Literature DB >> 10913774

Automated scoring of patient pain drawings using artificial neural networks: efforts toward a low back pain triage application.

N W Sanders1, N H Mann.   

Abstract

The goal of this research was to examine methods of automatically scoring patient pain drawings. Two hundred and fifty pain drawings were selected from the files of an orthopaedic surgeon who specializes in the treatment of low back pain patients. An artificial neural network was designed to score these drawings. The drawings were segmented into 85 regions following dermatomal mappings and from these regions the percent area in pain in each was computed and used as the neural network input variables. With five outcome categories (scores) we obtained a classification sensitivity of 49%, which is approximately as well as physician experts and discriminant analysis achieved using a subset of the same data. We conclude that an artificial neural network is well suited to automatically score patient pain drawings.

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Year:  2000        PMID: 10913774     DOI: 10.1016/s0010-4825(00)00013-5

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  8 in total

1.  Reliability of a preliminary 3-D pain mapping program.

Authors:  Robert N Jamison; Tabitha A Washington; Padma Gulur; Gilbert J Fanciullo; John R Arscott; Gregory J McHugo; John C Baird
Journal:  Pain Med       Date:  2011-01-28       Impact factor: 3.750

2.  [Psychometric properties of the pain drawing and the Ransford technique in patients with chronic low back pain].

Authors:  M Pfingsten; M Baller; H Liebeck; J Strube; J Hildebrandt; P Schöps
Journal:  Schmerz       Date:  2003-10       Impact factor: 1.107

3.  Reproducibility of pain manikins: a comparison of paper versus online questionnaires.

Authors:  Gareth T Jones; Ramona Kyabaggu; Debbi Marais; Gary J Macfarlane
Journal:  Br J Pain       Date:  2013-08

Review 4.  Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review.

Authors:  Federico D'Antoni; Fabrizio Russo; Luca Ambrosio; Luca Bacco; Luca Vollero; Gianluca Vadalà; Mario Merone; Rocco Papalia; Vincenzo Denaro
Journal:  Int J Environ Res Public Health       Date:  2022-05-14       Impact factor: 4.614

5.  Computer-aided surface estimation of pain drawings - intra- and inter-rater reliability.

Authors:  Ann L Persson; Sofia Garametsos; Jonna Pedersen
Journal:  J Pain Res       Date:  2011-05-15       Impact factor: 3.133

6.  The pain drawing as an instrument for identifying cervical spine nerve involvement in chronic whiplash-associated disorders.

Authors:  Gabriella Bernhoff; Maria Landén Ludvigsson; Gunnel Peterson; Bo Christer Bertilson; Madeleine Elf; Anneli Peolsson
Journal:  J Pain Res       Date:  2016-06-13       Impact factor: 3.133

7.  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

8.  Novel Software for Pain Drawing Analysis.

Authors:  Asimakis K Kanellopoulos; Emmanouil K Kanellopoulos; Zacharias Dimitriadis; Nikolaos S Strimpakos; Andriana Koufogianni; Anthi A Kellari; Ioannis A Poulis
Journal:  Cureus       Date:  2021-12-14
  8 in total

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