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Utilization of Machine Learning Approaches to Predict Mortality in Pediatric Warzone Casualties.

BACKGROUND: Identification of pediatric trauma patients at the highest risk for death may promote optimization of care. This becomes increasingly important in austere settings with constrained medical capabilities. This study aimed to develop and validate predictive models using supervised machine learning (ML) techniques to identify pediatric warzone trauma patients at the highest risk for mortal...

[Application and prospect of machine learning in orthopaedic trauma].

OBJECTIVE: To review the current applications of machine learning in orthopaedic trauma and anticipate its future role in clinical practice.

[Application and research progress of artificial intelligence technology in trauma treatment].

OBJECTIVE: To review the application and research progress of artificial intelligence (AI) technology in trauma treatment.

Development of a Machine Learning-Based Prescriptive Tool to Address Racial Disparities in Access to Care After Penetrating Trauma.

IMPORTANCE: The use of artificial intelligence (AI) in clinical medicine risks perpetuating existing bias in care, such as disparities in access to po...

Automatic Identification and Segmentation of Orbital Blowout Fractures Based on Artificial Intelligence.

PURPOSE: The incidence of orbital blowout fractures (OBFs) is gradually increasing due to traffic accidents, sports injuries, and ocular trauma. Orbit...

[Robot-assisted linear aortofemoral bypass surgery].

Aortofemoral bypass surgery is still the «gold standard» for aortoiliac lesions with excellent results and long-term patency despite improvement of en...

Cirq Robotic Assistance for Thoracolumbar Pedicle Screw Placement: Overcoming the Disadvantages of Minimally Invasive Spine Surgery.

INTRODUCTION: Various minimally invasive spine surgery (MISS) techniques have been developed with the goal of reducing approach-related soft-tissue tr...

Artificial intelligence and computer vision in orthopaedic trauma : the why, what, and how.

Artificial intelligence (AI) is, in essence, the concept of 'computer thinking', encompassing methods that train computers to perform and learn from e...

Parkland Trauma Index of Mortality: Real-Time Predictive Model for Trauma Patients.

OBJECTIVE: Vital signs and laboratory values are used to guide decisions to use damage control techniques in lieu of early definitive fracture fixatio...

Robot-Assisted Surgery for Endometriosis Current and Future Perspectives.

Successful resection of all visible lesions may effectively treat endometriosis-related infertility and pelvic pain. Minimally invasive surgery provid...

The derivation of an International Classification of Diseases, Tenth Revision-based trauma-related mortality model using machine learning.

BACKGROUND: Existing mortality prediction models have attempted to quantify injury burden following trauma-related admissions with the most notable be...

Road traffic accident trauma: A model for road safety management utilizing the artificial intelligence with geo-mapping and geospatial data in Pakistan.

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...

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 (...

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...

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...