Novel Deep Learning Method in Hip Osteoarthritis Investigation Before and After Total Hip Arthroplasty
The application of gait analysis on patients with Hip Osteoarthritis (HOA) before and after Total Hip Arthroplasty (THA) surgery can provide accurate diagnostics, reliable treatment decision making, and proper rehabilitation efforts. Acquired kinematic trajectories provide discriminating features th...
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2025-01-01
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| author | Roel Pantonial Milan Simic |
| author_facet | Roel Pantonial Milan Simic |
| author_sort | Roel Pantonial |
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| description | The application of gait analysis on patients with Hip Osteoarthritis (HOA) before and after Total Hip Arthroplasty (THA) surgery can provide accurate diagnostics, reliable treatment decision making, and proper rehabilitation efforts. Acquired kinematic trajectories provide discriminating features that can be used to determine the gait patterns of healthy subjects and the effects of surgical operation. However, there is still a lack of consensus on the best discriminating kinematics to achieve this. Our investigation aims to utilize Deep Learning (DL) methodologies and improve classification results for the kinematic parameters of healthy, HOA, and 6 months post-THA gait cycles. Kinematic angles from the lower limb are used directly as one-dimensional inputs into a DL model. Based on the human gait cycle’s features, a hybrid Long Short-Term Memory–Convolutional Neural Network (HLSTM-CNN) is designed for the classification of healthy/HOA/THA gaits. It was found, from the results, that the sagittal angles of hip and knee, and front angles of FPA and knee, provide the most discriminating results with accuracy above 94% between healthy and HOA gaits. Interestingly, when using the sagittal angles of hip and knee to analyze the THA gaits, common subjects have the same results on the misclassifications. This crucial information provides a glimpse in the determination for the success or failure of THA. |
| format | Article |
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| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-572378848c324104b73dd251ca5dbd0a2025-01-24T13:21:09ZengMDPI AGApplied Sciences2076-34172025-01-0115287210.3390/app15020872Novel Deep Learning Method in Hip Osteoarthritis Investigation Before and After Total Hip ArthroplastyRoel Pantonial0Milan Simic1School of Engineering, RMIT University, Melbourne, VIC 3000, AustraliaSchool of Engineering, RMIT University, Melbourne, VIC 3000, AustraliaThe application of gait analysis on patients with Hip Osteoarthritis (HOA) before and after Total Hip Arthroplasty (THA) surgery can provide accurate diagnostics, reliable treatment decision making, and proper rehabilitation efforts. Acquired kinematic trajectories provide discriminating features that can be used to determine the gait patterns of healthy subjects and the effects of surgical operation. However, there is still a lack of consensus on the best discriminating kinematics to achieve this. Our investigation aims to utilize Deep Learning (DL) methodologies and improve classification results for the kinematic parameters of healthy, HOA, and 6 months post-THA gait cycles. Kinematic angles from the lower limb are used directly as one-dimensional inputs into a DL model. Based on the human gait cycle’s features, a hybrid Long Short-Term Memory–Convolutional Neural Network (HLSTM-CNN) is designed for the classification of healthy/HOA/THA gaits. It was found, from the results, that the sagittal angles of hip and knee, and front angles of FPA and knee, provide the most discriminating results with accuracy above 94% between healthy and HOA gaits. Interestingly, when using the sagittal angles of hip and knee to analyze the THA gaits, common subjects have the same results on the misclassifications. This crucial information provides a glimpse in the determination for the success or failure of THA.https://www.mdpi.com/2076-3417/15/2/872gaitkinematic parametership osteoarthritistotal hip arthroplastylong short-term memoryconvolutional neural networks |
| spellingShingle | Roel Pantonial Milan Simic Novel Deep Learning Method in Hip Osteoarthritis Investigation Before and After Total Hip Arthroplasty Applied Sciences gait kinematic parameters hip osteoarthritis total hip arthroplasty long short-term memory convolutional neural networks |
| title | Novel Deep Learning Method in Hip Osteoarthritis Investigation Before and After Total Hip Arthroplasty |
| title_full | Novel Deep Learning Method in Hip Osteoarthritis Investigation Before and After Total Hip Arthroplasty |
| title_fullStr | Novel Deep Learning Method in Hip Osteoarthritis Investigation Before and After Total Hip Arthroplasty |
| title_full_unstemmed | Novel Deep Learning Method in Hip Osteoarthritis Investigation Before and After Total Hip Arthroplasty |
| title_short | Novel Deep Learning Method in Hip Osteoarthritis Investigation Before and After Total Hip Arthroplasty |
| title_sort | novel deep learning method in hip osteoarthritis investigation before and after total hip arthroplasty |
| topic | gait kinematic parameters hip osteoarthritis total hip arthroplasty long short-term memory convolutional neural networks |
| url | https://www.mdpi.com/2076-3417/15/2/872 |
| work_keys_str_mv | AT roelpantonial noveldeeplearningmethodinhiposteoarthritisinvestigationbeforeandaftertotalhiparthroplasty AT milansimic noveldeeplearningmethodinhiposteoarthritisinvestigationbeforeandaftertotalhiparthroplasty |