AIMC Topic: Humans

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Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine Learning.

Molecules (Basel, Switzerland)
The src-homology 2 domain-containing phosphatase 2 (SHP2) is a human cytoplasmic protein tyrosine phosphatase that plays a crucial role in cellular signal transduction. Aberrant activation and mutations of SHP2 are associated with tumor growth and im...

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...

Assessing the accuracy and quality of artificial intelligence (AI) chatbot-generated responses in making patient-specific drug-therapy and healthcare-related decisions.

BMC medical informatics and decision making
BACKGROUND: Interactive artificial intelligence tools such as ChatGPT have gained popularity, yet little is known about their reliability as a reference tool for healthcare-related information for healthcare providers and trainees. The objective of t...

Optimizing sequence data analysis using convolution neural network for the prediction of CNV bait positions.

BMC bioinformatics
BACKGROUND: Accurate prediction of copy number variations (CNVs) from targeted capture next-generation sequencing (NGS) data relies on effective normalization of read coverage profiles. The normalization process is particularly challenging due to hid...

Analysis of the basement membrane-related genes ITGA7 and its regulatory role in periodontitis via machine learning: a retrospective study.

BMC oral health
BACKGROUND: Periodontitis is among the most prevalent inflammatory conditions and greatly impacts oral health. This study aimed to elucidate the role of basement membrane-related genes in the pathogenesis and diagnosis of periodontitis.

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...