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A machine learning-based lung ultrasound algorithm for the diagnosis of acute heart failure.
Internal and emergency medicine
May 23, 2024
Lung ultrasound (LUS) is an effective tool for diagnosing acute heart failure (AHF). However, several imaging protocols currently exist and how to best use LUS remains undefined. We aimed at developing a lung ultrasound-based model for AHF diagnosis ...
Machine-learning models for diagnosis of rotator cuff tears in osteoporosis patients based on anteroposterior X-rays of the shoulder joint.
SLAS technology
May 23, 2024
OBJECTIVE: This study aims to diagnose Rotator Cuff Tears (RCT) and classify the severity of RCT in patients with Osteoporosis (OP) through the analysis of shoulder joint anteroposterior (AP) X-ray-based localized proximal humeral bone mineral densit...
Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans.
Scientific reports
May 23, 2024
In this retrospective study, we aimed to assess the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast head computed tomography (CT) images. In total, 152 adult head CT sca...
Deciphering the microbial landscape of lower respiratory tract infections: insights from metagenomics and machine learning.
Frontiers in cellular and infection microbiology
May 23, 2024
BACKGROUND: Lower respiratory tract infections represent prevalent ailments. Nonetheless, current comprehension of the microbial ecosystems within the lower respiratory tract remains incomplete and necessitates further comprehensive assessment. Lever...
Artificial intelligence to predict individualized outcome of acute ischemic stroke patients: The SIBILLA project.
European stroke journal
May 22, 2024
INTRODUCTION: Formulating reliable prognosis for ischemic stroke patients remains a challenging task. We aimed to develop an artificial intelligence model able to formulate in the first 24 h after stroke an individualized prognosis in terms of NIHSS.
Using a new artificial intelligence-aided method to assess body composition CT segmentation in colorectal cancer patients.
Journal of medical radiation sciences
May 22, 2024
INTRODUCTION: This study aimed to evaluate the accuracy of our own artificial intelligence (AI)-generated model to assess automated segmentation and quantification of body composition-derived computed tomography (CT) slices from the lumber (L3) regio...
Predicting 1 year readmission for heart failure: A comparative study of machine learning and the LACE index.
ESC heart failure
May 22, 2024
AIMS: There is a lack of tools for accurately identifying the risk of readmission for heart failure in elderly patients with arrhythmia. The aim of this study was to establish and compare the performance of the LACE [length of stay ('L'), acute (emer...
An ingenious deep learning approach for pressure injury depth evaluation with limited data.
Journal of tissue viability
May 22, 2024
BACKGROUND: The development of models using deep learning (DL) to assess pressure injuries from wound images has recently gained attention. Creating enough supervised data is important for improving performance but is time-consuming. Therefore, the d...
A deep learning model for brain segmentation across pediatric and adult populations.
Scientific reports
May 22, 2024
Automated quantification of brain tissues on MR images has greatly contributed to the diagnosis and follow-up of neurological pathologies across various life stages. However, existing solutions are specifically designed for certain age ranges, limiti...
Artificial intelligence-based prognostic model accurately predicts the survival of patients with diffuse large B-cell lymphomas: analysis of a large cohort in China.
BMC cancer
May 22, 2024
BACKGROUND: Diffuse large B-cell lymphomas (DLBCLs) display high molecular heterogeneity, but the International Prognostic Index (IPI) considers only clinical indicators and has not been updated to include molecular data. Therefore, we developed a wi...
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