AIMC Topic: Humans

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Whole-genome sequencing reveals individual and cohort level insights into chromosome 9p syndromes.

Genome medicine
BACKGROUND: Previous genomic efforts on chromosome 9p deletion and duplication syndromes have utilized low-resolution strategies (i.e., karyotypes, chromosome microarrays). These studies have provided important initial insights into these syndromes. ...

High-acceleration pancreatobiliary MRI with deep learning-based super-resolution reconstruction for evaluating presumed pancreatic intraductal papillary mucinous neoplasm.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: To evaluate the feasibility and diagnostic utility of a deep learning (DL)-based super-resolution (SR) reconstruction algorithm applied to pancreatobiliary MRI for assessing pancreatic intraductal papillary mucinous neoplasms (IPMNs).

Exploring synthetic controls in rare diseases with a proof of concept in spinal cord injury.

BMC medicine
BACKGROUND: Successfully completing clinical trials for rare and heterogeneous disorders, like spinal cord injuries (SCI), remains challenging, thereby reducing the ability to test and translate promising preclinical findings. We propose synthetic co...

Implementing an AI-enhanced clinical decision support system for Stenotrophomonas maltophilia: a survey-based randomized controlled trial of antibiotic precision and impact on survival.

Implementation science : IS
BACKGROUND: The World Health Organization has identified Stenotrophomonas maltophilia (SM) as a high-risk antibiotic-resistant pathogen. Notably, determining the effectiveness of current antibiotics against SM is challenging, leading to improper ther...

Use of machine learning for risk stratification of chest pain patients in the emergency department.

BMC medical informatics and decision making
OBJECTIVE: To improve the initial risk assessment capability for emergency chest pain patients without relying on laboratory test results.

Virtual case reasoning and AI-assisted diagnostic instruction: an empirical study based on body interact and large language models.

BMC medical education
BACKGROUND: Integrating large language models (LLMs) with virtual patient platforms offers a novel approach to teaching clinical reasoning. This study evaluated the performance and educational value of combining Body Interact with two AI models, Chat...

Identification of clinically meaningful, overlapping obstructive respiratory disease subtypes via data-driven approaches in a primary care population.

BMC pulmonary medicine
BACKGROUND: Obstructive respiratory conditions, including asthma, bronchiectasis, and chronic obstructive pulmonary disease (COPD), are increasingly recognised as heterogeneous syndromes with significant overlap. Multiple disease pathways contribute ...

Characterizing immune profiles in hepatocellular carcinoma patients benefiting from pembrolizumab and lenvatinib using machine learning.

BMC cancer
BACKGROUND: Combination immunotherapies, such as pembrolizumab plus lenvatinib (PL), are commonly used in treatment for unresectable hepatocellular carcinoma (uHCC). However, it remains challenging to predict which patients will benefit from this the...

Dual-task walking for early detection of Alzheimer's disease: comparative analysis of tasks using whole-body gait variables.

BMC geriatrics
BACKGROUND: The worldwide rise in dementia creates an urgent need for screening methods that are both sensitive and easy to administer. Dual-task walking-requiring people to walk while performing a second cognitive or motor task-meets these criteria ...

Toxigraphnet: a graph neural network framework for precise toxicity prediction of drug molecules.

Journal of computer-aided molecular design
Accurate prediction of a drug molecule's toxicity is a critical step in pharmaceutical research, offering the potential to reduce experimental costs, mitigate adverse effects, and accelerate drug development. Traditional computational methods often r...