Deep Learning-Based Body Composition Analysis for Cancer Patients Using Computed Tomographic Imaging.

Malnutrition is a commonly observed side effect in cancer patients, with a 30-85% worldwide prevalence in this population. Existing malnutrition screening tools miss ~ 20% of at-risk patients at initial screening and do not capture the abnormal body composition phenotype. Meanwhile, the gold-standard clinical criteria to diagnose malnutrition use changes in body composition as key parameters, particularly body fat and skeletal muscle mass loss.

Detection and Localization of Spine Disorders from Plain Radiography.

Spine disorders can cause severe functional limitations, including back pain, decreased pulmonary function, and increased mortality risk. Plain radiography is the first-line imaging modality to diagnose suspected spine disorders. Nevertheless, radiographical appearance is not always sufficient due to highly variable patient and imaging parameters, which can lead to misdiagnosis or delayed diagnosis.

Proximal femur fracture detection on plain radiography via feature pyramid networks.

Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240-310% by 2050. Hip fractures are predominantly diagnosed by radiologist review of radiographs. In this study, we developed a deep learning model by extending the VarifocalNet Feature Pyramid Network (FPN) for detection and localization of proximal femur fractures from plain radiography with clinically relevant metrics.

An Automated Vertebrae Localization, Segmentation, and Osteoporotic Compression Fracture Detection Pipeline for Computed Tomographic Imaging.

Osteoporosis is the most common chronic metabolic bone disease worldwide. Vertebral compression fracture (VCF) is the most common type of osteoporotic fracture. Approximately 700,000 osteoporotic VCFs are diagnosed annually in the USA alone, resulting in an annual economic burden of ~$13.

A deep learning wearable-based solution for continuous at-home monitoring of upper limb goal-directed movements.

Introduction: Monitoring upper limb function is crucial for tracking progress, assessing treatment effectiveness, and identifying potential problems or complications. Hand goal-directed movements (GDMs) are a crucial aspect of daily life, reflecting planned motor commands with hand trajectories towards specific target locations. Previous studies have shown that GDM tasks can detect early changes in upper limb function in neurodegenerative diseases and can be used to track disease progression over time.

Using Deep Learning to Detect the Presence and Location of Hemoperitoneum on the Focused Assessment with Sonography in Trauma (FAST) Examination in Adults.

Abdominal ultrasonography has become an integral component of the evaluation of trauma patients. Internal hemorrhage can be rapidly diagnosed by finding free fluid with point-of-care ultrasound (POCUS) and expedite decisions to perform lifesaving interventions. However, the widespread clinical application of ultrasound is limited by the expertise required for image interpretation.

Automated detection and localization of pericardial effusion from point-of-care cardiac ultrasound examination.

Focused Assessment with Sonography in Trauma (FAST) exam is the standard of care for pericardial and abdominal free fluid detection in emergency medicine. Despite its life saving potential, FAST is underutilized due to requiring clinicians with appropriate training and practice. To aid ultrasound interpretation, the role of artificial intelligence has been studied, while leaving room for improvement in localization information and computation time.

Automated Bone Tumor Segmentation and Classification as Benign or Malignant Using Computed Tomographic Imaging.

The purpose of this study was to pair computed tomography (CT) imaging and machine learning for automated bone tumor segmentation and classification to aid clinicians in determining the need for biopsy. In this retrospective study (March 2005-October 2020), a dataset of 84 femur CT scans (50 females and 34 males, 20 years and older) with definitive histologic confirmation of bone lesion (71% malignant) were leveraged to perform automated tumor segmentation and classification. Our method involves a deep learning architecture that receives a DICOM slice and predicts (i) a segmentation mask over the estimated tumor region, and (ii) a corresponding class as benign or malignant.