Novel Method of Classification in Knee Osteoarthritis: Machine Learning Application Versus Logistic Regression Model

Yang, Jung Ho; Park, Jae Hyeon; Jang, Seong Ho; Cho, Jaesung

doi:10.5535/arm.20071

Cited by 16 publications

(11 citation statements)

References 36 publications

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“…20,32 In addition, previous studies comparing non-pathologic gait patterns with patients with either KOA, HOA, or LSS also used SVM for classification. [23][24][25] Hence, it is not surprising that SVM was the best-performing algorithm in our study as well. Reasons given for the good performance of SVM on gait data include good generalization ability on unknown datasets 20 and robustness to data bias and data variance.…”

Section: Discussionsupporting

confidence: 59%

“…20 In this way, machine learning algorithms have been able to successfully discriminate between gait patterns of healthy persons and different patient groups, such as Parkinson's disease, stroke, ataxia, or sport injuries with accuracies varying from 70% to 97%. 21 Similar results were found when comparing the gait of patients with KOA, HOA, or LSS with healthy persons using Support Vector Machines (SVM) with an accuracy of 91.8% for patients with KOA, 23 of 88% for patients with HOA 24 and 80.4% for patients with LSS. 25 Furthermore, machine learning has been successfully used to discriminate between different severities of KOA using k-means clustering 26,27 and SVM.…”

supporting

confidence: 68%

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Classification of inertial sensor‐based gait patterns of orthopaedic conditions using machine learning: A pilot study

Dammeyer,

Nüesch,

Visscher

et al. 2024

Journal Orthopaedic Research

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Elderly patients often have more than one disease that affects walking behavior. An objective tool to identify which disease is the main cause of functional limitations may aid clinical decision making. Therefore, we investigated whether gait patterns could be used to identify degenerative diseases using machine learning. Data were extracted from a clinical database that included sagittal joint angles and spatiotemporal parameters measured using seven inertial sensors, and anthropometric data of patients with unilateral knee or hip osteoarthritis, lumbar or cervical spinal stenosis, and healthy controls. Various classification models were explored using the MATLAB Classification Learner app, and the optimizable Support Vector Machine was chosen as the best performing model. The accuracy of discrimination between healthy and pathologic gait was 82.3%, indicating that it is possible to distinguish pathological from healthy gait. The accuracy of discrimination between the different degenerative diseases was 51.4%, indicating the similarities in gait patterns between diseases need to be further explored. Overall, the differences between pathologic and healthy gait are distinct enough to classify using a classical machine learning model; however, routinely recorded gait characteristics and anthropometric data are not sufficient for successful discrimination of the degenerative diseases.

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Section: Discussionsupporting

confidence: 59%

supporting

confidence: 68%

Classification of inertial sensor‐based gait patterns of orthopaedic conditions using machine learning: A pilot study

Dammeyer,

Nüesch,

Visscher

et al. 2024

Journal Orthopaedic Research

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“…39 Studies have also shown the superiority of Machine Learning/AI techniques including DL 25 over conventional statistical analysis for disease detection and prediction of medical outcomes. 40,41 We assessed the importance of the differentially methylated genes on biological pathways, both to further elucidate the molecular mechanisms of PC and also to determine the biological plausibility of our findings. A high percentage of the 66 epigenetically dysregulated molecular pathways identified (Table S3) was related to cancer.…”

Section: Discussionmentioning

confidence: 99%

“…AI techniques are specifically engineered to handle such big data 39 . Studies have also shown the superiority of Machine Learning/AI techniques including DL 25 over conventional statistical analysis for disease detection and prediction of medical outcomes 40,41 …”

Section: Discussionmentioning

confidence: 99%

Precision oncology: Artificial intelligence, circulating cell‐free DNA, and the minimally invasive detection of pancreatic cancer—A pilot study

Bahado‐Singh,

Turkoglu,

Aydas

et al. 2023

Cancer Medicine

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BackgroundPancreatic cancer (PC) is among the most lethal cancers. The lack of effective tools for early detection results in late tumor detection and, consequently, high mortality rate. Precision oncology aims to develop targeted individual treatments based on advanced computational approaches of omics data. Biomarkers, such as global alteration of cytosine (CpG) methylation, can be pivotal for these objectives. In this study, we performed DNA methylation profiling of pancreatic cancer patients using circulating cell‐free DNA (cfDNA) and artificial intelligence (AI) including Deep Learning (DL) for minimally invasive detection to elucidate the epigenetic pathogenesis of PC.MethodsThe Illumina Infinium HD Assay was used for genome‐wide DNA methylation profiling of cfDNA in treatment‐naïve patients. Six AI algorithms were used to determine PC detection accuracy based on cytosine (CpG) methylation markers. Additional strategies for minimizing overfitting were employed. The molecular pathogenesis was interrogated using enrichment analysis.ResultsIn total, we identified 4556 significantly differentially methylated CpGs (q‐value < 0.05; Bonferroni correction) in PC versus controls. Highly accurate PC detection was achieved with all 6 AI platforms (Area under the receiver operator characteristics curve [0.90–1.00]). For example, DL achieved AUC (95% CI): 1.00 (0.95–1.00), with a sensitivity and specificity of 100%. A separate modeling approach based on logistic regression‐based yielded an AUC (95% CI) 1.0 (1.0–1.0) with a sensitivity and specificity of 100% for PC detection. The top four biological pathways that were epigenetically altered in PC and are known to be linked with cancer are discussed.ConclusionUsing a minimally invasive approach, AI, and epigenetic analysis of circulating cfDNA, high predictive accuracy for PC was achieved. From a clinical perspective, our findings suggest that that early detection leading to improved overall survival may be achievable in the future.

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“…We used an inertial measurement unit (IMU) sensor-based gait analysis system (Human Track, R. Biotech Co. Ltd., Seoul, Korea), which has been used in previous studies [14,17,18]. The accuracy of this system has been validated against the widely used three-dimensional gait motion analysis system proposed by Cho et al [17].…”

Section: Gait Analysismentioning

confidence: 99%

Analysis of Gait Characteristics Using Hip-Knee Cyclograms in Patients with Hemiplegic Stroke

Lee

Ryu

Lee

et al. 2021

Sensors

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Gait disturbance is a common sequela of stroke. Conventional gait analysis has limitations in simultaneously assessing multiple joints. Therefore, we investigated the gait characteristics in stroke patients using hip-knee cyclograms, which have the advantage of simultaneously visualizing the gait kinematics of multiple joints. Stroke patients (n = 47) were categorized into two groups according to stroke severity, and healthy controls (n = 32) were recruited. An inertial measurement unit sensor-based gait analysis system, which requires placing seven sensors on the dorsum of both feet, the shafts of both tibias, the middle of both femurs, and the lower abdomen, was used for the gait analysis. Then, the hip-knee cyclogram parameters (range of motion, perimeter, and area) were obtained from the collected data. The coefficient of variance of the cyclogram parameters was obtained to evaluate gait variability. The cyclogram parameters differed between the stroke patients and healthy controls, and differences according to stroke severity were also observed. The gait variability parameters mainly differed in patients with more severe stroke, and specific visualized gait patterns of stroke patients were obtained through cyclograms. In conclusion, the hip-knee cyclograms, which show inter-joint coordination and visualized gait cycle in stroke patients, are clinically significant.

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Novel Method of Classification in Knee Osteoarthritis: Machine Learning Application Versus Logistic Regression Model

Cited by 16 publications

References 36 publications

Classification of inertial sensor‐based gait patterns of orthopaedic conditions using machine learning: A pilot study

Classification of inertial sensor‐based gait patterns of orthopaedic conditions using machine learning: A pilot study

Precision oncology: Artificial intelligence, circulating cell‐free DNA, and the minimally invasive detection of pancreatic cancer—A pilot study

Analysis of Gait Characteristics Using Hip-Knee Cyclograms in Patients with Hemiplegic Stroke

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