Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection Patient Monitoring using Deep Learning CT Image Analysis

03/10/2020
by   Ophir Gozes, et al.
0

Purpose: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus; demonstrate they can differentiate coronavirus patients from non-patients. Materials and Methods: Multiple international datasets, including from Chinese disease-infected areas were included. We present a system that utilizes robust 2D and 3D deep learning models, modifying and adapting existing AI models and combining them with clinical understanding. We conducted multiple retrospective experiments to analyze the performance of the system in the detection of suspected COVID-19 thoracic CT features and to evaluate evolution of the disease in each patient over time using a 3D volume review, generating a Corona score. The study includes a testing set of 157 international patients (China and U.S). Results: Classification results for Coronavirus vs Non-coronavirus cases per thoracic CT studies were 0.996 AUC (95 infected patients. Possible working point: 98.2 specificity. For time analysis of Coronavirus patients, the system output enables quantitative measurements for smaller opacities (volume, diameter) and visualization of the larger opacities in a slice-based heat map or a 3D volume display. Our suggested Corona score measures the progression of disease over time. Conclusion: This initial study, which is currently being expanded to a larger population, demonstrated that rapidly developed AI-based image analysis can achieve high accuracy in detection of Coronavirus as well as quantification and tracking of disease burden.

READ FULL TEXT

page 15

page 16

page 18

page 19

research
07/20/2020

Integrative Analysis for COVID-19 Patient Outcome Prediction

While image analysis of chest computed tomography (CT) for COVID-19 diag...
research
08/04/2020

COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring

In this work, we estimate the severity of pneumonia in COVID-19 patients...
research
10/27/2022

Deep Learning for Segmentation-based Hepatic Steatosis Detection on Open Data: A Multicenter International Validation Study

Despite high global prevalence of hepatic steatosis, no automated diagno...
research
05/10/2022

Using Deep Learning-based Features Extracted from CT scans to Predict Outcomes in COVID-19 Patients

The COVID-19 pandemic has had a considerable impact on day-to-day life. ...
research
10/29/2021

AI-Powered Semantic Segmentation and Fluid Volume Calculation of Lung CT images in Covid-19 Patients

COVID-19 pandemic is a deadly disease spreading very fast. People with t...
research
11/21/2019

Semantic Segmentation of Thigh Muscle using 2.5D Deep Learning Network Trained with Limited Datasets

Purpose: We propose a 2.5D deep learning neural network (DLNN) to automa...

Please sign up or login with your details

Forgot password? Click here to reset