AIMC Topic: Humans

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Rigorous validation of machine learning in laboratory medicine: guidance toward quality improvement.

Critical reviews in clinical laboratory sciences
The application of artificial intelligence (AI) in laboratory medicine will revolutionize predictive modeling using clinical laboratory information. Machine learning (ML), a sub-discipline of AI, involves fitting algorithms to datasets and is broadly...

Combined peritumoral radiomics and clinical features predict 12-month progression free survival in glioblastoma.

Journal of neuro-oncology
PURPOSE: Analyzing post-treatment MRIs from glioblastoma patients can be challenging due to similar radiological presentations of disease progression and treatment effects. Identifying radiomics features (RFs) revealing progressive glioblastoma can c...

ASS1 is a hub gene and possible therapeutic target for regulating metabolic dysfunction-associated steatotic liver disease modulated by a carbohydrate-restricted diet.

Molecular diversity
Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading cause of chronic liver disease globally. A low-carbohydrate diet (LCD) offers benefits to MASLD patients, albeit its exact mechanism is not fully understood. Using public...

Fully Automated Online Adaptive Radiation Therapy Decision-Making for Cervical Cancer Using Artificial Intelligence.

International journal of radiation oncology, biology, physics
BACKGROUND: Interfraction variations during radiation therapy pose a challenge for patients with cervical cancer, highlighting the benefits of online adaptive radiation therapy (oART). However, adaptation decisions rely on subjective image reviews by...

Integrative neurorehabilitation using brain-computer interface: From motor function to mental health after stroke.

Bioscience trends
Stroke remains a leading cause of mortality and long-term disability worldwide, frequently resulting in impairments in motor control, cognition, and emotional regulation. Conventional rehabilitation approaches, while partially effective, often lack i...

Surface-Enhanced Raman Scattering (SERS) combined with machine learning enables accurate diagnosis of cervical cancer: From molecule to cell to tissue level.

Critical reviews in oncology/hematology
The rising number of cervical cancer cases is placing a heavy economic strain on the country and its people. Improving survival rates hinges on early detection, precise diagnosis, and thorough treatment. Common screening and diagnostic methods like P...

Tailored self-supervised pretraining improves brain MRI diagnostic models.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Self-supervised learning has shown potential in enhancing deep learning methods, yet its application in brain magnetic resonance imaging (MRI) analysis remains underexplored. This study seeks to leverage large-scale, unlabeled public brain MRI datase...

Development and Validation an AI Model to Improve the Diagnosis of Deep Infiltrating Endometriosis for Junior Sonologists.

Ultrasound in medicine & biology
OBJECTIVE: This study aims to develop and validate an artificial intelligence (AI) model based on ultrasound (US) videos and images to improve the performance of junior sonologists in detecting deep infiltrating endometriosis (DE).

Transcriptomic analyses of human brains with Alzheimer's disease identified dysregulated epilepsy-causing genes.

Epilepsy & behavior : E&B
BACKGROUND & OBJECTIVE: Alzheimer's Disease (AD) patients at multiple stages of disease progression have a high prevalence of seizures. However, whether AD and epilepsy share pathophysiological changes remains poorly defined. In this study, we levera...

Performance and hypothetical clinical impact of an mNGS-based machine learning model for antimicrobial susceptibility prediction of five ESKAPEE bacteria.

Microbiology spectrum
UNLABELLED: Antimicrobial resistance is an escalating global health crisis, underscoring the urgent need for timely and targeted therapies to ensure effective clinical treatment. We developed a machine learning model based on metagenomic next-generat...