AIMC Topic: Thorax
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Semi-supervised learning for an improved diagnosis of COVID-19 in CT images.
PloS one
Apr 1, 2021
Coronavirus disease 2019 (COVID-19) has been spread out all over the world. Although a real-time reverse-transcription polymerase chain reaction (RT-PCR) test has been used as a primary diagnostic tool for COVID-19, the utility of CT based diagnostic...
Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development.
Scientific data
Mar 25, 2021
We developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye-tracking system while a radiologist reviewed and reported on 1,083 CXR images. The dataset contains the f...
The characteristics and evolution of pulmonary fibrosis in COVID-19 patients as assessed by AI-assisted chest HRCT.
PloS one
Mar 23, 2021
The characteristics and evolution of pulmonary fibrosis in patients with coronavirus disease 2019 (COVID-19) have not been adequately studied. AI-assisted chest high-resolution computed tomography (HRCT) was used to investigate the proportion of COVI...
Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings.
Korean journal of radiology
Feb 24, 2021
OBJECTIVE: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management.
Fast and Accurate Detection of COVID-19 Along With 14 Other Chest Pathologies Using a Multi-Level Classification: Algorithm Development and Validation Study.
Journal of medical Internet research
Feb 10, 2021
BACKGROUND: COVID-19 has spread very rapidly, and it is important to build a system that can detect it in order to help an overwhelmed health care system. Many research studies on chest diseases rely on the strengths of deep learning techniques. Alth...
Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort.
Interdisciplinary sciences, computational life sciences
Feb 9, 2021
Corona Virus Disease (COVID-19) has spread globally quickly, and has resulted in a large number of causalities and medical resources insufficiency in many countries. Reverse-transcriptase polymerase chain reaction (RT-PCR) testing is adopted as biops...
An aggregate method for thorax diseases classification.
Scientific reports
Feb 5, 2021
A common problem found in real-word medical image classification is the inherent imbalance of the positive and negative patterns in the dataset where positive patterns are usually rare. Moreover, in the classification of multiple classes with neural ...
Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks.
Sensors (Basel, Switzerland)
Jan 27, 2021
When the displacement of an object is evaluated using sensor data, its movement back to the starting point can be used to correct the measurement error of the sensor. In medicine, the movements of chest compressions also involve a reciprocating movem...
Extracting and Learning Fine-Grained Labels from Chest Radiographs.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
Chest radiographs are the most common diagnostic exam in emergency rooms and intensive care units today. Recently, a number of researchers have begun working on large chest X-ray datasets to develop deep learning models for recognition of a handful o...
Association of AI quantified COVID-19 chest CT and patient outcome.
International journal of computer assisted radiology and surgery
Jan 23, 2021
PURPOSE: Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to d...
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