An Artificial Neural Network Algorithm for the Evaluation of Postoperative Rehabilitation of Patients

Tang, Kunhao; Luo, Ruogu; Zhang, Sanhua

doi:10.1155/2021/3959844

Cited by 6 publications

(6 citation statements)

References 8 publications

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“…If no suitable model (autocorrelation, residual distribution) can be found, a neural network will be used to answer this research question [ 92 ].…”

Section: Methodsmentioning

confidence: 99%

Clinical phenotypes and prognostic factors in persons with hip osteoarthritis undergoing total hip arthroplasty: protocol for a longitudinal prospective cohort study (HIPPROCLIPS)

Sergooris

Verbrugghe

Matheve

et al. 2023

BMC Musculoskelet Disord

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Background Large heterogeneity exists in the clinical manifestation of hip osteoarthritis (OA). It is therefore not surprising that pain and disability in individuals with hip OA and after total hip arthroplasty (THA) cannot be explained by biomedical variables alone. Indeed, also maladaptive pain-related cognitions and emotions can contribute to pain and disability, and can lead to poor treatment outcomes. Traumatic experiences, mental disorders, self-efficacy and social support can influence stress appraisal and strategies to cope with pain, but their influence on pain and disability has not yet been established in individuals with hip OA undergoing THA. This study aims (1) to determine the influence of traumatic experiences and mental disorders on pain processing before and shortly after THA (2) to identify preoperative clinical phenotypes in individuals with hip OA eligible for THA, (3) to identify pre- and early postoperative prognostic factors for outcomes in pain and disability after THA, and (4) to identify postoperative clinical phenotypes in individuals after THA. Methods This prospective longitudinal cohort study will investigate 200 individuals undergoing THA for hip OA. Phenotyping variables and candidate prognostic factors include pain-related fear-avoidance behaviour, perceived injustice, mental disorders, traumatic experiences, self-efficacy, and social support. Peripheral and central pain mechanisms will be assessed with thermal quantitative sensory testing. The primary outcome measure is the hip disability and osteoarthritis outcome score. Other outcome measures include performance-based measures, hip muscle strength, the patient-specific functional scale, pain intensity, global perceived effect, and outcome satisfaction. All these measurements will be performed before surgery, as well as 6 weeks, 3 months, and 12 months after surgery. Pain-related cognitions and emotions will additionally be assessed in the early postoperative phase, on the first, third, fifth, and seventh day after THA. Main statistical methods that will be used to answer the respective research questions include: LASSO regression, decision tree learning, gradient boosting algorithms, and recurrent neural networks. Discussion The identification of clinical phenotypes and prognostic factors for outcomes in pain and disability will be a first step towards pre- and postoperative precision medicine for individuals with hip OA undergoing THA. Trial registration ClinicalTrials.gov: NCT05265858. Registered on 04/03/2022.

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“…If no suitable model (autocorrelation, residual distribution) can be found, a neural network will be used to answer this research question [ 92 ].…”

Section: Methodsmentioning

confidence: 99%

Clinical phenotypes and prognostic factors in persons with hip osteoarthritis undergoing total hip arthroplasty: protocol for a longitudinal prospective cohort study (HIPPROCLIPS)

Sergooris

Verbrugghe

Matheve

et al. 2023

BMC Musculoskelet Disord

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“…The paper Tang et al (2021) proposed an RNN for rehabilitation evaluation. This method extracts gait characteristic parameters of patients with different ages, disease types, and disease courses by learning existing clinical gait data.…”

Section: Neural Networkmentioning

confidence: 99%

AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: a review

Coser,

Tamantini,

Soda

et al. 2024

Front. Robot. AI

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Over the past few years, there has been a noticeable surge in efforts to design novel tools and approaches that incorporate Artificial Intelligence (AI) into rehabilitation of persons with lower-limb impairments, using robotic exoskeletons. The potential benefits include the ability to implement personalized rehabilitation therapies by leveraging AI for robot control and data analysis, facilitating personalized feedback and guidance. Despite this, there is a current lack of literature review specifically focusing on AI applications in lower-limb rehabilitative robotics. To address this gap, our work aims at performing a review of 37 peer-reviewed papers. This review categorizes selected papers based on robotic application scenarios or AI methodologies. Additionally, it uniquely contributes by providing a detailed summary of input features, AI model performance, enrolled populations, exoskeletal systems used in the validation process, and specific tasks for each paper. The innovative aspect lies in offering a clear understanding of the suitability of different algorithms for specific tasks, intending to guide future developments and support informed decision-making in the realm of lower-limb exoskeleton and AI applications.

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“…This algorithm has the advantages of high precision, wide applicability, strong nonlinear data analysis ability, and overfitting difficulty [ 31 ]. The artificial neural network algorithm (ANN) is an algorithmic model that imitates the structure and function of biological neural networks [ 32 ]. Inspired by the neural organization of the human brain, the algorithm designs computing nodes similar to neurons and connects them to form a network.…”

Section: Introductionmentioning

confidence: 99%

The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review

Wei,

2023

Sensors

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The integration of wearable sensor technology and machine learning algorithms has significantly transformed the field of intelligent medical rehabilitation. These innovative technologies enable the collection of valuable movement, muscle, or nerve data during the rehabilitation process, empowering medical professionals to evaluate patient recovery and predict disease development more efficiently. This systematic review aims to study the application of wearable sensor technology and machine learning algorithms in different disease rehabilitation training programs, obtain the best sensors and algorithms that meet different disease rehabilitation conditions, and provide ideas for future research and development. A total of 1490 studies were retrieved from two databases, the Web of Science and IEEE Xplore, and finally 32 articles were selected. In this review, the selected papers employ different wearable sensors and machine learning algorithms to address different disease rehabilitation problems. Our analysis focuses on the types of wearable sensors employed, the application of machine learning algorithms, and the approach to rehabilitation training for different medical conditions. It summarizes the usage of different sensors and compares different machine learning algorithms. It can be observed that the combination of these two technologies can optimize the disease rehabilitation process and provide more possibilities for future home rehabilitation scenarios. Finally, the present limitations and suggestions for future developments are presented in the study.

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An Artificial Neural Network Algorithm for the Evaluation of Postoperative Rehabilitation of Patients

Cited by 6 publications

References 8 publications

Clinical phenotypes and prognostic factors in persons with hip osteoarthritis undergoing total hip arthroplasty: protocol for a longitudinal prospective cohort study (HIPPROCLIPS)

Clinical phenotypes and prognostic factors in persons with hip osteoarthritis undergoing total hip arthroplasty: protocol for a longitudinal prospective cohort study (HIPPROCLIPS)

AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: a review

The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review

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