We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
Feather Safety Razor

Download Mobile App




Unsupervised AI Model Accurately Predicts COVID-19 Patient's Survival Based on Chest CT Exams

By HospiMedica International staff writers
Posted on 08 Aug 2021
Print article
Illustration
Illustration
An "unsupervised" artificial intelligence (AI) model, or one trained without image annotations, can accurately predict the survival of COVID-19 patients on the basis of their chest computed tomography (CT) exams.

Researchers from Massachusetts General Hospital (Boston, MA, USA) have shown that the performance of their pix2surv algorithm based on CT images significantly outperformed those of existing laboratory tests and image-based visual and quantitative predictors in estimating the disease progression and mortality of COVID-19 patients. Thus, pix2surv offers a promising approach for performing image-based prognostic predictions.

Because of the rapid spread and wide range of the clinical manifestations of the coronavirus disease 2019 (COVID-19), fast and accurate estimation of the disease progression and mortality is vital for the management of the patients. Currently available image-based prognostic predictors for patients with COVID-19 are largely limited to semi-automated schemes with manually designed features and supervised learning, and the survival analysis is largely limited to logistic regression. To resolve this problem, the researchers developed a weakly unsupervised conditional generative adversarial network, called pix2surv, which can be trained to estimate the time-to-event information for survival analysis directly from the chest CT images of a patient.

pix2surv enables the estimation of the distribution of the survival time directly from the chest CT images of patients. The model avoids the technical limitations of the previous image-based COVID-19 predictors, because the use of a fully automated conditional GAN makes it possible to train a complete image-based end-to-end survival analysis model for producing the time-to-event distribution directly from input chest CT images without an explicit segmentation or feature extraction efforts. Also, because of the use of weakly unsupervised learning, the annotation effort is reduced to the pairing of input training CT images with the corresponding observed survival time of the patient.

In their study the researchers showed that the prognostic performance of pix2surv based on chest CT images compares favorably with those of currently available laboratory tests and existing image-based visual and quantitative predictors in the estimation of the disease progression and mortality of COVID-19 patients. They also showed that the time-to-event information calculated by pix2surv based on chest CT images enables stratification of the patients into low- and high-risk groups by a wider margin than those of the other predictors. Thus, pix2surv offers a promising approach for performing image-based prognostic prediction for the management of COVID-19 patients.

Related Links:

Print article

Channels

Critical Care

view channel
Image: EsoGuard has demonstrated over 90% specificity and 90% sensitivity in identifying Barrett’s Esophagus (Photo courtesy of Lucid Diagnostics)

Biomarker Based Non-Endoscopic Technology Identifies Risk for Esophageal Cancer

Barrett's esophagus (BE) is the benign and treatable precursor condition to esophageal adenocarcinomas (EAC) which is usually diagnosed at an advanced stage and is difficult to treat. Finding BE, a sign... Read more

Surgical Techniques

view channel
Image: Bio-glue enables near-instantaneous gelling, sealing and healing of injured tissue (Photo courtesy of Pexels)

Game-Changing ‘Bio-Glue’ Could End Use of Surgical Sutures and Staple

Tissue adhesive washout and detachment are major issues for medical practitioners and may prove fatal for patients, especially when the separation happens in vital organs like the lungs, liver and the heart.... Read more

Patient Care

view channel
Image: Future wearable health tech could measure gases released from skin (Photo courtesy of Pexels)

Wearable Health Tech Could Measure Gases Released From Skin to Monitor Metabolic Diseases

Most research on measuring human biomarkers, which are measures of a body’s health, rely on electrical signals to sense the chemicals excreted in sweat. But sensors that rely on perspiration often require... Read more

Health IT

view channel
Image: AI can reveal a patient`s heart health (Photo courtesy of Mayo Clinic)

AI Trained for Specific Vocal Biomarkers Could Accurately Predict Coronary Artery Disease

Earlier studies have examined the use of voice analysis for identifying voice markers associated with coronary artery disease (CAD) and heart failure. Other research groups have explored the use of similar... Read more
Copyright © 2000-2022 Globetech Media. All rights reserved.