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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, poor concentration, and language difficulties. However, their neural mechanisms remain unclear, and objective resting-state fMRI-based diagnostic tools are still lacking. To address these challenges, we first recruited 72 patients who experienced pers...

Early-stage anomaly discrimination in lithium-ion batteries using internal pressure-temperature coupling and multi-source data fusion.

Thermal runaway is one of the most critical safety risks in lithium-ion batteries. Mitigating this risk requires not only detecting imminent failures but also effectively discriminating early-stage chemical instabilities. With the growing complexity of battery systems, it is crucial to shift from reactive, post-event alarms to proactive anomaly discrimination strategies. To meet this need, a frame...

Integrating Confidence, Difficulty, and Language Model Calibration for Better Explainability in Clinical Documents Coding: Applications of AI.

BACKGROUND: In recent years, there has been increasing interest in developing machine and deep learning models capable of annotating clinical document...

Prognostic Utility of Systemic Immune-Inflammation Markers in Locally Advanced Cervical Cancer Undergoing Radical Radiotherapy.

PURPOSE: To improve survival prediction in locally advanced cervical cancer (LACC) post-radiotherapy, we developed and compared a nomogram and machine...

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...

Interpretable machine learning for river salinity dynamics in arid basins.

Managing salinity in arid rivers is impeded by sparse monitoring, relying on low-frequency grab samples that miss hydrological event dynamics. Here, i...

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...

Artificial intelligence (AI)-assisted ultrasound in clinical trials: Endpoint automation, decentralized monitoring, and regulatory readiness.

Ultrasound is among the most widely used imaging modalities in clinical trials, and yet its dependence on operator skill and equipment settings has hi...

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...

Persistent white matter disruption underlies apparent functional normalization in intractable temporal lobe epilepsy: Evidence from multimodal MRI.

BACKGROUND: Intractable temporal lobe epilepsy (ITLE) poses ongoing therapeutic challenges due to resistance to antiseizure medications and limited im...

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...

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...

Changes in computed tomography derived muscle and pulmonary tissue in patients hospitalized for COVID-19: a 12-month follow-up study.

BACKGROUND: Prolonged muscle loss and persistent pulmonary radiological manifestations have been observed among previously hospitalized COVID-19 patie...

A multi-cohort evaluation of a curriculum-aligned, oncology-focused chatbot in pharmacy education.

INTRODUCTION: Pharmacy students increasingly rely on generative artificial intelligence (AI) for learning support, yet most available tools remain gen...

Lower pre-treatment TMS-evoked cortical reactivity and alpha-band oscillatory dynamics predict efficacy of primary motor cortex neuromodulation for chronic pain.

Repetitive transcranial magnetic stimulation (rTMS) to the primary motor cortex (M1) provides significant pain relief in ∼45% of chronic pain patients...

Automated detection of superior mesenteric artery occlusion on post-contrast CT Using a 3D deep learning model.

PURPOSE: To develop and evaluate a 3D deep learning model for detecting superior mesenteric artery occlusion (SMAO) on post-contrast abdominal CT exam...

Integrating AI-assisted image enhancement with physics-based synthesis of low-field MRI from high-field MRI.

OBJECTIVE: Low-field magnetic resonance imaging (MRI) offers distinct advantages in terms of affordability, portability, and accessibility. However, i...

A deep learning-based digital biopsy for predicting early recurrence in gastric cancer.

Early postoperative recurrence is a major cause of treatment failure in patients with locally advanced gastric cancer (LAGC), yet current staging syst...

Applications of Machine Learning, Natural Language Processing, and Generative Artificial Intelligence in Dermatology Education and Research: A Scoping Review.

Artificial intelligence (AI) is being increasingly used in dermatology education and research as digital health data expands and large language models...