Monitoring of the Achilles tendon healing process: can artificial intelligence be helpful?

• Autorzy: Kapiński Norbert , Zieliński Jakub , Borucki Bartosz A. , Trzciński Tomasz , Ciszkowska-Łysoń Beata , Zdanowicz Urszula , Śmigielski Robert , Nowiński Krzysztof S.

Identyfikatory

DOI

10.5277/ABB-01240-2018-04

• Języki publikacji: EN

Abstrakty

EN

The aim of this study was to verify improved, ensemble-based strategy for inferencing with use of our solution for quantitative assessment of tendons and ligaments healing process and to show possible applications of the method. Methods: We chose the problem of the Achilles tendon rupture as an example representing a group of common sport traumas. We derived our dataset from 90 individuals and divided it into two subsets: healthy individuals and patients with complete Achilles tendon ruptures. We computed approx. 160 000 2D axial cross-sections from 3D MRI studies and preprocessed them to create a suitable input for artificial intelligence methods. Finally, we compared different training methods for chosen approaches for quantitative assessment of tendon tissue healing with the use of statistical analysis. Results: We showed improvement in inferencing with use of the ensemble technique that results from achieving comparable accuracy of 99% for our previously published method trained on 500 000 samples and for the new ensemble technique trained on 160 000 samples. We also showed real-life applications of our approach that address several clinical problems: (1) automatic classification of healthy and injured tendons, (2) assessment of the healing process, (3) a pathologic tissue localization. Conclusions: The presented method enables acquiring comparable accuracy with less training samples. The applications of the method presented in the paper as case studies can facilitate evaluation of the healing process and comparing with previous examination of the same patient as well as with other patients. This approach might be probably transferred to other musculoskeletal tissues and joints.

Słowa kluczowe

EN

tendon traumas; magnetic resonance; medical imaging; artificial intelligence; deep learning

PL

rezonans magnetyczny; obrazowanie medyczne; sztuczna inteligencja

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Acta of Bioengineering and Biomechanics
• Rocznik: 2019
• Tom: Vol. 21, no. 1
• Strony: 103--111
• Opis fizyczny: Bibliogr. 19 poz., rys., tab., wykr.

Twórcy

autor

Kapiński Norbert

• Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Poland

autor

Zieliński Jakub

• Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Poland.
• Department of Biophysics and Human Physiology, Medical University of Warsaw, Poland.

autor

Borucki Bartosz A.

• Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Poland

autor

Trzciński Tomasz

• Faculty of Electronics and Information Technology, Warsaw University of Technology, Poland
• Tooploox, Wrocław, Poland

autor

Ciszkowska-Łysoń Beata

• Carolina Medical Center, Warsaw, Poland

autor

Zdanowicz Urszula

• Carolina Medical Center, Warsaw, Poland

autor

Śmigielski Robert

• MIRAI Institute: Trauma, Orthopaedics, Physical Therapy, Warsaw, Poland
• ARS MEDICINALIS Foundation, Warsaw, Poland

autor

Nowiński Krzysztof S.

• Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Poland

Bibliografia

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Uwagi

The following work was part of “Novel Scaffold-based Tissue Engineering Approaches to Healing and Regeneration of Tendons and Ligaments (START)” project, co-funded by The National Centre for Research and Development (Poland) within STRATEGMED programme (STRATEGMED1/233224/10/NCBR/2014).

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).