alexa A Novel Software Tool for Detection of Meniscus Injury using Dynamic Fuzzy Cognitive Networks | OMICS International| Abstract
ISSN: 2573-0312

Journal of Physiotherapy & Physical Rehabilitation
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  • Research Article   
  • Physiother Rehabil 2018, Vol 3(1): 155
  • DOI: 10.4172/2573-0312.1000155

A Novel Software Tool for Detection of Meniscus Injury using Dynamic Fuzzy Cognitive Networks

Anninou AP1*, Poulios P2, Groumpos PP1 and Gliatis I2
1Department of Electrical and Computer Engineering, Laboratory of Automation and Robotics, University of Patras, Greece
2Department of Medicine, University of Patras, Greece
*Corresponding Author : Anninou AP, Department of Electrical and Computer Engineering, Laboratory of Automation and Robotics, University of Patras, 26504, Patras, Greece, Tel: +302610996450, Email: [email protected]

Received Date: Jan 30, 2018 / Accepted Date: Mar 06, 2018 / Published Date: Mar 12, 2018

Abstract

Objective: The main objective of this study is the construction of a tool to diagnose a specific type of knee injuries, the meniscus injury, without the aid of imaging modalities. This tool exploits physician’s knowledge and experience and extracts all information needed for a diagnostic procedure.
Methods: A specific type of Fuzzy Cognitive Maps, which is named Dynamic Fuzzy Cognitive Networks, is used for the implementation of diagnostic process. This method simulates the physician’s way of thinking and making decisions in order to conclude to a final diagnosis and will constitute the background of a user interface platform, named “KneeD,” designed especially as a supportive tool for the physician. It exploits information about patient history and clinical examination and the arisen symptoms and risk factors constitute the main attributes that contribute to injury identification.
Results: A pilot sample of 17 patients with knee injuries who arrived at the University Hospital of Patras, in Greece, was examined by the orthopedic clinician and the diagnoses confirmed with MRI were compared to our platform’s outcomes. Results concerned a) the initial distinction between “meniscus injury” and “other disease” and b) discrimination between acute and degenerative injury for patients positive to meniscal tear from the first level. Both levels provided us with very satisfactory results in total agreement with MRI outcomes.
Conclusion: The outcome of a close collaboration between engineers and medical doctors was a platform for real time diagnosing of knee injuries, which is simple, user friendly, real-time, easily accessible, fast, reliable, and low-cost. Furthermore, it could also be used as a patient storage database. The obtained results have been evaluated by orthopedic surgeons who found them very satisfactory.

Keywords: Clinical diagnosis; Knee injuries; Meniscus; Dynamic fuzzy cognitive networks; Decision making; Platform; Database

Citation: Anninou AP, Poulios P, Groumpos PP, Gliatis I (2018) A Novel Software Tool for Detection of Meniscus Injury using Dynamic Fuzzy Cognitive Networks. Physiother Rehabil 3: 155. Doi: 10.4172/2573-0312.1000155

Copyright: ©2018 Anninou AP, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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