Computer aided patient evaluation in the low back pain pathology

Andrei, Diana; Poenaru, Dan V.; Nemes, D.; Milicin, Cristian; Vida, M.; Gal, Norbert; Nădăşan, Emanuela

doi:10.1109/saci.2015.7208233

Cited by 2 publications

(8 citation statements)

References 4 publications

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“…Sample sizes ranged from 71 to 4665 people. Six studies showed an accuracy of 61−98% 21,22,27,31,51,52 , while three did not report accuracy directly 46,59,67 . One study reported an area under the curve of 0.75 30 , while the other study reported a sensitivity and specificity of 88% and 86%, respectively 46 .…”

Section: Machine Learningmentioning

confidence: 96%

“…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%

“…No studies have used AI/ML techniques to assess LBP prognosis of pre-defined sub-groups on pain and disability outcomes. However, nine studies assessed the prognosis of LBP based on input parameters 21,22,27,30,31,46,51,52,59 . Studies examined prognosis prediction using AI/ML techniques of: satisfaction after lumbar stenosis surgery 21 , recurrent lumbar disc herniation 22 , recovery from acute LBP 27,30 , recovery from CLBP 31 , poor outcomes following lumbar surgery 46,51 , successful outcomes from cognitive behavioural therapy 52 and recovery based on pain chart measurements 59 .…”

Section: Machine Learningmentioning

confidence: 99%

“…Overall, 29 studies 20,[23][24][25]28,29,32,34,38,[40][41][42]44,45,[47][48][49][50][53][54][55]57,58,[61][62][63][64][65][66] were case−control while eight 21,22,27,30,31,37,46,52 were cohort studies. Eleven studies did not fit the criteria for case−control or cohort studies and did not undergo the risk of bias assessment 19,26,33,35,36,39,43,51,56,59,60 . Of the case−control studies, eight were considered 'fair' quality 20,48,…”

Section: Machine Learningmentioning

confidence: 99%

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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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Section: Machine Learningmentioning

confidence: 96%

Section: Machine Learningmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2020

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“…Information on inclusion and exclusion criteria were absent in almost all the studies. Ten studies [30, 32-36, 38, 39, 41, 42] made an effort to have a control group but definition of controls was not always the same: in four studies control group included healthy patients [30,32,34,42], one used datasets from healthy persons [35]; in one study [33] the control group was represented by LBP surgical patients; in three studies [36,38,41] the comparison used was experts suggestions/ classification/evaluation (versus ML system suggestions); in one study [34] the algorithm results were compared against a back screening tool results; in one study [39] the results from active LBP patients were compared with healthy controls and with remission LBP patients.…”

Section: Characteristics Of Studies Includedmentioning

confidence: 99%

Machine Learning Applied to Low Back Pain Rehabilitation – A Systematic Review

Amorim

Paulo

Silva

et al. 2021

International Journal of Digital Health

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Background: Low back pain (LBP) is the leading cause of disability worldwide and an important cause of work absenteeism in the active population. As a recurrent condition, prevention is crucial. Home exercises are effective, but adherence and accurate performance of the exercises are difficult to monitor by doctors and therapists. Machine learning (ML) applied to rehabilitation systems could be a solution to address telerehabilitation for people with chronic LBP if it holds sufficient accuracy in monitoring adherence performance while providing patient guidance. The aim was to search and review studies that have used ML techniques for rehabilitation of people with LBP. To develop an understanding on the outcomes measured, the clinical setting (face-to-face rehabilitation or remote rehabilitation) where interventions took place, and the clinical research methodology that has been used.Materials and Methods: a systematic review was performed based on research material obtained from literature indexed on MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), Scopus, Web of Science and IEEE Xplore databases to locate papers focused on the use of ML applied to rehabilitation of LBP.Results: after revision of the inclusion and exclusion criteria using the PRISMA methodology, only 14 studies remained for the analysis that is presented as a qualitative synthesis.Conclusions: ML approaches applied to rehabilitation could help health professionals and LBP patients to manage this condition that affects a significant amount of the active population. ML could be applied to support clinical decisions and to guide patients self-manage their LBP remotely, which makes it a potential telerehabilitation solution. More and better studies, with more participants and following guidelines for best research practice are needed to strengthen the clinical evidence.

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Computer aided patient evaluation in the low back pain pathology

Cited by 2 publications

References 4 publications

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

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

Machine Learning Applied to Low Back Pain Rehabilitation – A Systematic Review

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