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Prognostic value of end-to-end deep learning assessment of myocardial scar and microvascular obstruction on late gadolinium enhancement cardiovascular magnetic resonance.

BACKGROUND: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is the reference standard for assessing myocardial scar and microvascular obstruction (MVO), strong predictors of post-acute myocardial infarction (AMI) outcomes. However, manual segmentation is time-consuming and subject to inter-observer variability, limiting clinical scalability. This study develops and valida...

Advancing Global Drug Safety: Challenges and Future Perspectives in Pharmacovigilance.

Adverse Drug Reactions (ADRs) pose significant challenges to patient safety, healthcare systems, and public health worldwide. As the pharmaceutical landscape expands with complex therapies such as biologics and biosimilars, the need for robust pharmacovigilance systems has become paramount. This review provides a comprehensive overview of ADRs, their classification, underlying mechanisms, and asso...

Strategic Governance of Artificial Intelligence-Enabled Clinical Algorithm Development: Formative Evaluation of the Semiautomatic Clinical Algorithm Development Framework.

BACKGROUND: Health care leaders face a strategic dilemma: traditional expert-led content development ensures safety but is too slow for digital innova...

Preliminary assessment of AI as a triage tool for forensic toxicology case interpretation.

Large language models (LLMs) such as ChatGPT have demonstrated potential for interpretation in various scientific disciplines; however, their applicat...

Toward Long-Term Visual Field Appearance Forecasting Using Artificial Intelligence for Ophthalmic Education and Diagnosis.

OBJECTIVE: To provide state-of-the-art, post hoc-explainable visual field (VF) forecasts to aid in training ophthalmic residents to characterize glauc...

Spatiotemporal clustering of state-level opioid, opioid-stimulant, and opioid-benzodiazepine polysubstance deaths across the U.S.: Hierarchical agglomerative clustering analysis.

BACKGROUND: Polysubstance use continues to complicate the US overdose landscape, with over 107,000 lives lost to drug overdoses in 2022. The present s...

Transient frontal spectral events from EEG predict antidepressant response to sertraline in depression.

Resting-state scalp electroencephalography (EEG) is a promising method for predicting patient outcomes of antidepressant treatments. Machine-learning-...

Non-Neural Post-Thyroidectomy Dysphonia: Long-Term Voice Outcomes in 527 Patients.

OBJECTIVE: To characterize non-neural post-thyroidectomy dysphonia (PTD) by analyzing long-term voice outcomes in patients with no evidence of nerve i...

Critical Appraisal of Artificial Intelligence for Rare-Event Recognition: Principles and Pharmacovigilance Case Studies.

Many high-stakes artificial intelligence (AI) applications target low-prevalence events, where apparent accuracy can conceal limited real-world value....

Risk prediction modelling of 30-day all-cause mortality following percutaneous coronary intervention in an Australian population: leveraging machine learning.

BACKGROUND: Preprocedural risk prediction of 30-day all-cause mortality after percutaneous coronary intervention (PCI) aids in clinical decision-makin...

Predicting Electrolyte Stability in Lithium and Post-Lithium Metal Batteries: The Failure of Linear Single Descriptor-Based Models.

A stable solid electrolyte interphase (SEI), formed by the reductive decomposition of electrolytes at the anode surface, is crucial for ensuring the s...

Proof-of-concept: Differentiating upper trapezius muscle with myofascial trigger point using deep learning model on a small prospective sEMG dataset.

Myofascial pain syndrome commonly arises from myofascial trigger points (MTrPs). While needle electromyography (iEMG) reveals spontaneous activity at ...

Fine-scale mapping of Oncomelania hupensis habitats in eastern China using multi-season Sentinel-2 imagery and semi-supervised deep learning.

Accurate mapping of Oncomelania hupensis, the intermediate host of Schistosoma japonicum, is challenging due to sparse field data and dynamic seasonal...

Deep Learning Reconstruction on Quantitative Analysis in Brain Tumors With Diffusion-Weighted Imaging and Dynamic Susceptibility Contrast Imaging.

BACKGROUND: Although deep learning reconstruction (DLR) has been shown to improve image quality in MRI, its impact on quantitative physiologic paramet...

Enhancing adverse drug event extraction and summarization for cancer drugs through large language models.

PURPOSE: Cancer treatments such as chemotherapy, targeted therapy, and immunotherapy can effectively combat malignant cells but frequently cause serio...

Inferring preoperative cognitive function from intraoperative electroencephalography in elderly patients using machine learning.

To develop and evaluate machine learning (ML) models that infer preoperative cognitive function from intraoperative electroencephalography (EEG). This...

Development, application and evaluation of an artificial intelligence (AI)-based platform (SurgSmart) for the automatic assessment of the critical view of safety (CVS) in laparoscopic cholecystectomy (LC).

BACKGROUND: Laparoscopic Cholecystectomy (LC) is the standard surgical treatment for symptomatic benign gallbladder diseases. Bile Duct Injury (BDI) i...

Predicting VRE Infection After Liver Transplantation With a Time-Updated Colonization Score.

BACKGROUND: Vancomycin-resistant Enterococcus (VRE) infection is a life-threatening complication after liver transplantation (LT). This study aimed to...

Artificial intelligence-derived electrocardiographic age gap as a predictor of mortality after coronary revascularization: prognostic value and short-term intra-patient variability.

AIMS: The artificial intelligence (AI)-derived electrocardiographic (ECG) age gap-the difference between AI-predicted ECG age and chronological age-is...

Application of treatment response assessment maps (TRAMs), based on delayed-contrast MRI for radiomic characterization of breast lesions.

Dynamic contrast-enhanced (DCE) breast MRI is a highly sensitive modality for detecting breast cancer, but its limited specificity often leads to fals...