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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Autores principales:	Scott D. Tagliaferri, Maia Angelova, Xiaohui Zhao, Patrick J. Owen, Clint T. Miller, Tim Wilkin, Daniel L. Belavy
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/9092303b7bbc4ef1b1ffb87ef1d96762
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spelling	oai:doaj.org-article:9092303b7bbc4ef1b1ffb87ef1d967622021-12-02T16:15:03ZArtificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews10.1038/s41746-020-0303-x2398-6352https://doaj.org/article/9092303b7bbc4ef1b1ffb87ef1d967622020-07-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0303-xhttps://doaj.org/toc/2398-6352Abstract 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.Scott D. TagliaferriMaia AngelovaXiaohui ZhaoPatrick J. OwenClint T. MillerTim WilkinDaniel L. BelavyNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-16 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 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
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.
format	article
author	Scott D. Tagliaferri Maia Angelova Xiaohui Zhao Patrick J. Owen Clint T. Miller Tim Wilkin Daniel L. Belavy
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 Portfolio
publishDate	2020
url	https://doaj.org/article/9092303b7bbc4ef1b1ffb87ef1d96762
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Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

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