COMPARISON OF SELECTED CLASSIFICATION METHODS BASED ON MACHINE LEARNING AS A DIAGNOSTIC TOOL FOR KNEE JOINT CARTILAGE DAMAGE BASED ON GENERATED VIBROACOUSTIC PROCESSES
Robert KARPIŃSKI
r.karpinski@pollub.plDepartment of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, (Poland)
https://orcid.org/0000-0003-4063-8503
Przemysław KRAKOWSKI
Medical University of Lublin, Chair and Department of Traumatology and Emergency Medicine, Staszica 11, 20-081 Lublin, Poland, przemyslawkrakowski@umlub.pl, Orthopaedic and Sports Traumatology Department, Carolina Medical Center, Pory 78, 02-757, Warsaw, Poland (Poland)
Józef JONAK
Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin, Poland, (Poland)
Anna MACHROWSKA
Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin, Poland, (Poland)
Marcin MACIEJEWSKI
Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Institute of Electronics and Information Technology, Nadbystrzycka 36, 20-618 Lublin, Poland, (Poland)
Abstract
Osteoarthritis is one of the most common cause of disability among elderly. It can affect every joint in human body, however, it is most prevalent in hip, knee, and hand joints. Early diagnosis of cartilage lesions is essential for fast and accurate treatment, which can prolong joint function. Available diagnostic methods include conventional X-ray, ultrasound and magnetic resonance imaging. However, those diagnostic modalities are not suitable for screening purposes. Vibroarthrography is proposed in literature as a screening method for cartilage lesions. However, exact method of signal acquisition as well as classification method is still not well established in literature. In this study, 84 patients were assessed, of whom 40 were in the control group and 44 in the study group. Cartilage status in the study group was evaluated during surgical treatment. Multilayer perceptron - MLP, radial basis function - RBF, support vector method - SVM and naive classifier – NBC were introduced in this study as classification protocols. Highest accuracy (0.893) was found when MLP was introduced, also RBF classification showed high sensitivity (0.822) and specificity (0.821). On the other hand, NBC showed lowest diagnostic accuracy reaching 0.702. In conclusion vibroarthrography presents a promising diagnostic modality for cartilage evaluation in clinical setting with the use of MLP and RBF classification methods.
Keywords:
articular cartilage, Artificial Intelligence, RBF, MLP, SVM, knee jointReferences
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Authors
Robert KARPIŃSKIr.karpinski@pollub.pl
Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
https://orcid.org/0000-0003-4063-8503
Authors
Przemysław KRAKOWSKIMedical University of Lublin, Chair and Department of Traumatology and Emergency Medicine, Staszica 11, 20-081 Lublin, Poland, przemyslawkrakowski@umlub.pl, Orthopaedic and Sports Traumatology Department, Carolina Medical Center, Pory 78, 02-757, Warsaw, Poland Poland
Authors
Józef JONAKLublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin, Poland, Poland
Authors
Anna MACHROWSKALublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin, Poland, Poland
Authors
Marcin MACIEJEWSKILublin University of Technology, Faculty of Electrical Engineering and Computer Science, Institute of Electronics and Information Technology, Nadbystrzycka 36, 20-618 Lublin, Poland, Poland
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