AI-driven solutions for low back pain: A pilot study on diagnosis and treatment planning-Reference-Cited by-同舟云学术

AI-driven solutions for low back pain: A pilot study on diagnosis and treatment planning

Published:2024-09-01 Issue:5 Volume:21 Page:em601
ISSN:2516-3507
Container-title:Electronic Journal of General Medicine
language:
Short-container-title:ELECTRON J GEN MED

Author:

Neto Agrinazio Geraldo Nascimento¹^ORCID,Herrera Sávia Denise Silva Carlotto¹^ORCID,Moura Rodrigo²^ORCID,Cielo Graciele Moura³^ORCID,Pegoraro Fábio¹^ORCID,Lira Valmir Fernandes de¹^ORCID,Paiva Maykon Jhuly Martins de⁴^ORCID,Rosa Carlos Gustavo Sakuno⁴^ORCID,Alves Rafaela Carvalho¹^ORCID,D’Alessandro Walmirton Bezerra⁴^ORCID

Affiliation:

1. University of Gurupi –UNIRG, Av. Rio de Janeiro, Nº 1585 -St. Central, Gurupi -TO, 77403-090, BRAZIL

2. Institute of Education and Research Santa Casa: R. Domingos Vieira, 590 - Santa Efigênia, Belo Horizonte - MG, 30150-240, BRAZIL

3. MEDME.CARE®, Belo Horizonte, BRAZIL

4. University of Gurupi –UNIRG, St. Oeste, Paraíso do Tocantins - TO, 77600-000, BRAZIL

Abstract

Low back pain (LBP) mainly affects the working-age population, and few specific causes can be identified, making diagnosis difficult and rendering them nonspecific. Artificial intelligence (AI) can be a great ally for prognosis, diagnosis, and treatment plans in healthcare. To describe the development of software aimed at providing prognoses, diagnoses, and treatment suggestions for LBP with AI support, as well as to report the functionality and initial limitations through a pilot study. Fifty assessment records from a database of patients at the Physiotherapy School Clinic of the University of Gurupi-UnirG, who were treated for LBP, were analyzed. Using data mining, including information described by patients and post-processing of discovered anamnesis patterns (rules), it was possible to develop software for evaluation and intervention in this patient group. Subsequently, a pilot study was initiated with 34 patients residing in the city of Gurupi-TO to test the application’s functionality. The software enabled more accurate treatments, diagnoses, and prognoses during the pilot study, directing the patient towards physiotherapeutic intervention based on the presented condition.

Publisher

Modestum Ltd

Link

https://www.ejgm.co.uk/download/ai-driven-solutions-for-low-back-pain-a-pilot-study-on-diagnosis-and-treatment-planning-14934.pdf

Reference12 articles.

1. WHO. Low back pain. Word Health Organization; 2023. Available at: https://www.who.int/news-room/fact-sheets/detail/low-back-pain (Accessed: 5 May 2024).

2. GBD. GBD 2019: Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: A systematic analysis for the global burden of disease study 2019. GBD; 2019. Available at: https://vizhub.healthdata.org/gbd-results/ (Accessed: 5 May 2024).

3. GBD 2021 Low Back Pain Collaborators. Global, regional, and national burden of low back pain, 1990-2020, its attributable risk factors, and projections to 2050: A systematic analysis of the global burden of disease study 2021. Lancet Rheumatol. 2023:5(6):e316-29. https://doi.org/10.1016/S2665-9913(23)00098-X PMid:37273833

4. Dzakpasu FQS, Carver A, Brakenridge CJ, et al. Musculoskeletal pain and sedentary behaviour in occupational and non-occupational settings: A systematic review with meta-analysis. Int J Behav Nutr Phys Act. 2021;18(1):159. https://doi.org/10.1186/s12966-021-01191-y PMid:34895248 PMCid:PMC8666269

5. Dziedzinski AT, Johnston C, Zardo E. Perfil epidemiológico dos pacientes com dor lombar que procuram o serviço de traumatologia e ortopedia do Hsl-PUC-RS [Perfil epidemiológico dos pacientes com dor lombar que procuram o serviço de traumatologia e ortopedia do Hsl-PUC- RS]. Rio Grande Sul. 2005;13(12):453-8.