Literature DB >> 1826553

Artificial intelligence in the diagnosis of low back pain.

N H Mann1, M D Brown.   

Abstract

Computerized methods are used to recognize the characteristics of patient pain drawings. Artificial neural network (ANN) models are compared with expert predictions and traditional statistical classification methods when placing the pain drawings of low back pain patients into one of five clinically significant categories. A discussion is undertaken outlining the differences in these classifiers and the potential benefits of the ANN model as an artificial intelligence technique.

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Mesh:

Year:  1991        PMID: 1826553

Source DB:  PubMed          Journal:  Orthop Clin North Am        ISSN: 0030-5898            Impact factor:   2.472


  6 in total

Review 1.  Modeling paradigms for medical diagnostic decision support: a survey and future directions.

Authors:  Kavishwar B Wagholikar; Vijayraghavan Sundararajan; Ashok W Deshpande
Journal:  J Med Syst       Date:  2011-10-01       Impact factor: 4.460

2.  Neural network based on adaptive resonance theory as compared to experts in suggesting treatment for schizophrenic and unipolar depressed in-patients.

Authors:  I Modai; A Israel; S Mendel; E L Hines; R Weizman
Journal:  J Med Syst       Date:  1996-12       Impact factor: 4.460

Review 3.  A vision for the future of wearable sensors in spine care and its challenges: narrative review.

Authors:  Paul W Hodges; Wolbert van den Hoorn
Journal:  J Spine Surg       Date:  2022-03

Review 4.  An Evolution Gaining Momentum-The Growing Role of Artificial Intelligence in the Diagnosis and Treatment of Spinal Diseases.

Authors:  Andre Wirries; Florian Geiger; Ludwig Oberkircher; Samir Jabari
Journal:  Diagnostics (Basel)       Date:  2022-03-29

Review 5.  Artificial intelligence in medicine and male infertility.

Authors:  D J Lamb; C S Niederberger
Journal:  World J Urol       Date:  1993       Impact factor: 4.226

6.  From Paper to Digital Applications of the Pain Drawing: Systematic Review of Methodological Milestones.

Authors:  Nour Shaballout; Till-Ansgar Neubert; Shellie Boudreau; Florian Beissner
Journal:  JMIR Mhealth Uhealth       Date:  2019-09-05       Impact factor: 4.773

  6 in total

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