Head Trauma

Unsupervised Machine Learning Drives Dynamic Alignment Classification In Navigated Total Knee Arthroplasty.

BACKGROUND: Current classifications used for total knee arthroplasty (TKA) are static and fail to capture the dynamic behavior of the limb during gait. This study introduces a novel intraoperative method to measure dynamic hip-knee angle (dHKA) using an intra-articular device coupled with a computer-assisted orthopaedic surgery (CAOS) system. This device applies a quasi-constant distraction force ...

Neural Stem Cell-Derived Extracellular Vesicles: The Next Frontier in Neurological and Neurodegenerative Diseases.

The present manuscript provides a comprehensive overview of neural stem cell (NSC)-derived extracellular vesicles (NSC-EVs( as a cell-free approach to treating central nervous system (CNS) disorders. The study noted that NSCs are regenerative and neuroprotective, but direct transplantation is limited by short survival, immunological rejection, and tumorigenic risk. However, NSC-EVs-nano-sized vesi...

Does the Timing of USMLE Step 1 Affect Performance? A Comparison of Pre- and Post-Clerkship Timing in the Pass/Fail Era.

In 2022, Step 1 of the United States Medical Licensing Examination transitioned to pass/fail scoring, removing a major performance-oriented incentive ...

Artificial intelligence-driven donor-recipient gut microbiome matching for optimized fecal microbiota transplantation.

Fecal microbiota transplantation (FMT) has emerged as a promising therapy for gastrointestinal diseases, yet its clinical efficacy remains individuall...

Editorial: future of ureteral stents: what do we need? a pearls opinion.

Ureteral stents, introduced in 1960s, are essential for managing upper urinary tract pathologies, maintaining ureteral patency in conditions like obst...

Volumetric phase-specific three-dimensional radiomics and machine learning for differentiation of non-mass enhancement in breast magnetic resonance imaging.

PURPOSE: Non-mass enhancement (NME) in breast magnetic resonance imaging (MRI) is a diagnostically challenging entity due to overlapping benign and ma...

Evaluation of an AI-Driven Risk Stratification System for Clinical Deterioration in Geriatric Hip Fracture Patients.

OBJECTIVES: To determine whether an artificial-intelligence-driven Clinical Deterioration Index (CDI) could identify geriatric hip-fracture patients a...

Deep learning discriminates seizures from normal brain oscillations in the electroencephalogram of a rat model of post-traumatic epilepsy.

This study used machine learning to objectively identify seizures in the electroencephalogram of a model of post-traumatic epilepsy based on fluid per...

Optimizing temporal windows for wearable-augmented post-discharge risk prediction: a methods study.

OBJECTIVE: Traditional readmission risk models relying on static discharge data have limited predictive performance and fail to capture patients' reco...

Evaluating the Utility and Limitations of Machine Learning Tumor Segmentation for Automated Longitudinal RANO Treatment Response Classification.

PURPOSE: Machine learning segmentation has emerged in tumor assessment with high performance in volumetric evaluation of brain tumors. It is unclear, ...

Classifying Post-COVID "Brain Fog" Patients and Identifying Key ROIs via Graph Neural Network Model.

Brain fog has raised significant public health concerns as a common neurocognitive impairment in the post-COVID-19 condition, involving memory loss, p...

Personalized Treatment Selection for Digital Eating Disorder Interventions: A Proof-of-Concept for the Personalized Advantage Index.

OBJECTIVE: Although a range of evidence-based treatments for eating disorders exist, treatment response varies substantially. The ability to match ind...

From prediction to practice: early implementation of a machine learning-based hospitalization prediction tool in the emergency department.

BACKGROUND: Machine learning (ML) models can accurately predict hospital admissions in emergency departments (EDs), but real-world adoption remains ra...

Deep learning driven clustering of post-traumatic stress disorder (PTSD) profiles in student-athletes: implications for precision and stratified mental health support.

BACKGROUND: Athletes frequently experience potentially traumatic events related to sports injuries, significantly increasing their risk of developing ...

In-Sensor-Memory Computing for Post-Von Neumann Intelligence: A Perspective.

The rapid growth of artificial intelligence, ubiquitous sensing, and edge computing is exposing fundamental limitations of conventional von Neumann ar...

The B7 family subgroup reflects tumor cell heterogeneity and patient post-operative prognosis in gallbladder cancer.

PURPOSE: To elucidate the biological heterogeneity of gallbladder cancer (GBC) cells and refine post-operative risk stratification by investigating th...

Fully digital and robot-assisted workflow for mandibular molar autotransplantation: A proof-of-concept.

OBJECTIVES: To present a robot-assisted protocol for autogenous mandibular molar transplantation using an autonomous robotic system for shape-matched ...

Ensemble Machine Learning Models for Predicting Death in Post-Acute Skilled Nursing Facilities.

OBJECTIVE: Patients discharged from hospitals to skilled nursing facilities (SNFs) for post-acute care are at high risk for adverse outcomes, includin...

Advances in HLA and Non-HLA Immune Profiling: Integrating Next-Generation Sequencing and Artificial Intelligence in Transplantation.

Understanding transplantation outcomes requires an integrated view of immunological, genetic, and clinical determinants that collectively shape graft ...

An explainable multi-head attention network for healthcare IoT threat detection based on the MedDefender-MHAN framework.

The rapid proliferation of Internet of Medical Things (IoMT) devices in healthcare environments has created critical cybersecurity vulnerabilities tha...