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Predicting macular hole surgery outcomes: Integrating preoperative OCT features with supervised machine learning statistical models.

Indian journal of ophthalmology
PURPOSE: To evaluate various supervised machine learning (ML) statistical models to predict anatomical outcomes after macular hole (MH) surgery using preoperative optical coherence tomography (OCT) features.

Consensus Between Radiologists, Specialists in Internal Medicine, and AI Software on Chest X-Rays in a Hospital-at-Home Service: Prospective Observational Study.

JMIR formative research
BACKGROUND: Home hospitalization is a care modality growing in popularity worldwide. Telemedicine-driven hospital-at-home (HAH) services could replace traditional hospital departments for selected patients. Chest x-rays typically serve as a key diagn...

3-Dimensional morphological characterization of neuroretinal microglia in Alzheimer's disease via machine learning.

Acta neuropathologica communications
Alzheimer's Disease (AD) is a debilitating neurodegenerative disease that affects 47.5 million people worldwide. AD is characterised by the formation of plaques containing extracellular amyloid-β (Aβ) and neurofibrillary tangles composed of hyper-pho...

Prognostic value of a composite physiologic index developed by adding bronchial and hyperlucent volumes quantified via artificial intelligence technology.

Respiratory research
BACKGROUND: The composite physiologic index (CPI) was developed to estimate the extent of interstitial lung disease (ILD) in idiopathic pulmonary fibrosis (IPF) patients based on pulmonary function tests (PFTs). The CALIPER-revised version of the CPI...

Trajectory of breastfeeding among Chinese women and risk prediction models based on machine learning: a cohort study.

BMC pregnancy and childbirth
BACKGROUND: Breastfeeding is the optimal source of nutrition for infants and young children, essential for their healthy growth and development. However, a gap in cohort studies tracking breastfeeding up to six months postpartum may lead caregivers t...

Machine learning for predicting acute myocardial infarction in patients with sepsis.

Scientific reports
Acute myocardial infarction (AMI) and sepsis are the leading causes of high mortality rates in intensive care units. While sepsis frequently affects the cardiovascular system, distinguishing between sepsis-induced cardiomyopathy and AMI remains chall...

Pulmonologists-level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approach.

Scientific reports
Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated consi...

Impact of Artificial Intelligence-Generated Content Labels On Perceived Accuracy, Message Credibility, and Sharing Intentions for Misinformation: Web-Based, Randomized, Controlled Experiment.

JMIR formative research
BACKGROUND: The proliferation of generative artificial intelligence (AI), such as ChatGPT, has added complexity and richness to the virtual environment by increasing the presence of AI-generated content (AIGC). Although social media platforms such as...

A comprehensive and bias-free machine learning approach for risk prediction of preeclampsia with severe features in a nulliparous study cohort.

BMC pregnancy and childbirth
Preeclampsia is one of the leading causes of maternal morbidity, with consequences during and after pregnancy. Because of its diverse clinical presentation, preeclampsia is an adverse pregnancy outcome that is uniquely challenging to predict and mana...