Volatile organic compounds (VOCs) present in exhaled breath can help in analysing biochemical processes in the human body. Liver diseases can be traced using VOCs as biomarkers for physiological and pathophysiological conditions. In this work, we pro...
BACKGROUND AND OBJECTIVES: Both fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) are widely used to evaluate ischemia-causing coronary lesions. A new method of CT-iFR, namely AccuiFRct, for calculating iFR based on deep learning ...
OBJECTIVE: Hypoxia presents a salient feature investigated in most solid tumors that holds key roles in cancer progression, including glioblastoma multiforme (GBM). Here, we aimed to construct a hypoxia-derived gene signature for identifying the high...
We aimed to develop an explainable and reliable method to diagnose cysts and tumors of the jaw with massive panoramic radiographs of healthy peoples based on deep learning, since collecting and labeling massive lesion samples are time-consuming, and ...
For image-guided small animal irradiations, the whole workflow of imaging, organ contouring, irradiation planning, and delivery is typically performed in a single session requiring continuous administration of anaesthetic agents. Automating contourin...
Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This development has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The deman...
To evaluate the performance of a deep convolutional neural network (DCNN) in detecting local tumor progression (LTP) after tumor ablation for hepatocellular carcinoma (HCC) on follow-up arterial phase CT images. The DCNN model utilizes three-dimensio...
Chronic lymphocytic leukemia (CLL) is the most common blood cancer in adults. The course of CLL and patients' response to treatment are varied. This variability makes it difficult to select the most appropriate treatment regimen and predict the progr...
Differentiation between Crohn's disease and intestinal tuberculosis is difficult but crucial for medical decisions. This study aims to develop an effective framework to distinguish these two diseases through an explainable machine learning (ML) model...
Diagnostic and interventional imaging
Jan 26, 2022
PURPOSE: The purpose of this study was to evaluate a deep-learning model (DLM) for classifying coronary arteries on coronary computed tomography -angiography (CCTA) using the Coronary Artery Disease-Reporting and Data System (CAD-RADS).
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