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Diagnosis of traumatic liver injury on computed tomography using machine learning algorithms and radiomics features: The role of artificial intelligence for rapid diagnosis in emergency rooms.

BACKGROUND: The initial assessment of trauma is a time-consuming and challenging task. The purpose of this research is to examine the diagnostic effectiveness and usefulness of machine learning models paired with radiomics features to identify blunt traumatic liver injury in abdominal computed tomography (CT) images.

An artificial intelligence application to predict prolonged dependence on mechanical ventilation among patients with critical orthopaedic trauma: an establishment and validation study.

BACKGROUND: Prolonged dependence on mechanical ventilation is a common occurrence in clinical ICU patients and presents significant challenges for patient care and resource allocation. Predicting prolonged dependence on mechanical ventilation is crucial for improving patient outcomes, preventing ventilator-associated complications, and guiding targeted clinical interventions. However, specific too...

Hope for the best prepare for the worst: acute kidney disease and catastrophic comorbidities (a case report).

It is evident that Acute Kidney Injury (AKI) is an independent risk factor for both the survival of patients and their kidneys. Here, we present a cas...

Development of Nipple Trauma Evaluation System With Deep Learning.

BACKGROUND: No research has been conducted on the use of deep learning for breastfeeding support.

Artificial intelligence in emergency and trauma radiology: ASER AI/ML expert panel Delphi consensus statement on research guidelines, practices, and priorities.

BACKGROUND: Emergency/trauma radiology artificial intelligence (AI) is maturing along all stages of technology readiness, with research and developmen...

SMART: Development and Application of a Multimodal Multi-organ Trauma Screening Model for Abdominal Injuries in Emergency Settings.

RATIONALE AND OBJECTIVES: Effective trauma care in emergency departments necessitates rapid diagnosis by interdisciplinary teams using various medical...

A quality assessment tool for focused abdominal sonography for trauma examinations using artificial intelligence.

BACKGROUND: Current tools to review focused abdominal sonography for trauma (FAST) images for quality have poorly defined grading criteria or are deve...

Fatal fall from a height: is it possible to apply artificial intelligence techniques for height estimation?

Fall from a height trauma is characterized by a multiplicity of injuries, related to multiple factors. The height of the fall is the factor that most ...

PREDICTING IN-HOSPITAL MORTALITY IN CRITICAL ORTHOPEDIC TRAUMA PATIENTS WITH SEPSIS USING MACHINE LEARNING MODELS.

Purpose: This study aims to establish and validate machine learning-based models to predict death in hospital among critical orthopedic trauma patient...

Enhancements in artificial intelligence for medical examinations: A leap from ChatGPT 3.5 to ChatGPT 4.0 in the FRCS trauma & orthopaedics examination.

INTRODUCTION: ChatGPT is a sophisticated AI model capable of generating human-like text based on the input it receives. ChatGPT 3.5 showed an inabilit...

Evaluating the Utility of ChatGPT in Diagnosing and Managing Maxillofacial Trauma.

Maxillofacial trauma is a significant concern in emergency departments (EDs) due to its high prevalence and the complexity of its management. However,...

External validation and performance analysis of a deep learning-based model for the detection of intracranial hemorrhage.

PurposeWe aimed to investigate the external validation and performance of an FDA-approved deep learning model in labeling intracranial hemorrhage (ICH...

Development of an Artificial Intelligence-Based Algorithm for the Assessment of Skeletal Age and Detection of Cervical Vertebral Anomalies in Patients with Cleft Lip and Palate.

ObjectiveTo develop an artificial intelligence (AI)-based algorithm for the assessment and comparison of skeletal maturation in patients with and with...

A digital phenotyping dataset for impending panic symptoms: a prospective longitudinal study.

This study investigated the utilization of digital phenotypes and machine learning algorithms to predict impending panic symptoms in patients with moo...

Fluorescence spectroscopy combined with multilayer perceptron deep learning to identify the authenticity of monofloral honey-Rape honey.

Honey authenticity is critical to honey quality. The development of a quick, easy, and non-destructive technique for determining the authenticity of h...

Unreal that feels real: artificial intelligence-enhanced augmented reality for treating social and occupational dysfunction in post-traumatic stress disorder and anxiety disorders.

Fear- and trauma-related conditions, such as post-traumatic stress disorder (PTSD) and social phobia, often manifest as socially avoidant behaviours,...

[Possibilities of the utilization of trauma networks of the German Society for Trauma Surgery using digital solutions].

This paper describes the use of digital solutions to improve the care of trauma patients in Germany. The focus is on the trauma networks of the German...

Enhancing Performance of the National Field Triage Guidelines Using Machine Learning: Development of a Prehospital Triage Model to Predict Severe Trauma.

BACKGROUND: Prehospital trauma triage is essential to get the right patient to the right hospital. However, the national field triage guidelines propo...

Predicting Intra- and Postpartum Hemorrhage through Artificial Intelligence.

: Intra/postpartum hemorrhage stands as a significant obstetric emergency, ranking among the top five leading causes of maternal mortality. The aim of...

A machine learning-based Coagulation Risk Index predicts acute traumatic coagulopathy in bleeding trauma patients.

BACKGROUND: Acute traumatic coagulopathy (ATC) is a well-described phenomenon known to begin shortly after injury. This has profound implications for ...