Investigation of the Role of Machine Learning and Deep Learning in Improving Clinical Decision Making for Musculoskeletal Rehabilitation
Musculoskeletal rehabilitation is an important aspect of healthcare that involves the treatment and management of injuries and conditions affecting the muscles, bones, joints, and related tissues. Clinical decision-making in musculoskeletal rehabilitation involves complex and multifactorial consider...
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| Format: | Article |
| Language: | English |
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Ediciones Universidad de Salamanca
2024-06-01
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| Series: | Advances in Distributed Computing and Artificial Intelligence Journal |
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| Online Access: | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31590 |
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| author | Madhu Yadav Pushpendra Kumar Verma Sumaiya Ansari |
| author_facet | Madhu Yadav Pushpendra Kumar Verma Sumaiya Ansari |
| author_sort | Madhu Yadav |
| collection | DOAJ |
| description | Musculoskeletal rehabilitation is an important aspect of healthcare that involves the treatment and management of injuries and conditions affecting the muscles, bones, joints, and related tissues. Clinical decision-making in musculoskeletal rehabilitation involves complex and multifactorial considerations that can be challenging for healthcare professionals. Machine learning and deep learning techniques have the potential to enhance clinical judgement in musculoskeletal rehabilitation by providing insights into complex relationships between patient characteristics, treatment interventions, and outcomes. These techniques can help identify patterns and predict outcomes, allowing for personalized treatment plans and improved patient outcomes. In this investigation, we explore the various applications of machine learning and deep learning in musculoskeletal rehabilitation, including image analysis, predictive modelling, and decision support systems. We also examine the challenges and limitations associated with implementing these techniques in clinical practice and the ethical considerations surrounding their use. This investigation aims to highlight the potential benefits of using machine learning and deep learning in musculoskeletal rehabilitation and the need for further research to optimize their use in clinical practice. |
| format | Article |
| id | doaj-art-dcac4bdaa4834665817b1bff0009c01e |
| institution | Kabale University |
| issn | 2255-2863 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Ediciones Universidad de Salamanca |
| record_format | Article |
| series | Advances in Distributed Computing and Artificial Intelligence Journal |
| spelling | doaj-art-dcac4bdaa4834665817b1bff0009c01e2025-01-23T11:25:18ZengEdiciones Universidad de SalamancaAdvances in Distributed Computing and Artificial Intelligence Journal2255-28632024-06-0113e31590e3159010.14201/adcaij.3159037071Investigation of the Role of Machine Learning and Deep Learning in Improving Clinical Decision Making for Musculoskeletal RehabilitationMadhu Yadav0Pushpendra Kumar Verma1Sumaiya Ansari2Assistant Professor, Department of Physiotherapy, IIMT University, Meerut, Uttar Pradesh, India-250001Associate Professor, School of Computer Science Applications, IIMT University, Uttar Pradesh, India-250001Assistant Professor, Department of Physiotherapy, IIMT University, Meerut, Uttar Pradesh, India-250001Musculoskeletal rehabilitation is an important aspect of healthcare that involves the treatment and management of injuries and conditions affecting the muscles, bones, joints, and related tissues. Clinical decision-making in musculoskeletal rehabilitation involves complex and multifactorial considerations that can be challenging for healthcare professionals. Machine learning and deep learning techniques have the potential to enhance clinical judgement in musculoskeletal rehabilitation by providing insights into complex relationships between patient characteristics, treatment interventions, and outcomes. These techniques can help identify patterns and predict outcomes, allowing for personalized treatment plans and improved patient outcomes. In this investigation, we explore the various applications of machine learning and deep learning in musculoskeletal rehabilitation, including image analysis, predictive modelling, and decision support systems. We also examine the challenges and limitations associated with implementing these techniques in clinical practice and the ethical considerations surrounding their use. This investigation aims to highlight the potential benefits of using machine learning and deep learning in musculoskeletal rehabilitation and the need for further research to optimize their use in clinical practice.https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31590machine learningdeep learning, rehabilitationphysical therapymusculoskeletal |
| spellingShingle | Madhu Yadav Pushpendra Kumar Verma Sumaiya Ansari Investigation of the Role of Machine Learning and Deep Learning in Improving Clinical Decision Making for Musculoskeletal Rehabilitation Advances in Distributed Computing and Artificial Intelligence Journal machine learning deep learning, rehabilitation physical therapy musculoskeletal |
| title | Investigation of the Role of Machine Learning and Deep Learning in Improving Clinical Decision Making for Musculoskeletal Rehabilitation |
| title_full | Investigation of the Role of Machine Learning and Deep Learning in Improving Clinical Decision Making for Musculoskeletal Rehabilitation |
| title_fullStr | Investigation of the Role of Machine Learning and Deep Learning in Improving Clinical Decision Making for Musculoskeletal Rehabilitation |
| title_full_unstemmed | Investigation of the Role of Machine Learning and Deep Learning in Improving Clinical Decision Making for Musculoskeletal Rehabilitation |
| title_short | Investigation of the Role of Machine Learning and Deep Learning in Improving Clinical Decision Making for Musculoskeletal Rehabilitation |
| title_sort | investigation of the role of machine learning and deep learning in improving clinical decision making for musculoskeletal rehabilitation |
| topic | machine learning deep learning, rehabilitation physical therapy musculoskeletal |
| url | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31590 |
| work_keys_str_mv | AT madhuyadav investigationoftheroleofmachinelearninganddeeplearninginimprovingclinicaldecisionmakingformusculoskeletalrehabilitation AT pushpendrakumarverma investigationoftheroleofmachinelearninganddeeplearninginimprovingclinicaldecisionmakingformusculoskeletalrehabilitation AT sumaiyaansari investigationoftheroleofmachinelearninganddeeplearninginimprovingclinicaldecisionmakingformusculoskeletalrehabilitation |