Fuzzy Expert System Prediction of Lumbar Spine Subchondral Sclerosis and Lumbar Disk Hernia

Gal, Norbert; Andrei, Diana; Stoicu-Tivadar, Vasile; Nemes, D.; Nădăşan, Emanuela

doi:10.1007/978-3-319-18296-4_21

Cited by 2 publications

(4 citation statements)

References 8 publications

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“…Based on that information, doctors decide on the most appropriate therapeutic plan, including ergonomic education, exercises, and medication. ML could help either in the diagnosis [32, 34, 35, 37-39, 41, 42] or in supporting doctors' decisions [33,40] or guiding patients through a personalized therapeutic plan [29,31,36], with accuracy and in real-time, giving important clinical information to doctors and therapists and allowing adherence monitoring. ML could also be useful to identify patients' response to functional rehabilitation [30], automatic recognition of fear-avoidance behaviour [41,42] or to categorize patients into different subgroups of LBP risk [37].…”

Section: Discussionmentioning

confidence: 99%

“…Visual Analogue Scale or Oswestry Low Back Pain Questionnaire are examples of most used tools for pain assessment and disability in LBP patients [30,37]. Depression scales are also used in LBP research [35], as well as daily activity questionnaires [33,40]. A more holistic approach is the one given by International Classification of Functioning, Disability and Health (ICF), a standardized reference system for classifying health by accounting for functioning [47].…”

Section: Discussionmentioning

confidence: 99%

“…Based on that fact, some studies used ML techniques to accurately locate the symptomatic muscles after collecting sEMG [32]. ML can also be used in the development of an assessment system based on a modified fuzzy inference system consisting in a determined number of rules, which could be used to support treatment decisions [33,40].…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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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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“…A fuzzy expert system was developed by Ohri et al [14] to diagnose breast cancer. Gal et al [15] proposed a fuzzy expert system to predict subchondral sclerosis. In the study of Katigari et al [16], a fuzzy expert system was presented to diagnose diabetic neuropathy.…”

Section: Introductionmentioning

confidence: 99%

Type-2 Fuzzy Rule-Based Expert System for Diagnosis of Spinal Cord Disorders

et al. 2018

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The majority of people have experienced pain in their low back or neck in their lives. In this paper, a type-2 fuzzy rule-based expert system is presented for diagnosing the spinal cord disorders. The interval type-2 fuzzy logic system permits us to handle the high uncertainty of diagnosing the type of disorder and its severity. The spinal cord disorders are studied in ve categories using historical data and clinical symptoms of the patients. The main novelty of this paper lies in presentation of the interval type-2 fuzzy hybrid rule-based system, which is a combination of the forward and backward chaining approaches in its inference engine and avoids unnecessary medical questions. Use of parametric operations for fuzzy calculations increases the robustness of the system and the compatibility of the diagnosis with a wide range of physicians' diagnosis. The outputs of the system are comprised of type of disorder, location, and severity as well as the necessity of taking an M.R. Image. A comparison of the performance of the developed system with the expert shows an acceptable accuracy of the system in diagnosing the disorders and determining the necessity of the M.R. Image.

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Fuzzy Expert System Prediction of Lumbar Spine Subchondral Sclerosis and Lumbar Disk Hernia

Cited by 2 publications

References 8 publications

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

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

Type-2 Fuzzy Rule-Based Expert System for Diagnosis of Spinal Cord Disorders

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