AI-driven solutions for low back pain: A pilot study on diagnosis and treatment planning
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Agrinazio Geraldo Nascimento Neto 1 , Sávia Denise Silva Carlotto Herrera 1 , Rodrigo Moura 2 , Graciele Moura Cielo 3 , Fábio Pegoraro 1 , Valmir Fernandes de Lira 1 , Maykon Jhuly Martins de Paiva 4 , Carlos Gustavo Sakuno Rosa 4 , Rafaela Carvalho Alves 1 , Walmirton Bezerra D’Alessandro 4 *
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1 University of Gurupi –UNIRG, Av. Rio de Janeiro, Nº 1585 -St. Central, Gurupi -TO, 77403-090, BRAZIL2 Institute of Education and Research Santa Casa: R. Domingos Vieira, 590 - Santa Efigênia, Belo Horizonte - MG, 30150-240, BRAZIL3 MEDME.CARE®, Belo Horizonte, BRAZIL4 University of Gurupi –UNIRG, St. Oeste, Paraíso do Tocantins - TO, 77600-000, BRAZIL* Corresponding Author

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.

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Article Type: Original Article

ELECTRON J GEN MED, Volume 21, Issue 5, October 2024, Article No: em601

https://doi.org/10.29333/ejgm/14934

Publication date: 01 Sep 2024

Online publication date: 10 Aug 2024

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