Université Paris-Saclay, 91020 Evry, France;
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Chen PY, Yang TW, Tseng YS, Tsai CY, Yeh CS, Lee YH, Lin PH, Lin TC, Wu YJ, Yang TH, Chiang YT, Hsu JSJ, Hsu CJ, Chen PL, Chou CF, Wu CC. Machine learning-based longitudinal prediction for GJB2-related sensorineural hearing loss. Comput Biol Med 2024; 176:108597. [PMID: 38763069 DOI: 10.1016/j.compbiomed.2024.108597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/21/2024]
Abstract
BACKGROUND Recessive GJB2 variants, the most common genetic cause of hearing loss, may contribute to progressive sensorineural hearing loss (SNHL). The aim of this study is to build a realistic predictive model for GJB2-related SNHL using machine learning to enable personalized medical planning for timely intervention. METHOD Patients with SNHL with confirmed biallelic GJB2 variants in a nationwide cohort between 2005 and 2022 were included. Different data preprocessing protocols and computational algorithms were combined to construct a prediction model. We randomly divided the dataset into training, validation, and test sets at a ratio of 72:8:20, and repeated this process ten times to obtain an average result. The performance of the models was evaluated using the mean absolute error (MAE), which refers to the discrepancy between the predicted and actual hearing thresholds. RESULTS We enrolled 449 patients with 2184 audiograms available for deep learning analysis. SNHL progression was identified in all models and was independent of age, sex, and genotype. The average hearing progression rate was 0.61 dB HL per year. The best MAE for linear regression, multilayer perceptron, long short-term memory, and attention model were 4.42, 4.38, 4.34, and 4.76 dB HL, respectively. The long short-term memory model performed best with an average MAE of 4.34 dB HL and acceptable accuracy for up to 4 years. CONCLUSIONS We have developed a prognostic model that uses machine learning to approximate realistic hearing progression in GJB2-related SNHL, allowing for the design of individualized medical plans, such as recommending the optimal follow-up interval for this population.
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Affiliation(s)
- Pey-Yu Chen
- Department of Otolaryngology, MacKay Memorial Hospital, Taipei, Taiwan; Department of Audiology and Speech-Language Pathology, Mackay Medical College, New Taipei City, Taiwan; Department of Otolaryngology, National Taiwan University Hospital, Taipei, Taiwan
| | - Ta-Wei Yang
- Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan
| | - Yi-Shan Tseng
- Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Cheng-Yu Tsai
- Department of Otolaryngology, National Taiwan University Hospital, Taipei, Taiwan; Graduate Institute of Medical Genomics and Proteomics, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Chiung-Szu Yeh
- Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Yen-Hui Lee
- Department of Otolaryngology, National Taiwan University Biomedical Park Hospital, Hsinchu County, Taiwan; Department of Otolaryngology, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu City, Taiwan; Hearing and Speech Center, National Taiwan University Hospital, Taipei, Taiwan
| | - Pei-Hsuan Lin
- Department of Otolaryngology, National Taiwan University Hospital, Taipei, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Ting-Chun Lin
- Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Yu-Jen Wu
- Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Ting-Hua Yang
- Department of Otolaryngology, National Taiwan University Hospital, Taipei, Taiwan
| | - Yu-Ting Chiang
- Department of Otolaryngology, National Taiwan University Hospital, Taipei, Taiwan; Graduate Institute of Medical Genomics and Proteomics, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Jacob Shu-Jui Hsu
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Chuan-Jen Hsu
- Department of Otolaryngology, National Taiwan University Hospital, Taipei, Taiwan; Department of Otorhinolaryngology-Head and Neck Surgery, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan; School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Pei-Lung Chen
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University College of Medicine, Taipei, Taiwan; Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan
| | - Chen-Fu Chou
- Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Chen-Chi Wu
- Department of Otolaryngology, National Taiwan University Hospital, Taipei, Taiwan; Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan; Department of Medical Research, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan.
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Ding G, Georgilas I, Plummer A. A Deep Learning Model with a Self-Attention Mechanism for Leg Joint Angle Estimation across Varied Locomotion Modes. SENSORS (BASEL, SWITZERLAND) 2023; 24:211. [PMID: 38203073 PMCID: PMC10781404 DOI: 10.3390/s24010211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/22/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024]
Abstract
Conventional trajectory planning for lower limb assistive devices usually relies on a finite-state strategy, which pre-defines fixed trajectory types for specific gait events and activities. The advancement of deep learning enables walking assistive devices to better adapt to varied terrains for diverse users by learning movement patterns from gait data. Using a self-attention mechanism, a temporal deep learning model is developed in this study to continuously generate lower limb joint angle trajectories for an ankle and knee across various activities. Additional analyses, including using Fast Fourier Transform and paired t-tests, are conducted to demonstrate the benefits of the proposed attention model architecture over the existing methods. Transfer learning has also been performed to prove the importance of data diversity. Under a 10-fold leave-one-out testing scheme, the observed attention model errors are 11.50% (±2.37%) and 9.31% (±1.56%) NRMSE for ankle and knee angle estimation, respectively, which are small in comparison to other studies. Statistical analysis using the paired t-test reveals that the proposed attention model appears superior to the baseline model in terms of reduced prediction error. The attention model also produces smoother outputs, which is crucial for safety and comfort. Transfer learning has been shown to effectively reduce model errors and noise, showing the importance of including diverse datasets. The suggested joint angle trajectory generator has the potential to seamlessly switch between different locomotion tasks, thereby mitigating the problem of detecting activity transitions encountered by the traditional finite-state strategy. This data-driven trajectory generation method can also reduce the burden on personalization, as traditional devices rely on prosthetists to experimentally tune many parameters for individuals with diverse gait patterns.
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Affiliation(s)
| | - Ioannis Georgilas
- Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK; (G.D.); (A.P.)
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