The Role of Muscle and Tendon in Predicting Cartilage Degeneration and Tendinopathy

Zakia Khatun, Mariella Tsirilaki, Alessia Lindemann, Francesco Tortorella, Nicola Maffulli, Halldór Jónsson, Paolo Gargiulo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This study is part of a European Union project called P4-FIT, whose aim is innovation in tendon repair. The main objective of this study was to understand the interplay between knee cartilage, quadriceps muscle, quadriceps tendon and patellar tendon. The dataset of another European Union project called RESTORE (http://restoreproject.ew was used to access the CT and MRI scans of patients with knee cartilage degeneration. To gain a better understanding of the interaction between cartilage, quadriceps and patellar tendons, several sets of features were extracted in five different categories, namely Gray-level co-occurrence matrix, Amount of fat and water containing tissues present in quadriceps and patellar tendons, Tendon thickness, Profile line analysis and Radiodensity (HU). For feature extraction, quadriceps and patellar tendons were used as the regions of interest (ROIs). Using different sets and combination of these features, different classifiers were trained to perform two distinct classification tasks. The first classifier determined whether there was degeneration of the knee cartilage, while the second one determined whether the patellar tendons were tendinopathic. To predict cartilage degeneration, some of the most important features were age, the total number of patellar tendon containing pixels, number of quadriceps and patellar tendon pixels containing water etc. Using these features, our best classifier model achieved an accuracy of 89.4% for cartilage degeneration prediction. Whereas the fat containing pixels of quadriceps and patellar tendons were two of the significant features to predict patellar tendon involvement in tendinopathic processes. Only the sets of important features were used to obtain the best result for predicting patellar tendinopathy, which resulted in an accuracy of 83%. To our best knowledge, this is the first study to show that even without using any information about the knee bone and cartilage themselves, quadriceps and patellar tendons alone may play a powerful role in predicting knee cartilage degeneration and patellar tendinopathy. Throughout our investigation, we also found that the total amount of water and fatty tissue in the quadriceps and patellar tendons plays important role in predicting such outcomes.

Original languageEnglish
Title of host publication2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages289-294
Number of pages6
ISBN (Electronic)9781665485746
DOIs
Publication statusPublished - 2022
Event1st IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Rome, Italy
Duration: 26 Oct 202228 Oct 2022

Publication series

Name2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings

Conference

Conference1st IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022
Country/TerritoryItaly
CityRome
Period26/10/2228/10/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Other keywords

  • Cartilage
  • Classification
  • CT
  • Degeneration
  • ML
  • MRI
  • Muscle
  • Segmentation
  • Tendinopathy
  • Tendon

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