Interdisciplinary sciences, computational life sciences
Feb 27, 2021
Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films brings a lot of concentration and time for doctors. Therefore, it is necessary to develop an automatic ...
Annals of the New York Academy of Sciences
Feb 26, 2021
We trained a deep learning algorithm to use skin optical coherence tomography (OCT) angiograms to differentiate between healthy and type 2 diabetic mice. OCT angiograms were acquired with a custom-built OCT system based on an akinetic swept laser at ...
Background CT colonography does not enable definite differentiation between benign and premalignant colorectal polyps. Purpose To perform machine learning-based differentiation of benign and premalignant colorectal polyps detected with CT colonograph...
International journal of environmental research and public health
Feb 21, 2021
BACKGROUND: The efficacy of artificial intelligence (AI)-driven automated medical-history-taking systems with AI-driven differential-diagnosis lists on physicians' diagnostic accuracy was shown. However, considering the negative effects of AI-driven ...
Journal of gastroenterology and hepatology
Feb 20, 2021
BACKGROUND AND AIM: Pattern analysis of big data can provide a superior direction for the clinical differentiation of diseases with similar endoscopic findings. This study aimed to develop a deep-learning algorithm that performs differential diagnosi...
The Journal of thoracic and cardiovascular surgery
Feb 16, 2021
OBJECTIVE: The study objective was to investigate if machine learning algorithms can predict whether a lung nodule is benign, adenocarcinoma, or its preinvasive subtype from computed tomography images alone.
Background and purpose - Machine learning (ML) techniques are a form of artificial intelligence able to analyze big data. Analyzing the outcome of (digital) questionnaires, ML might recognize different patterns in answers that might relate to differe...
Early differential diagnosis of several motor neuron diseases (MNDs) is extremely challenging due to the high number of overlapped symptoms. The routine clinical practice is based on clinical history and examination, usually accompanied by electrophy...
OBJECTIVES: To evaluate a deep learning-based model using model-generated segmentation masks to differentiate invasive pulmonary adenocarcinoma (IPA) from preinvasive lesions or minimally invasive adenocarcinoma (MIA) on CT, making comparisons with r...