Deep learning techniques have recently demonstrated remarkable success in numerous domains. Typically, the success of these deep learning models is measured in terms of performance metrics such as accuracy and mean average precision (mAP). Generally,...
Biotechnology & genetic engineering reviews
May 14, 2023
This study aimed to evaluate the potential of deep learning applied to the measurement of echocardiographic data in patients with sudden cardiac death (SCD). 320 SCD patients who met the inclusion and exclusion criteria underwent clinical evaluation,...
BACKGROUND: The emergence of artificial intelligence (AI) tools such as ChatGPT and Bard is disrupting a broad swathe of fields, including medicine. In pediatric medicine, AI is also increasingly being used across multiple subspecialties. However, th...
BACKGROUND: Radiomic diagnosis models generally consider only a single dimension of information, leading to limitations in their diagnostic accuracy and reliability. The integration of multiple dimensions of information into the deep learning model h...
Journal of magnetic resonance imaging : JMRI
May 13, 2023
BACKGROUND: The diagnosis of prenatal placenta accreta spectrum (PAS) with magnetic resonance imaging (MRI) is highly dependent on radiologists' experience. A deep learning (DL) method using the prior knowledge that PAS-related signs are generally fo...
OBJECTIVES: To evaluate the image quality of the 3D hybrid profile order technique and deep-learning-based reconstruction (DLR) for 3D magnetic resonance cholangiopancreatography (MRCP) within a single breath-hold (BH) at 3 T magnetic resonance imagi...
Journal of clinical monitoring and computing
May 13, 2023
Capnography monitors trigger high priority 'no breath' alarms when CO measurements do not exceed a given threshold over a specified time-period. False alarms occur when the underlying breathing pattern is stable, but the alarm is triggered when the C...
Deep learning networks powered by AI are essential predictive tools relying on image data availability and processing hardware advancements. However, little attention has been paid to explainable AI (XAI) in application fields, including environmenta...
Current models on Explainable Artificial Intelligence (XAI) have shown a lack of reliability when evaluating feature-relevance for deep neural biomarker classifiers. The inclusion of reliable saliency-maps for obtaining trustworthy and interpretable ...
AIM: To investigate the feasibility of using deep learning (DL) to differentiate normal from abnormal (or scarred) kidneys using technetium-99m dimercaptosuccinic acid (Tc-DMSA) single-photon-emission computed tomography (SPECT) in paediatric patient...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.