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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).

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.

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 ...

Artificial intelligence strategies based on random forests for detecting ischemia-reperfusion injury changes in kidney tissue during intravital imaging.

Scientific reports
This study presents a supervised machine learning approach using a Random Forest classifier to detect ischemia-reperfusion injury (IRI) in kidney tissue based on intravital two-photon microscopy data. A rodent model of unilateral renal IRI was used, ...

Prediction of advanced chronic kidney disease through retinal fundus images by deep learning.

Scientific reports
This study was developed and evaluated deep learning model for detecting chronic kidney disease (CKD) by retinal fundus images. This study included 42,963 clinical visits from 17,442 patients who underwent retinal fundus examination between October 1...

Artificial intelligence for predicting depression anxiety and stress using psychometric data.

Scientific reports
Mental health is a crucial aspect of overall well-being, yet it is often overlooked due to stigma and limited accessibility to care. This study investigates the ability of artificial intelligence (AI) to predict common psychological conditions, depre...

Machine learning identifies exosome related gene signatures for early prediction of non-small cell lung cancer.

Scientific reports
Non-small cell lung cancer (NSCLC) remains a major health challenge worldwide, mainly due to the lack of effective early diagnostic biomarkers. Exosome-related genes have recently emerged as potential diagnostic markers due to their roles in tumor pr...