Metode Baru Semi Otomatis Berbasis Active Shape Model Untuk Penentuan Tingkat Keparahan Osteoarthritis Lutut

Wahyuningrum, Rima Tri (2020) Metode Baru Semi Otomatis Berbasis Active Shape Model Untuk Penentuan Tingkat Keparahan Osteoarthritis Lutut. Doctoral thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Osteoarthritis (OA) merupakan penyakit arthritis yang sering ditemukan di masyarakat. Beberapa penelitian telah menetapkan bahwa Osteoarthritis (OA) ditemukan pada orang berusia 40-60 tahun dengan prevalensi 15,5% pada pria dan 12,7% pada wanita. Penyakit ini secara bertahap dapat menjadi kronis jika tidak dikontrol secara klinis dengan tepat. Gejala OA ditandai oleh nyeri sendi dan gangguan gerakan karena kerusakan tulang rawan. OA sering terjadi pada persendian lutut, pinggul, tulang belakang, dan kaki. OA dapat terjadi dengan etiologi yang berbeda-beda, namun mengakibatkan kelainan biologis, morfologis dan luaran klinis yang sama. Sehingga, studi mengenai OA dapat dilakukan secara multidisiplin. Identifikasi awal OA memainkan peran penting untuk pengambilan keputusan klinis, mendeteksi keparahan OA dan pengobatan yang tepat. Penyelarasan tulang femur-tibia merupakan faktor risiko utama untuk kejadian dan perkembangan knee osteoarthritis (KOA). Selain itu, untuk menentukan tingkat keparahan OA lutut dapat dilakukan dengan mengklasifikasikan tingkat keparahan berdasarkan KL grade dan Femur Tibia Angle (FTA). Pada penelitian ini, dilakukan penentuan tingkat keparahan OA lutut menggunakan beberapa metode hibrida baru pada citra radiografi (x-ray) yang diperoleh dari Osteoarthritis Initiative (OAI) database. Metode hibrida baru yang dimaksud adalah Structural Two Dimentional Principal Component Analysis (S2DPCA) + Support Vector Machine (SVM) dan Convolutional Neural Network (CNN) + Long Short Term Memory (LSTM) untuk penentuan tingkat keparahan OA lutut berdasarkan KL Grade serta Active Shape Model (ASM) untuk penentuan sudut femur dan tibia (FTA) secara semi otomatis. Hasil ujicoba pada metode S2DPCA + SVM menggunakan 80 citra x-ray lutut dengan ujicoba berdasarkan four-fold cross validation diperoleh hasil maksimal sebesar 94,33% untuk grade 0 dengan kernel Gaussian. Sedangkan untuk metode CNN + LSTM menggunakan 1530 citra x-ray lutut (1055 citra pelatihan dan 475 citra pengujian) dengan ujicoba berdasarkan three-fold cross validation diperoleh hasil maksimal sebesar 75,28% untuk rata-rata dari grade 0 – 4. Sementara itu, untuk metode ASM pada penentuan FTA yang dikembangkan (disebut TRIMA-FTA) dengan menggunakan 60 citra x-ray lutut (10 citra pelatihan dan 50 citra pengujian) diperoleh perbedaan hasil pengukuran antara FTA OAI dan FTA yang cukup kecil, yaitu rata-rata di bawah 0,810 untuk FTA kanan dan di bawah 0,770 untuk FTA kiri. Hasil-hasil tersebut menunjukkan bahwa pendekatan-pendekatan baru yang dikembangkan secara klinis cocok untuk penentuan tingkat keparahan OA lutut berdasarkan KL grade dan pengukuran FTA secara semi otomatis.
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Osteoarthritis (OA) is an arthritis diseases that was found commonly in society. Numerous investigations have established that Osteoarthritis (OA) were found in people aged 40-60 years with a prevalence of 15.5% in men and 12.7% in women. This disease gradually became a chronic if not correctly clinical controlled. OA’s symptom is characterized by joint pain and movement disorders due to damage of the cartilage. OA often occurs in the joints of knees, hips, spines and feets. OA can be occur with different etiologies, but the results could be same in biological, morphological and clinical outcomes. Thus, studies on OA can be done in a multidisciplinary science. Early identification of OA plays an important role to improve clinical decision making, to monitor disease progress and appropriate treatment. Some researchers have conducted studies on the severity of knee OA based on Kellgren Lawrence (KL) Grade, Joint Space Width (JSW), osteophyte formation, as well as the angle between the femur bone and the tibia / Femur Tibia Angle (FTA). Furthermore, alignment of the femur-tibia bone is a major risk factor for the occurrence and development of knee osteoarthritis (KOA). In addition, to determine the severity of knee OA can be done by classifying the severity based on KL grade and Femur Tibia Angle (FTA). In this study, the severity of knee OA was determined using several new hybrid methods on radiographic (x-ray) images obtained from the Osteoarthritis Initiative (OAI) database. The new hybrid method is Structural Two Dimentional Principal Component Analysis (S2DPCA) + Support Vector Machine (SVM) and Convolutional Neural Network (CNN) + Long Short Term Memory (LSTM) for determining the severity of knee OA based on KL Grade and Active Shape Model (ASM) for semi-automatic determination of the angle of the femur and tibia (FTA). The results of trials on the S2DPCA + SVM method using 80 knee x-ray images with trials based on four-fold cross validation obtained maximum results of 94.33% for grade 0 with Gaussian kernel. Whereas the CNN + LSTM method uses 1530 knee x ray images (1055 training images and 475 test images) with trials based on three-fold cross validation, the maximum results are 75.28% for an average of grades 0 - 4. Meanwhile , for the ASM method in determining the developed FTA (called TRIMA FTA) using 60 knee x-ray images (10 training images and 50 test images), the difference in measurement results between FTA OAI and FTA is quite small, namely the average in below 0.810 for the right FTA and below 0.770 for the left FTA. These results indicate that new clinically developed approaches are suitable for determining the severity of knee OA based on KL grade and semi-automatic FTA measurements.

Item Type:	Thesis (Doctoral)
Additional Information:	RDE 621.367 Wah m-1 2020
Uncontrolled Keywords:	Osteoarthritis lutut, KL grade, Active Shape Model, Femur Tibia Angle
Subjects:	Q Science Q Science > QA Mathematics > QA278.5 Principal components analysis. Factor analysis. Correspondence analysis (Statistics) Q Science > QA Mathematics > QA336 Artificial Intelligence
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20001-(S3) PhD Thesis
Depositing User:	Rima Tri Wahyuningrum
Date Deposited:	14 Mar 2025 03:03
Last Modified:	14 Mar 2025 03:03
URI:	http://repository.its.ac.id/id/eprint/75386

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