Domestic Violence

Scan-less machine-learning-enabled incoherent microscopy for minimally-invasive deep-brain imaging.

Deep-brain microscopy is strongly limited by the size of the imaging probe, both in terms of achievable resolution and potential trauma due to surgery. Here, we show that a segment of an ultra-thin multi-mode fiber (cannula) can replace the bulky microscope objective inside the brain. By creating a self-consistent deep neural network that is trained to reconstruct anthropocentric images from the r...

Validation of a Machine Learning Model for Early Shock Detection.

OBJECTIVES: The objectives of this study were to test in real time a Trauma Triage, Treatment, and Training Decision Support (4TDS) machine learning (ML) model of shock detection in a prospective silent trial, and to evaluate specificity, sensitivity, and other estimates of diagnostic performance compared to the gold standard of electronic medical records (EMRs) review.

Identification of colorectal cancer using structured and free text clinical data.

Colorectal cancer incidence has continually fallen among those 50 years old and over. However, the incidence has increased in those under 50. Even wit...

Clinical decision support for severe trauma patients: Machine learning based definition of a bundle of care for hemorrhagic shock and traumatic brain injury.

BACKGROUND: Deviation from guidelines is frequent in emergency situations, and this may lead to increased mortality. Probably because of time constrai...

Electronic health record machine learning model predicts trauma inpatient mortality in real time: A validation study.

INTRODUCTION: Patient outcome prediction models are underused in clinical practice because of lack of integration with real-time patient data. The ele...

Deep Learning for Outcome Prediction in Neurosurgery: A Systematic Review of Design, Reporting, and Reproducibility.

Deep learning (DL) is a powerful machine learning technique that has increasingly been used to predict surgical outcomes. However, the large quantity ...

Demographic Reporting in Publicly Available Chest Radiograph Data Sets: Opportunities for Mitigating Sex and Racial Disparities in Deep Learning Models.

OBJECTIVE: Data sets with demographic imbalances can introduce bias in deep learning models and potentially amplify existing health disparities. We ev...

Diagnostic Performance of AI for Cancers Registered in A Mammography Screening Program: A Retrospective Analysis.

To evaluate the performance of an artificial intelligence (AI) algorithm in a simulated screening setting and its effectiveness in detecting missed a...

Use of deep learning methods for hand fracture detection from plain hand radiographs.

BACKGROUND: Patients with hand trauma are usually examined in emergency departments of hospitals. Hand fractures are frequently observed in patients w...

An Early Warning System Using Machine Learning for the Detection of Intracranial Hematomas in the Emergency Trauma Setting.

AIM: To present an early warning system (EWS) that employs a supervised machine learning algorithm for the rapid detection of extra-axial hematomas (E...

Predicting outcomes after trauma: Prognostic model development based on admission features through machine learning.

In an overcrowded emergency department (ED), trauma surgeons and emergency physicians need an accurate prognostic predictor for critical decision-maki...

Early Identification of Trauma-induced Coagulopathy: Development and Validation of a Multivariable Risk Prediction Model.

OBJECTIVE: The aim of this study was to develop and validate a risk prediction tool for trauma-induced coagulopathy (TIC), to support early therapeuti...

A Machine Learning Approach to Reclassifying Miscellaneous Patient Safety Event Reports.

BACKGROUND AND OBJECTIVES: Medical errors are a leading cause of death in the United States. Despite widespread adoption of patient safety reporting s...

Are rib fractures stable? An analysis of progressive rib fracture offset in the acute trauma setting.

BACKGROUND: Rib fractures serve as both a marker of injury severity and a guide for clinical decision making for trauma patients. Although recent stud...

Interpretation and reporting of predictive or diagnostic machine-learning research in Trauma & Orthopaedics.

There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for v...

Artificial intelligence in musculoskeletal conditions.

Artificial intelligence (AI) is an iterative process by which information is captured, transformed into knowledge and processed to produce adaptive ch...

Artificial Intelligence Enabling Radiology Reporting.

The radiology reporting process is beginning to incorporate structured, semantically labeled data. Tools based on artificial intelligence technologies...

An Improved Networked Predictive Controller for Vascular Robot Using 5G Networks.

Percutaneous coronary intervention (PCI) has gradually become the most common treatment of coronary artery disease (CAD) in clinical practice due to i...

Updates in deep learning research in ophthalmology.

Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garn...

Machine learning for outcome predictions of patients with trauma during emergency department care.

OBJECTIVES: To develop and evaluate a machine learning model for predicting patient with trauma mortality within the US emergency departments.