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Development and Validation of a Deep Learning Strategy for Automated View Classification of Pediatric Focused Assessment With Sonography for Trauma.

OBJECTIVE: Pediatric focused assessment with sonography for trauma (FAST) is a sequence of ultrasound views rapidly performed by clinicians to diagnose hemorrhage. A technical limitation of FAST is the lack of expertise to consistently acquire all required views. We sought to develop an accurate deep learning view classifier using a large heterogeneous dataset of clinician-performed pediatric FAST...

Augmenting BDI Agency with a Cognitive Service: Architecture and Validation in Healthcare Domain.

Autonomous intelligent systems are starting to influence clinical practice, as ways to both readily exploit experts' knowledge when contextual conditions demand so, and harness the overwhelming amount of patient related data currently at clinicians' disposal. However, these two approaches are rarely synergistically exploited, and tend to be used without integration. In this paper, we follow recent...

Curriculum learning for improved femur fracture classification: Scheduling data with prior knowledge and uncertainty.

An adequate classification of proximal femur fractures from X-ray images is crucial for the treatment choice and the patients' clinical outcome. We re...

Improved Prediction of Older Adult Discharge After Trauma Using a Novel Machine Learning Paradigm.

BACKGROUND: The ability to reliably predict outcomes after trauma in older adults (age ≥ 65 y) is critical for clinical decision making. Using novel m...

Robotic mesh-supported pectopexy for pelvic organ prolapse: expanding the options of pelvic floor repair.

Pelvic organ prolapse affects 30-50% of the female population. For definitive treatment surgery is unavoidable. Sacrocolpopexy has been the gold stand...

Deep Learning-Based Natural Language Processing in Radiology: The Impact of Report Complexity, Disease Prevalence, Dataset Size, and Algorithm Type on Model Performance.

In radiology, natural language processing (NLP) allows the extraction of valuable information from radiology reports. It can be used for various downs...

A real-world demonstration of machine learning generalizability in the detection of intracranial hemorrhage on head computerized tomography.

Machine learning (ML) holds great promise in transforming healthcare. While published studies have shown the utility of ML models in interpreting medi...

Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.

Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic ...

What's in a trauma? Using machine learning to unpack what makes an event traumatic.

What differentiates a trauma from an event that is merely upsetting? Wildly different definitions of trauma have been used in both formal (psychiatric...

Atraumatic Insertion of a Cochlear Implant Pre-Curved Electrode Array by a Robot-Automated Alignment with the Coiling Direction of the Scala Tympani.

INTRODUCTION: Electrode array translocation is an unpredictable event with all types of arrays, even using a teleoperated robot in a clinical scenario...

Reduction of Time on the Ground Related to Real-Time Video Detection of Falls in Memory Care Facilities: Observational Study.

BACKGROUND: Lying on the floor for a long period of time has been described as a critical determinant of prognosis following a fall. In addition to fa...

Automated detection of cervical ossification of the posterior longitudinal ligament in plain lateral radiographs of the cervical spine using a convolutional neural network.

Cervical ossification of the posterior longitudinal ligament (OPLL) is a contributing factor to spinal cord injury or trauma-induced myelopathy in the...

The future of basic science in orthopaedics and traumatology: Cassandra or Prometheus?

Orthopaedic and trauma research is a gateway to better health and mobility, reflecting the ever-increasing and complex burden of musculoskeletal disea...

Phenotyping the Spectrum of Traumatic Brain Injury: A Review and Pathway to Standardization.

It is widely appreciated that the spectrum of traumatic brain injury (TBI), mild through severe, contains distinct clinical presentations, variably re...

Musculoskeletal trauma and artificial intelligence: current trends and projections.

Musculoskeletal trauma accounts for a significant fraction of emergency department visits and patients seeking urgent care, with a high financial cost...

Automatic Detection of Thyroid and Adrenal Incidentals Using Radiology Reports and Deep Learning.

BACKGROUND: Computed tomography (CT) is commonly performed when evaluating trauma patients with up to 55% showing incidental findings. Current workflo...

Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.

We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analy...

Assessing the speed-accuracy trade-offs of popular convolutional neural networks for single-crop rib fracture classification.

Rib fractures are injuries commonly assessed in trauma wards. Deep learning has demonstrated state-of-the-art accuracy for a variety of tasks, includi...

Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma.

Post-traumatic stress disorder (PTSD) is characterized by complex, heterogeneous symptomology, thus detection outside traditional clinical contexts is...

Predicting venous thromboembolism in hospitalized trauma patients: a combination of the Caprini score and data-driven machine learning model.

BACKGROUND: Venous thromboembolism (VTE) is a common complication of hospitalized trauma patients and has an adverse impact on patient outcomes. Howev...