Support vector machines are superior to principal components analysis for selecting the optimal bones’ CT attenuations for opportunistic screening for osteoporosis using CT scans of the foot or ankle

Sebro, Ronnie; Ramos, Cynthia

doi:10.1016/j.afos.2022.09.002

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…Other studies have demonstrated that the CTscans of the cervical spine, chest, abdomen and pelvis, wrist and forearm, ankle, and foot can be utilized for opportunistic screening for osteoporosis/osteopenia. 9,14,17,41,42 The spine and hips may be involved by degenerative changes or bony metastases that may affect the BMD of the bones. 2,[8][9][10]48 Our results show that opportunistic knee CT scans can be used in patients with degenerative changes of the knee.…”

Section: Discussionmentioning

confidence: 99%

“…Patients were excluded if they had fractures of the distal femur, patella, proximal tibia, or proximal fibula because fractures could affect the CT attenuation of the bones. 41,42 Patients were also excluded if they had hardware involving the knee. Patient age, gender, height, weight, and body mass index (BMI) were collected at the time of the DXA study.…”

Section: Patientsmentioning

confidence: 99%

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Machine Learning for Opportunistic Screening for Osteoporosis and Osteopenia Using Knee CT Scans

Sebro

Elmahdy

2023

Can Assoc Radiol J

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Purpose To predict whether a patient has osteoporosis/osteopenia using the attenuation of trabecular bone obtained from knee computed tomography (CT) scans. Methods Retrospective analysis of 273 patients who underwent contemporaneous knee CT scans and dual-energy X-ray absorptiometry (DXA) within 1 year. Volumetric segmentation of the trabecular bone of the distal femur, proximal tibia, patella, and proximal fibula was performed to obtain the bone CT attenuation. The data was randomly split into training/validation (78%) and test (22%) datasets and the performance in the test dataset were evaluated. The predictive properties of the CT attenuation of each bone to predict osteoporosis/osteopenia were assessed. Multivariable support vector machines (SVM) and random forest classifiers (RF) were used to predict osteoporosis/osteopenia. Results Patients with a mean age (range) of 67.9 (50–87) years, 85% female were evaluated. Seventy-seven (28.2%) of patients had normal bone mineral density (BMD), 140 (51.3%) had osteopenia, and 56 (20.5%) had osteoporosis. The proximal tibia had the best predictive ability of all bones and a CT attenuation threshold of 96.0 Hounsfield Units (HU) had a sensitivity of .791, specificity of .706, and area under the curve (AUC) of .748. The AUC for the SVM with cubic kernel classifier (AUC = .912) was better than the RF classifier (AUC = .683, P < .001) and better than using the CT attenuation threshold of 96.0 HU at the proximal tibia (AUC = .748, P = .025). Conclusions Opportunistic screening for osteoporosis/osteopenia can be performed using knee CT scans. Multivariable machine learning models are more predictive than the CT attenuation of a single bone.

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Section: Discussionmentioning

confidence: 99%

Section: Patientsmentioning

confidence: 99%

Machine Learning for Opportunistic Screening for Osteoporosis and Osteopenia Using Knee CT Scans

Sebro

Elmahdy

2023

Can Assoc Radiol J

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“…ML algorithms learn from data using statistical techniques, adapting and improving their performance over time. ML algorithms include random forests (RFs), 84 support vector machines (SVMs), 85 naive Bayes, 86 artificial neural networks (ANNs), 87 gradient boosting machines (GBMs), 86 principal components analysis (PCA), 88 Least Absolute Selection and Shrinkage Operator (LASSO), 89 Elastic Net, 89 Ridge regression, 89 and natural language processing. 90 Deep learning is a subset of ML that involves architectures based on convolutional neural networks with multiple layers to extract intricate patterns from data.…”

Section: F1-scorementioning

confidence: 99%

Advancing Diagnostics and Patient Care: The Role of Biomarkers in Radiology

Sebro

2024

Semin Musculoskelet Radiol

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The integration of biomarkers into medical practice has revolutionized the field of radiology, allowing for enhanced diagnostic accuracy, personalized treatment strategies, and improved patient care outcomes. This review offers radiologists a comprehensive understanding of the diverse applications of biomarkers in medicine. By elucidating the fundamental concepts, challenges, and recent advancements in biomarker utilization, it will serve as a bridge between the disciplines of radiology and epidemiology. Through an exploration of various biomarker types, such as imaging biomarkers, molecular biomarkers, and genetic markers, I outline their roles in disease detection, prognosis prediction, and therapeutic monitoring. I also discuss the significance of robust study designs, blinding, power and sample size calculations, performance metrics, and statistical methodologies in biomarker research. By fostering collaboration between radiologists, statisticians, and epidemiologists, I hope to accelerate the translation of biomarker discoveries into clinical practice, ultimately leading to improved patient care.

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Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty

Ritter,

Denard,

Raiss

et al. 2024

Journal of Shoulder and Elbow Surgery

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Support vector machines are superior to principal components analysis for selecting the optimal bones’ CT attenuations for opportunistic screening for osteoporosis using CT scans of the foot or ankle

Cited by 3 publications

References 30 publications

Machine Learning for Opportunistic Screening for Osteoporosis and Osteopenia Using Knee CT Scans

Machine Learning for Opportunistic Screening for Osteoporosis and Osteopenia Using Knee CT Scans

Advancing Diagnostics and Patient Care: The Role of Biomarkers in Radiology

Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty

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