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Chang MC, Kim JK, Park D, Kim JH, Kim CR, Choo YJ. The Use of Artificial Intelligence to Predict the Prognosis of Patients Undergoing Central Nervous System Rehabilitation: A Narrative Review. Healthcare (Basel) 2023; 11:2687. [PMID: 37830724 PMCID: PMC10572243 DOI: 10.3390/healthcare11192687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/27/2023] [Accepted: 09/30/2023] [Indexed: 10/14/2023] Open
Abstract
Applications of machine learning in the healthcare field have become increasingly diverse. In this review, we investigated the integration of artificial intelligence (AI) in predicting the prognosis of patients with central nervous system disorders such as stroke, traumatic brain injury, and spinal cord injury. AI algorithms have shown promise in prognostic assessment, but challenges remain in achieving a higher prediction accuracy for practical clinical use. We suggest that accumulating more diverse data, including medical imaging and collaborative efforts among hospitals, can enhance the predictive capabilities of AI. As healthcare professionals become more familiar with AI, its role in central nervous system rehabilitation is expected to advance significantly, revolutionizing patient care.
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Affiliation(s)
- Min Cheol Chang
- Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu 42415, Republic of Korea;
| | - Jeoung Kun Kim
- Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-si 38541, Republic of Korea;
| | - Donghwi Park
- Department of Rehabilitation Medicine, Daegu Fatima Hospital, Daegu 41199, Republic of Korea;
| | - Jang Hwan Kim
- Department of Rehabilitation Technology, Graduate School of Hanseo University, Seosan, Chungcheongnam-do 31962, Republic of Korea;
| | - Chung Reen Kim
- Department of Physical Medicine and Rehabilitation, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan 44033, Republic of Korea;
| | - Yoo Jin Choo
- Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu 42415, Republic of Korea;
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Choo YJ, Chang MC. Use of machine learning in the field of prosthetics and orthotics: A systematic narrative review. Prosthet Orthot Int 2023; 47:226-240. [PMID: 36811961 DOI: 10.1097/pxr.0000000000000199] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 09/08/2022] [Indexed: 02/24/2023]
Abstract
Although machine learning is not yet being used in clinical practice within the fields of prosthetics and orthotics, several studies on the use of prosthetics and orthotics have been conducted. We intend to provide relevant knowledge by conducting a systematic review of prior studies on using machine learning in the fields of prosthetics and orthotics. We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), Cochrane, Embase, and Scopus databases and retrieved studies published until July 18, 2021. The study included the application of machine learning algorithms to upper-limb and lower-limb prostheses and orthoses. The criteria of the Quality in Prognosis Studies tool were used to assess the methodological quality of the studies. A total of 13 studies were included in this systematic review. In the realm of prostheses, machine learning has been used to identify prosthesis, select an appropriate prosthesis, train after wearing the prosthesis, detect falls, and manage the temperature in the socket. In the field of orthotics, machine learning was used to control real-time movement while wearing an orthosis and predict the need for an orthosis. The studies included in this systematic review are limited to the algorithm development stage. However, if the developed algorithms are actually applied to clinical practice, it is expected that it will be useful for medical staff and users to handle prosthesis and orthosis.
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Affiliation(s)
- Yoo Jin Choo
- Production R&D Division Advanced Interdisciplinary Team, Medical Device Development Center, Daegu-Gyeongbuk Medical Innovation Foundation, Deagu, South Korea
| | - Min Cheol Chang
- Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu, South Korea
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Choo YJ, Chang MC. Use of Machine Learning in Stroke Rehabilitation: A Narrative Review. BRAIN & NEUROREHABILITATION 2022; 15:e26. [PMID: 36742082 PMCID: PMC9833483 DOI: 10.12786/bn.2022.15.e26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 09/09/2022] [Accepted: 10/07/2022] [Indexed: 12/05/2022] Open
Abstract
A narrative review was conducted of machine learning applications and research in the field of stroke rehabilitation. The machine learning models commonly used in medical research include random forest, logistic regression, and deep neural networks. Convolutional neural networks (CNNs), a type of deep neural network, are typically used for image analysis. Machine learning has been used in stroke rehabilitation to predict recovery of motor function using a large amount of clinical data as input. Recent studies on predicting motor function have trained CNN models using magnetic resonance images as input data together with clinical data to increase the accuracy of motor function prediction models. Additionally, a model interpreting videofluoroscopic swallowing studies was developed and investigated. In the future, we anticipate that machine learning will be actively used to treat stroke patients, such as predicting the occurrence of depression and the recovery of language, cognitive, and sensory function, as well as prescribing appropriate rehabilitation treatments.
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Affiliation(s)
- Yoo Jin Choo
- Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Daegu, Korea
| | - Min Cheol Chang
- Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Daegu, Korea
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Shin H, Kim JK, Choo YJ, Choi GS, Chang MC. Prediction of Motor Outcome of Stroke Patients Using a Deep Learning Algorithm with Brain MRI as Input Data. Eur Neurol 2022; 85:460-466. [PMID: 35738236 DOI: 10.1159/000525222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 05/22/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Deep learning techniques can outperform traditional machine learning techniques and learn from unstructured and perceptual data, such as images and languages. We evaluated whether a convolutional neural network (CNN) model using whole axial brain T2-weighted magnetic resonance (MR) images as input data can help predict motor outcomes of the upper and lower limbs at the chronic stage in stroke patients. METHODS We collected MR images taken at the early stage of stroke in 1,233 consecutive stroke patients. We categorized modified Brunnstrom classification (MBC) scores of ≥5 and functional ambulatory category (FAC) scores of ≥4 at 6 months after stroke as favorable outcomes in the upper and lower limbs, respectively, and MBC scores of <5 and FAC scores of <4 as poor outcomes. We applied a CNN to train the image data. Of the 1,233 patients, 70% (863 patients) were randomly selected for the training set and the remaining 30% (370 patients) were assigned to the validation set. RESULTS In the prediction of upper limb motor function on the validation dataset, the area under the curve (AUC) was 0.768, and for lower limb motor function, the AUC was 0.828. CONCLUSION We showed that a CNN model trained using whole-brain axial T2-weighted MR images of stroke patients would help predict upper and lower limb motor function at the chronic stage.
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Affiliation(s)
- Hyunkwang Shin
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Republic of Korea
| | - Jeoung Kun Kim
- Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-si, Republic of Korea
| | - Yoo Jin Choo
- Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu, Republic of Korea
| | - Gyu Sang Choi
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Republic of Korea
| | - Min Cheol Chang
- Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu, Republic of Korea
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Harris EJ, Khoo IH, Demircan E. A Survey of Human Gait-Based Artificial Intelligence Applications. Front Robot AI 2022; 8:749274. [PMID: 35047564 PMCID: PMC8762057 DOI: 10.3389/frobt.2021.749274] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 11/01/2021] [Indexed: 12/17/2022] Open
Abstract
We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.
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Affiliation(s)
- Elsa J Harris
- Human Performance and Robotics Laboratory, Department of Mechanical and Aerospace Engineering, California State University Long Beach, Long Beach, CA, United States
| | - I-Hung Khoo
- Department of Electrical Engineering, California State University Long Beach, Long Beach, CA, United States.,Department of Biomedical Engineering, California State University Long Beach, Long Beach, CA, United States
| | - Emel Demircan
- Human Performance and Robotics Laboratory, Department of Mechanical and Aerospace Engineering, California State University Long Beach, Long Beach, CA, United States.,Department of Biomedical Engineering, California State University Long Beach, Long Beach, CA, United States
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Kim JK, Chang MC, Park D. Deep Learning Algorithm Trained on Brain Magnetic Resonance Images and Clinical Data to Predict Motor Outcomes of Patients With Corona Radiata Infarct. Front Neurosci 2022; 15:795553. [PMID: 35046770 PMCID: PMC8763312 DOI: 10.3389/fnins.2021.795553] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 12/09/2021] [Indexed: 12/21/2022] Open
Abstract
The early and accurate prediction of the extent of long-term motor recovery is important for establishing specific rehabilitation strategies for stroke patients. Using clinical parameters and brain magnetic resonance images as inputs, we developed a deep learning algorithm to increase the prediction accuracy of long-term motor outcomes in patients with corona radiata (CR) infarct. Using brain magnetic resonance images and clinical data obtained soon after CR infarct, we developed an integrated algorithm to predict hand function and ambulatory outcomes of the patient 6 months after onset. To develop and evaluate the algorithm, we retrospectively recruited 221 patients with CR infarct. The area under the curve of the validation set of the integrated modified Brunnstrom classification prediction model was 0.891 with 95% confidence interval (0.814–0.967) and that of the integrated functional ambulatory category prediction model was 0.919, with 95% confidence interval (0.842–0.995). We demonstrated that an integrated algorithm trained using patients’ clinical data and brain magnetic resonance images obtained soon after CR infarct can promote the accurate prediction of long-term hand function and ambulatory outcomes. Future efforts will be devoted to finding more appropriate input variables to further increase the accuracy of deep learning models in clinical applications.
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Affiliation(s)
- Jeoung Kun Kim
- Department of Business Administration, School of Business, Yeungnam University, Gyeongsan, South Korea
| | - Min Cheol Chang
- Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Gyeongsan, South Korea
| | - Donghwi Park
- Department of Physical Medicine and Rehabilitation, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, South Korea
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