A Machine Learning-based Surface Electromyography Topography Evaluation for Prognostic Prediction of Functional Restoration Rehabilitation in Chronic Low Back Pain

Jiang, Naifu; Luk, K.D.K.; Hu, Yong

doi:10.1097/brs.0000000000002159

Cited by 39 publications

(48 citation statements)

References 39 publications

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“…ML has also been successfully employed to investigate spine disorders by means of electromyography systems . The authors built an SVM to identify patients responding to a functional restoration rehabilitation program for chronic low back pain, based on dynamic surface electromyography readings, with an accuracy of 96% on a sample of 30 patients.…”

Section: Applications Of Ai and ML In Spine Researchmentioning

confidence: 99%

Artificial intelligence and machine learning in spine research

2019

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Artificial intelligence (AI) and machine learning (ML) techniques are revolutionizing several industrial and research fields like computer vision, autonomous driving, natural language processing, and speech recognition. These novel tools are already having a major impact in radiology, diagnostics, and many other fields in which the availability of automated solution may benefit the accuracy and repeatability of the execution of critical tasks. In this narrative review, we first present a brief description of the various techniques that are being developed nowadays, with special focus on those used in spine research. Then, we describe the applications of AI and ML to problems related to the spine which have been published so far, including the localization of vertebrae and discs in radiological images, image segmentation, computer‐aided diagnosis, prediction of clinical outcomes and complications, decision support systems, content‐based image retrieval, biomechanics, and motion analysis. Finally, we briefly discuss major ethical issues related to the use of AI in healthcare, namely, accountability, risk of biased decisions as well as data privacy and security, which are nowadays being debated in the scientific community and by regulatory agencies.

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Section: Applications Of Ai and ML In Spine Researchmentioning

confidence: 99%

Artificial intelligence and machine learning in spine research

2019

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“…Overall, the sample sizes ranged from 10 to 34,589 people. The populations consisted of 16 studies that looked at chronic LBP 19,20,24,28,29,31,36,37,39,42,[54][55][56][57]62,64 , two acute LBP 27,30 , one recurrent 22 , one lumbar spinal stenosis 21 , two surgical 46,61 , nine other (mixed samples) 35,38,40,41,48,51,53,65,66 and 17 were unclear (LBP type not defined) 23,25,26,[32][33][34][43][44][45]47,49,50,52,[58][59][60]63 . Ten studies did not report training and testing of the data sets 26,29,...…”

Section: Machine Learningmentioning

confidence: 99%

“…Classification of LBP was assessed in 25 studies, all of which attempted binary classification to detect the presence of LBP or not 19,20,[23][24][25]28,29,[31][32][33]37,[40][41][42]44,47,49,50,[53][54][55]57,[62][63][64] . One study classified golfers with and without LBP based on electromyography and golf kinematic data using a support vector machine (multilayer perceptron with one layer, where input data are placed into vector spaces) 12 with 100% accuracy 47 .…”

Section: Machine Learningmentioning

confidence: 99%

Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

et al. 2020

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Artificial intelligence and machine learning (AI/ML) could enhance the ability to detect patterns of clinical characteristics in lowback pain (LBP) and guide treatment. We conducted three systematic reviews to address the following aims: (a) review the status of AI/ML research in LBP, (b) compare its status to that of two established LBP classification systems (STarT Back, McKenzie). AI/ML in LBP is in its infancy: 45 of 48 studies assessed sample sizes <1000 people, 19 of 48 studies used ≤5 parameters in models, 13 of 48 studies applied multiple models and attained high accuracy, 25 of 48 studies assessed the binary classification of LBP versus no-LBP only. Beyond the 48 studies using AI/ML for LBP classification, no studies examined use of AI/ML in prognosis prediction of specific subgroups , and AI/ML techniques are yet to be implemented in guiding LBP treatment. In contrast, the STarT Back tool has been assessed for internal consistency, test−retest reliability, validity, pain and disability prognosis, and influence on pain and disability treatment outcomes. McKenzie has been assessed for inter-and intra-tester reliability, prognosis, and impact on pain and disability outcomes relative to other treatments. For AI/ML methods to contribute to the refinement of LBP (sub-)classification and guide treatment allocation, large data sets containing known and exploratory clinical features should be examined. There is also a need to establish reliability, validity, and prognostic capacity of AI/ML techniques in LBP as well as its ability to inform treatment allocation for improved patient outcomes and/or reduced healthcare costs.

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“…The resultant predictions would allow surgeons and patients to better understand the likelihood of success and make more informed treatment decisions. 50,51 Another study used EMR data to predict the need for intraoperative or postoperative blood transfusion. 52 This allows for the surgical team to better preoperatively optimize these patients and have equipment available to mitigate blood loss and expedite transfusions as needed.…”

Section: Personalized Treatment Algorithmsmentioning

confidence: 99%

Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

et al. 2019

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Developments in machine learning in recent years have precipitated a surge in research on the applications of artificial intelligence within medicine. Machine learning algorithms are beginning to impact medicine broadly, and the field of spine surgery is no exception. Electronic medical records are a key source of medical data that can be leveraged for the creation of clinically valuable machine learning algorithms. This review examines the current state of machine learning using electronic medical records as it applies to spine surgery. Studies across the electronic medical record data domains of imaging, text, and structured data are reviewed. Discussed applications include clinical prognostication, preoperative planning, diagnostics, and dynamic clinical assistance, among others. The limitations and future challenges for machine learning research using electronic medical records are also discussed.

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A Machine Learning-based Surface Electromyography Topography Evaluation for Prognostic Prediction of Functional Restoration Rehabilitation in Chronic Low Back Pain

Abstract: 3.

Cited by 39 publications

References 39 publications

Artificial intelligence and machine learning in spine research

Artificial intelligence and machine learning in spine research

Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

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