Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews
Abstract Artificial intelligence and machine learning (AI/ML) could enhance the ability to detect patterns of clinical characteristics in low-back 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)...
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2020-07-01
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Online Access: | https://doi.org/10.1038/s41746-020-0303-x |
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doaj-9092303b7bbc4ef1b1ffb87ef1d967622021-07-11T11:08:31ZengNature Publishing Groupnpj Digital Medicine2398-63522020-07-013111610.1038/s41746-020-0303-xArtificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviewsScott D. Tagliaferri0Maia Angelova1Xiaohui Zhao2Patrick J. Owen3Clint T. Miller4Tim Wilkin5Daniel L. Belavy6Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin UniversitySchool of Information Technology, Deakin UniversityXi’an University of Architecture & TechnologyInstitute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin UniversityInstitute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin UniversitySchool of Information Technology, Deakin UniversityInstitute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin UniversityAbstract Artificial intelligence and machine learning (AI/ML) could enhance the ability to detect patterns of clinical characteristics in low-back 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 sub-groups, 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.https://doi.org/10.1038/s41746-020-0303-x |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Scott D. Tagliaferri Maia Angelova Xiaohui Zhao Patrick J. Owen Clint T. Miller Tim Wilkin Daniel L. Belavy |
spellingShingle |
Scott D. Tagliaferri Maia Angelova Xiaohui Zhao Patrick J. Owen Clint T. Miller Tim Wilkin Daniel L. Belavy Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews npj Digital Medicine |
author_facet |
Scott D. Tagliaferri Maia Angelova Xiaohui Zhao Patrick J. Owen Clint T. Miller Tim Wilkin Daniel L. Belavy |
author_sort |
Scott D. Tagliaferri |
title |
Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews |
title_short |
Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews |
title_full |
Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews |
title_fullStr |
Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews |
title_full_unstemmed |
Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews |
title_sort |
artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews |
publisher |
Nature Publishing Group |
series |
npj Digital Medicine |
issn |
2398-6352 |
publishDate |
2020-07-01 |
description |
Abstract Artificial intelligence and machine learning (AI/ML) could enhance the ability to detect patterns of clinical characteristics in low-back 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 sub-groups, 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. |
url |
https://doi.org/10.1038/s41746-020-0303-x |
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