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Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review.

Journal of medical Internet research
BACKGROUND: Accurate measurement of food and nutrient intake is crucial for nutrition research, dietary surveillance, and disease management, but traditional methods such as 24-hour dietary recalls, food diaries, and food frequency questionnaires are...

Public Perception on Artificial Intelligence-Driven Mental Health Interventions: Survey Research.

JMIR formative research
BACKGROUND: Artificial intelligence (AI) has become increasingly important in health care, generating both curiosity and concern. With a doctor-patient ratio of 1:834 in India, AI has the potential to alleviate a significant health care burden. Publi...

A deep learning approach for automated scoring of the Rey-Osterrieth complex figure.

eLife
Memory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey-Osterrieth complex figure (ROCF) is the state-of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visu...

Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy.

Frontiers in immunology
BACKGROUND: Lung adenocarcinoma (LUAD) is a heterogeneous tumor characterized by diverse genetic and molecular alterations. Developing a multi-omics-based classification system for LUAD is urgently needed to advance biological understanding.

Predicting patients with septic shock and sepsis through analyzing whole-blood expression of NK cell-related hub genes using an advanced machine learning framework.

Frontiers in immunology
BACKGROUND: Sepsis is a life-threatening condition that causes millions of deaths globally each year. The need for biomarkers to predict the progression of sepsis to septic shock remains critical, with rapid, reliable methods still lacking. Transcrip...

Acute Effects of Aminophylline Effects on Hemodynamic Parameters and Fluid Balance in Pediatric Cardiac Intensive Care Patients: Machine Learning Insights Using High Fidelity Data.

Pediatric cardiology
Fluid overload is associated with increased morbidity and mortality after pediatric cardiac surgery. Management of fluid overload can be difficult and conventional tools may increase the risk of acute kidney injury. This study aimed to study the effe...

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

The neuroradiology journal
PurposeWe aimed to investigate the external validation and performance of an FDA-approved deep learning model in labeling intracranial hemorrhage (ICH) cases on a real-world heterogeneous clinical dataset. Furthermore, we delved deeper into evaluatin...

Thickness Speed Progression Index: Machine Learning Approach for Keratoconus Detection.

American journal of ophthalmology
PURPOSE: To develop and validate a pachymetry-based machine learning (ML) index for differentiating keratoconus, keratoconus suspect, and normal corneas.

Rough hypervolume-driven feature selection with groupwise intelligent sampling for detecting clinical characterization of lupus nephritis.

Artificial intelligence in medicine
Systemic lupus erythematosus (SLE) is an autoimmune inflammatory disease. Lupus nephritis (LN) is a major risk factor for morbidity and mortality in SLE. Proliferative and pure membranous LN have different prognoses and may require different treatmen...

Assessing the effects of 5-HT and 5-HT receptor antagonists on DOI-induced head-twitch response in male rats using marker-less deep learning algorithms.

Pharmacological reports : PR
BACKGROUND: Serotonergic psychedelics, which display a high affinity and specificity for 5-HT receptors like 2,5-dimethoxy-4-iodoamphetamine (DOI), reliably induce a head-twitch response in rodents characterized by paroxysmal, high-frequency head rot...