OBJECTIVES: Cerebral ultrasound (CUS) is the main imaging screening tool in preterm infants. The aim of this work is to develop deep learning (DL) models that classify normal vs abnormal CUS to serve as a computer-aided detection tool providing timel...
OBJECTIVES: This study aimed to utilize MR radiomics-based machine learning classifiers on a large-sample, multicenter dataset to develop an optimal model for predicting malignant sinonasal tumors and tumor-like lesions.
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Aug 30, 2024
Artificial intelligence (AI) systems may personalise radiotherapy by assessing complex and multifaceted patient data and predicting tumour and normal tissue responses to radiotherapy. Here we describe three distinct generations of AI systems, namely ...
Archives of physical medicine and rehabilitation
Aug 30, 2024
OBJECTIVE: To derive and validate a prediction model for minimal clinically important differences (MCIDs) in upper extremity (UE) motor function after intention-driven robotic hand training using residual voluntary electromyography (EMG) signals from...
OBJECTIVE: To compare compressed sensing (CS) and the Cascades of Independently Recurrent Inference Machines (CIRIM) with respect to image quality and reconstruction times when 12-fold accelerated scans of patients with neurological deficits are reco...
The limited data poses a crucial challenge for deep learning-based volumetric medical image segmentation, and many methods have tried to represent the volume by its subvolumes (i.e., multi-view slices) for alleviating this issue. However, such method...
Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese
Aug 30, 2024
OBJECTIVE: Electrocardiography (ECGs) has been a vital tool for cardiovascular disease (CVD) diagnosis, which visually depicts the heart's electrical activity. To enhance automatic classification between normal and diseased ECG, it is essential to ex...
Archives of physical medicine and rehabilitation
Aug 30, 2024
OBJECTIVE: To identify and quantify ability bias in generative artificial intelligence large language model chatbots, specifically OpenAI's ChatGPT and Google's Gemini.
BACKGROUND AND AIMS: Artificial intelligence (AI) is increasingly used to improve adenoma detection during colonoscopy. This meta-analysis aimed to provide an updated evaluation of computer-aided detection (CADe) systems and their impact on key colon...
RATIONALE AND OBJECTIVES: Accurately predicting the pathological response to chemotherapy before treatment is important for selecting the appropriate treatment groups, formulating individualized treatment plans, and improving the survival rates of pa...
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