AIMC Topic: Humans

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Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms.

Scientific reports
Right ventricular dysfunction (RVD) is strongly associated with increased mortality in patients with acute pulmonary embolism (PE), making its early detection crucial. Identifying RVD risk factors rapidly, accurately, and economically within the acut...

Multimodal deep learning for cephalometric landmark detection and treatment prediction.

Scientific reports
In orthodontics and maxillofacial surgery, accurate cephalometric analysis and treatment outcome prediction are critical for clinical decision-making. Traditional approaches rely on manual landmark identification, which is time-consuming and subject ...

Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech.

Translational psychiatry
Psychosis poses substantial social and healthcare burdens. The analysis of speech is a promising approach for the diagnosis and monitoring of psychosis, capturing symptoms like thought disorder and flattened affect. Recent advancements in Natural Lan...

Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis.

Nature communications
Activity recognition in live-cell imaging is labor-intensive and requires significant human effort. Existing automated analysis tools are largely limited in versatility. We present the Intelligent Vesicle Exocytosis Analysis (IVEA) platform, an Image...

Incidental Finding of Coronary and Non-Coronary Artery Calcium: What Do Clinicians Need To Know?

Current atherosclerosis reports
PURPOSE OF REVIEW: This review summarizes the role of incidentally and non-incidentally discovered coronary artery calcification (CAC) and the evolving role of non-coronary artery calcification in atherosclerotic cardiovascular disease (ASCVD) risk a...

Integrated clinicopathological-radiomic-blood model for glioma survival prediction via machine learning: a multicenter cohort study.

Neurosurgical review
BACKGROUND: Glioma is characterized by a poor prognosis and limited possibilities for treatment. Previous studies have developed prediction models for glioma using genetic, clinical, pathological, imaging and other aspects; however, few studies have ...

Machine learning-based prediction of stone-free rate after retrograde intrarenal surgery for lower pole renal stones.

World journal of urology
BACKGROUND: Lower pole renal stones (LPS) present unique challenges for retrograde intrarenal surgery (RIRS) due to unfavorable anatomical features, often resulting in suboptimal stone-free rates (SFR). Recent advancements in machine learning (ML) of...

Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis.

BMC genomic data
BACKGROUND: Mycobacterium tuberculosis (MTB) is a human-specific pathogen that primarily infects humans, causing tuberculosis (TB). Antimicrobial resistance (AMR) in MTB presents a formidable challenge to global health. The employment of machine lear...

Neural network based AI model for lung health assessment.

Scientific reports
Treating pulmonary diseases is pivotal in healthcare since they are the third leading cause of mortality globally. To aid medical experts in diagnosis, various studies have been conducted using artificial intelligence (AI) compatible devices to analy...

Computational exploration of global venoms for antimicrobial discovery with Venomics artificial intelligence.

Nature communications
The rise of antibiotic-resistant pathogens, particularly gram-negative bacteria, highlights the urgent need for novel therapeutics. Drug-resistant infections now contribute to approximately 5 million deaths annually, yet traditional antibiotic discov...