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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.
Czasopismo
Rocznik
Tom
Strony
103--111
Opis fizyczny
Bibliogr. 19 poz., rys., tab., wykr.
Twórcy
autor
- Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Poland
autor
- Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Poland.
- Department of Biophysics and Human Physiology, Medical University of Warsaw, Poland.
autor
- Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Poland
autor
- Faculty of Electronics and Information Technology, Warsaw University of Technology, Poland
- Tooploox, Wrocław, Poland
autor
- Carolina Medical Center, Warsaw, Poland
autor
- Carolina Medical Center, Warsaw, Poland
autor
- MIRAI Institute: Trauma, Orthopaedics, Physical Therapy, Warsaw, Poland
- ARS MEDICINALIS Foundation, Warsaw, Poland
autor
- Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Poland
Bibliografia
- [1] BAHRAMPOUR S., NAVEEN RAMAKRISHNAN, SCHOTT L., SHAH M., Comparative Study of Caffe, Neon, Theano, and Torch for Deep Learning, Computer Research Repository, 2015, abs/1511.06435.
- [2] ESTEVA A., KUPREL B., NOVOA R.A., KO J., SWETTER S.M., BLAU H.M., THRUN S., Dermatologist-level classification of skin cancer with deep neural networks, Nature, 2017, Vol. 542, No. 7639, 115–118.
- [3] GLASSER M.F., COALSON T.S., ROBINSON E.C., HACKER C.D., HARWELL J., YACOUB E., UGURBIL K., ANDERSSON J., BECKMANN C.F., JENKINSON M., SMITH S.M., VAN ESSEN D.C., A multi-modal parcellation of human cerebral cortex, Nature, 2016, Vol. 536, No. 7615, 171–178.
- [4] GOEL A., SRIVASTAVA S.K., Role of Kernel Parameters in Performance Evaluation of SVM, Second International Conference on Computational Intelligence & Communication Technology, Ghaziabad, 2016, 166–169.
- [5] GULSHAN V., PENG L., CORAM M., STUMPE M.C., WU D., NARAYANASWAMY A., VENUGOPALAN S., WIDNER K., MADAMS T., CUADROS J., KIM R., RAMAN R., NELSON P.Q., MEGA J., WEBSTER D., Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, Journal of the American Medical Association, 2016, arXiv:1803.04337.
- [6] HE K., ZHANG X., REN S., SUN J., Deep residual learning for image recognition, Conference on Computer Vision and Pattern Recognition, 2016, 770–778.
- [7] KAPIŃSKI N., ZIELIŃSKI J., BORUCKI B., NOWIŃSKI K.S., MRI-based deep learning for in-situ monitoring of achilles tendon regeneration process, International Journal of Computer Assisted Radiology and Surgery, 2017, Vol. 12, 57–58.
- [8] KAPIŃSKI N., ZIELIŃSKI J., BORUCKI B., TRZCIŃSKI T., CISZKOWSKA-LYSON B., Estimating Achilles tendon healing progress with convolutional neural networks, Proceedings of the Medical Image Computing and Computer Assisted Intervention, 2018, arXiv:1806.05091.
- [9] KEARNEY R., ACHTEN J., LAMB S., PARSONS N., COSTA M.L., The Achilles tendon total rupture score: a study of responsiveness, internal consistency and convergent validity on patients with acute Achilles tendon ruptures, Health and quality of life outcomes, 2012, Vol. 10, 24–29.
- [10] KRIZHEVSKY A., SUTSKEVER I., HINTON G.E., Imagenet classification with deep convolutional neural networks, Conference on Neural Information Processing Systems, 2012, 25, 1097–1105.
- [11] MILLETARI F., NAVAB N., V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation, Computer Research Repository, 2016, http://arxiv.org/abs/1606.04797.
- [12] NOWIŃSKI K.S., BORUCKI B., VisNow a Modular, Extensible Visual Analysis Platform, Proc. of 22nd Int. Conf. in Central Europe on Computer Graphics, Visualization and Computer Vision WSCG2014, 2014, 73–76.
- [13] RAIKIN S., GARRAS D.N., KRAPCHEV P.V., Achilles Tendon Injuries in a United States Population, Foot & Ankle International, 2013, 34, 475–480.
- [14] RAJPURKAR P., HANNUN A.Y., HAGHPANAHI M., BOURN C., NG A.Y., Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks, Computer Research Repository, 2017, Vol. abs/1707.01836.
- [15] ROBINSON J.M., COOK J.L., PURDAM C., VISENTINI P., ROSS J., MAFFULLI N., TAUNTON J., KHAN K., The VISA-A questionnaire: a valid and reliable index of the clinical severity of Achilles tendinopathy, British Journal of Sports Medicine, 2001, Vol. 35, 335–341.
- [16] ROOS E.M., BRANDSSON S., KARLSSON J., Validation of the Foot and Ankle Outcome Score for Ankle Ligament Reconstruction, Foot & Ankle International, 2001, Vol. 22, 788–794.
- [17] SARRAF S., TOFIGHI G., Deep learning-based pipeline to recognize alzheimer’s disease using fmri data, IEEE Future Technologies Conference, 2016, 816–820.
- [18] WANG D., KHOSLA A., GARGEYA R., IRSHAD H., BECK A.H., Deep Learning for Identifying Metastatic Breast Cancer, Computer Research Repository, 2017, arXiv:1606.05718.
- [19] ZHANG W., DOI K., GIGER M.L., WU Y., NISHIKAWA R.M., SCHMIDT R.A., Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network, Medical Physics, 1994, Vol. 21, 517–524.
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).
Typ dokumentu
Bibliografia
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