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Association of artificial intelligence-based immunoscore with the efficacy of chemoimmunotherapy in patients with advanced non-squamous non-small cell lung cancer: a multicentre retrospective study.

Frontiers in immunology
PURPOSE: Currently, chemoimmunotherapy is effective only in a subset of patients with advanced non-squamous non-small cell lung cancer. Robust biomarkers for predicting the efficacy of chemoimmunotherapy would be useful to identify patients who would...

Optimizing thyroid AUS nodules malignancy prediction: a comprehensive study of logistic regression and machine learning models.

Frontiers in endocrinology
BACKGROUND: The accurate diagnosis of thyroid nodules with indeterminate cytology, particularly in the atypia of undetermined significance (AUS) category, remains challenging. This study aims to predict the risk of malignancy in AUS nodules by compar...

Lower Limb Torque Prediction for Sit-To-Walk Strategies Using Long Short-Term Memory Neural Networks.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Joint torque prediction is crucial when investigating biomechanics, evaluating treatments, and designing powered assistive devices. Controllers in assistive technology require reference torque trajectories to set the level of assistance for a patient...

Development of a Dual-Plane MRI-Based Deep Learning Model to Assess the 1-Year Postoperative Outcomes in Lumbar Disc Herniation After Tubular Microdiscectomy.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Tubular microdiscectomy (TMD) is a treatment for lumbar disc herniation (LDH). Although the combination of MRI and deep learning (DL) has shown promise, its application in evaluating postoperative outcomes in TMD has not been fully explor...

Unbiased and reproducible liver MRI-PDFF estimation using a scan protocol-informed deep learning method.

European radiology
OBJECTIVE: To estimate proton density fat fraction (PDFF) from chemical shift encoded (CSE) MR images using a deep learning (DL)-based method that is precise and robust to different MR scanners and acquisition echo times (TEs).

Evaluating Advanced Machine Learning Models for Histopathological Diagnosis of Hansen Disease.

The American Journal of dermatopathology
INTRODUCTION: Leprosy is a neglected infectious disease caused by Mycobacterium leprae and Mycobacterium lepromatosis and remains a public health challenge in tropical regions. Therefore, the development of technological tools such as machine learnin...

The Effect of Ambient Artificial Intelligence Notes on Provider Burnout.

Applied clinical informatics
BACKGROUND:  Healthcare provider burnout is a critical issue with significant implications for individual well-being, patient care, and healthcare system efficiency. Addressing burnout is essential for improving both provider well-being and the quali...

A critical comparative study of the performance of three AI-assisted programs for bone age determination.

European radiology
OBJECTIVES: To date, AI-supported programs for bone age (BA) determination for medical use in Europe have almost only been validated separately, according to Greulich and Pyle (G&P). Therefore, the current study aimed to compare the performance of th...

Evaluation of the impact Of ChatGPT support on acromegaly management and patient education.

Endocrine
PURPOSE: ChatGPT is a widely used artificial intelligence modeling tool. Healthcare is one potential area of use of ChatGPT. This study aimed to test the usability and reliability of ChatGPT in acromegaly, which is less known in society and should be...

Minimizing prostate diffusion weighted MRI examination time through deep learning reconstruction.

Clinical imaging
PURPOSE: To study the diagnostic image quality of high b-value diffusion weighted images (DWI) derived from standard and variably reduced datasets reconstructed with a commercially available deep learning reconstruction (DLR) algorithm.