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Machine Learning-based World Health Organization Disability Assessment Schedule for persons with Parkinson's disease.

Parkinsonism & related disorders
INTRODUCTION: The World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) is a well-known measure to assess disability in persons with Parkinson's disease (PD). The purpose of this study was to develop a short form of the WHODAS 2.0...

Personalized auto-segmentation for magnetic resonance imaging-guided adaptive radiotherapy of large brain metastases.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Magnetic resonance-guided adaptive radiotherapy (MRgART) may improve the efficacy of large brain metastases (BMs)(≥2 cm), whereas the workflow requires optimized. This study develops a two-stage, personalized deep learning aut...

Machine learning-based integration reveals immunological heterogeneity and the clinical potential of T cell receptor (TCR) gene pattern in hepatocellular carcinoma.

Apoptosis : an international journal on programmed cell death
The T Cell Receptor (TCR) significantly contributes to tumor immunity, whereas the intricate interplay with the Hepatocellular Carcinoma (HCC) microenvironment and clinical significance remains largely unexplored. Here, we aimed to examine the functi...

Deep learning opportunistic screening for osteoporosis and osteopenia using radiographs of the foot or ankle - A pilot study.

European journal of radiology
BACKGROUND: The gold standard method for diagnosing low bone mineral density (BMD) is using dual-energy X-ray absorptiometry (DXA) however, most patients with low BMD are often not screened. We aimed to create a deep learning (DL) model to screen for...

Automated detection of early-stage osteonecrosis of the femoral head in adult using YOLOv10: Multi-institutional validation.

European journal of radiology
OBJECTIVES: To develop a deep learning model based on the You Only Look Once version 10 (YOLOv10) for detecting early-stage ONFH in adult using radiographs.

Machine learning for predicting severe dengue in Puerto Rico.

Infectious diseases of poverty
BACKGROUND: Distinguishing between non-severe and severe dengue is crucial for timely intervention and reducing morbidity and mortality. World Health Organization (WHO)-recommended warning signs offer a practical approach for clinicians but have limi...

Synthetic CT generation from CBCT and MRI using StarGAN in the Pelvic Region.

Radiation oncology (London, England)
RATIONALE AND OBJECTIVES: This study evaluated StarGAN, a deep learning model designed to generate synthetic computed tomography (sCT) images from magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT) data using a single model. Th...

Quantifying the tumour vasculature environment from CD-31 immunohistochemistry images of breast cancer using deep learning based semantic segmentation.

Breast cancer research : BCR
BACKGROUND: Tumour vascular density assessed from CD-31 immunohistochemistry (IHC) images has previously been shown to have prognostic value in breast cancer. Current methods to measure vascular density, however, are time-consuming, suffer from high ...

Development of a machine learning model related to explore the association between heavy metal exposure and alveolar bone loss among US adults utilizing SHAP: a study based on NHANES 2015-2018.

BMC public health
BACKGROUND: Alveolar bone loss (ABL) is common in modern society. Heavy metal exposure is usually considered to be a risk factor for ABL. Some studies revealed a positive trend found between urinary heavy metals and periodontitis using multiple logis...

Deep learning-based CT-free attenuation correction for cardiac SPECT: a new approach.

BMC medical imaging
BACKGROUND: Computed tomography attenuation correction (CTAC) is commonly used in cardiac SPECT imaging to reduce soft-tissue attenuation artifacts. However, CTAC is prone to inaccuracies due to CT artifacts and SPECT-CT mismatch, along with addition...