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
Sekisui Diagnostics UK Ltd.

Download Mobile App




AI Diagnostic Tool Performs On Par with Radiologists in Detecting Diseases on Chest X-Rays

By HospiMedica International staff writers
Posted on 19 Sep 2022
Print article
Image: New tool overcomes major hurdle in clinical AI design (Photo courtesy of Unsplash)
Image: New tool overcomes major hurdle in clinical AI design (Photo courtesy of Unsplash)

Most artificial intelligence (AI) models require labeled datasets during their “training” so they can learn to correctly identify pathologies. This process is especially burdensome for medical image-interpretation tasks since it involves large-scale annotation by human clinicians, which is often expensive and time-consuming. For instance, to label a chest X-ray dataset, expert radiologists would have to look at hundreds of thousands of X-ray images one by one and explicitly annotate each one with the conditions detected. While more recent AI models have tried to address this labeling bottleneck by learning from unlabeled data in a “pre-training” stage, they eventually require fine-tuning on labeled data to achieve high performance. Now, scientists have developed an AI diagnostic tool that can detect diseases on chest X-rays directly from natural-language descriptions contained in accompanying clinical reports.

The new model named CheXzero that was developed by scientists at Harvard Medical School (Boston, MA, USA) and colleagues at Stanford University (Stanford, CA, USA) is self-supervised, in the sense that it learns more independently, without the need for hand-labeled data before or after training. The step is deemed a major advance in clinical AI design because most current AI models require laborious human annotation of vast reams of data before the labeled data are fed into the model to train it. The model relies solely on chest X-rays and the English-language notes found in accompanying X-ray reports. The model was “trained” on a publicly available dataset containing more than 377,000 chest X-rays and more than 227,000 corresponding clinical notes.

Its performance was then tested on two separate datasets of chest X-rays and corresponding notes collected from two different institutions, one of which was in a different country. This diversity of datasets was meant to ensure that the model performed equally well when exposed to clinical notes that may use different terminology to describe the same finding. Upon testing, the researchers successfully identified pathologies that were not explicitly annotated by human clinicians. It outperformed other self-supervised AI tools and performed with accuracy similar to that of human radiologists. The approach, the researchers said, could eventually be applied to imaging modalities well beyond X-rays, including CT scans, MRIs, and echocardiograms.

“We’re living the early days of the next-generation medical AI models that are able to perform flexible tasks by directly learning from text,” said study lead investigator Pranav Rajpurkar, assistant professor of biomedical informatics in the Blavatnik Institute at HMS. “Up until now, most AI models have relied on manual annotation of huge amounts of data - to the tune of 100,000 images - to achieve a high performance. Our method needs no such disease-specific annotations.”

“With CheXzero, one can simply feed the model a chest X-ray and corresponding radiology report, and it will learn that the image and the text in the report should be considered as similar—in other words, it learns to match chest X-rays with their accompanying report,” Rajpurkar added. “The model is able to eventually learn how concepts in the unstructured text correspond to visual patterns in the image.”

Related Links:
Harvard Medical School 
Stanford University 

Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
Silver Member
Compact 14-Day Uninterrupted Holter ECG
NR-314P
New
Video Laryngoscope
SH-VL1

Print article

Channels

Critical Care

view channel
Image: The new risk assessment tool determines patient-specific risks of developing unfavorable outcomes with heart failure (Photo courtesy of 123RF)

Powerful AI Risk Assessment Tool Predicts Outcomes in Heart Failure Patients

Heart failure is a serious condition where the heart cannot pump sufficient blood to meet the body's needs, leading to symptoms like fatigue, weakness, and swelling in the legs and feet, and it can ultimately... Read more

Surgical Techniques

view channel
Image: The multi-sensing device can be implanted into blood vessels to help physicians deliver timely treatment (Photo courtesy of IIT)

Miniaturized Implantable Multi-Sensors Device to Monitor Vessels Health

Researchers have embarked on a project to develop a multi-sensing device that can be implanted into blood vessels like peripheral veins or arteries to monitor a range of bodily parameters and overall health status.... Read more

Patient Care

view channel
Image: The portable, handheld BeamClean technology inactivates pathogens on commonly touched surfaces in seconds (Photo courtesy of Freestyle Partners)

First-Of-Its-Kind Portable Germicidal Light Technology Disinfects High-Touch Clinical Surfaces in Seconds

Reducing healthcare-acquired infections (HAIs) remains a pressing issue within global healthcare systems. In the United States alone, 1.7 million patients contract HAIs annually, leading to approximately... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The Quantra Hemostasis System has received US FDA special 510(k) clearance for use with its Quantra QStat Cartridge (Photo courtesy of HemoSonics)

Critical Bleeding Management System to Help Hospitals Further Standardize Viscoelastic Testing

Surgical procedures are often accompanied by significant blood loss and the subsequent high likelihood of the need for allogeneic blood transfusions. These transfusions, while critical, are linked to various... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.