En Es
Categories

Industry News

AI Could Learn How to Understand Radiologist Reports

By Medimaging International staff writers
08 Feb 2018

Image: Researchers have taken an important first step in the development of artificial intelligence (AI) that could interpret scans and diagnose conditions (Photo courtesy of iStock).Researchers at the Icahn School of Medicine at Mount Sinai (New York, NY, USA) have used machine learning techniques, including natural language processing algorithms, to identify clinical concepts in radiologist reports for computed tomography (CT) scans. The technology marks an important first step in the development of artificial intelligence (AI) that could interpret scans and diagnose conditions.

AI is expected to help radiologists interpret X-rays, CT scans, and magnetic resonance imaging (MRI) studies, but requires computer software to be "taught" the difference between a normal study and abnormal findings. The researchers conducted a study to train AI technology to understand text reports written by radiologists by creating a series of algorithms to teach the computer clusters of phrases, such as phospholipid, heartburn, and colonoscopy.

Using 96,303 radiologist reports associated with head CT scans performed at The Mount Sinai Hospital and Mount Sinai Queens between 2010 and 2016, the researchers trained the computer software. They calculated metrics that reflected the variety of language used in these reports and compared them to other large collections of text, including thousands of books, Reuters news stories, inpatient physician notes, and Amazon product reviews in order characterize the "lexical complexity" of radiologist reports. The researchers found an accuracy of 91%, demonstrating that it is possible to automatically identify concepts in text from the complex domain of radiology.

"The language used in radiology has a natural structure, which makes it amenable to machine learning," said senior author Eric Oermann, MD, Instructor in the Department of Neurosurgery at the Icahn School of Medicine at Mount Sinai. "Machine learning models built upon massive radiological text datasets can facilitate the training of future AI-based systems for analyzing radiological images."

"The ultimate goal is to create algorithms that help doctors accurately diagnose patients," says first author John Zech, a medical student at the Icahn School of Medicine at Mount Sinai. "Deep learning has many potential applications in radiology -- triaging to identify studies that require immediate evaluation, flagging abnormal parts of cross-sectional imaging for further review, characterizing masses concerning for malignancy -- and those applications will require many labeled training examples."

Related Links:
Icahn School of Medicine at Mount Sinai



E-mail Print
FaceBook Twitter Google+ Linked in

Additional news

08 Jan 2019
New AI Technology Pinpoints Negative Symptoms in Cancer Patients
Researchers from the University of Surrey and the University of California have developed a new artificial intelligence (AI) tool, which can predict symptoms and their severity throughout the course of a cancer patient's treatment.
Read More
08 Jan 2019
Luminex Acquires MilliporeSigma Flow Cytometry Portfolio
Luminex Corporation has completed its previously announced acquisition of MilliporeSigma's flow cytometry portfolio.
Read More
31 Dec 2018
Deep Learning Technique Could Reveal Transparent Features in Medical Images
Engineers at the Massachusetts Institute of Technology have developed a deep learning technique that can reveal images of transparent features or objects that are nearly impossible to decipher in almost total darkness.
Read More
31 Dec 2018
Next-Gen Products Driving Global POC Molecular Diagnostics Market
The global point of care (POC) molecular diagnostics market is expected to grow at a CAGR of close to 14% during the period 2019-2023, driven mainly by the growing number of M&As and collaborations, and rising focus on next-generation products.
Read More
31 Dec 2018
Increased Breast Cancer Driving Global Digital Breast Tomosynthesis Market
The global digital breast tomosynthesis market is expected to grow at a CAGR of around 13% for the period 2019-2023, driven by the increasing incidence of breast cancer, growing popularity of digital breast tomosynthesis and consistent launch of new products.
Read More
31 Dec 2018
Global Urinalysis Market to Reach USD 1.5 Billion by 2024
The global urinalysis market is expected to grow at a steady CAGR of 5.40% over the 2016-2024 period to reach a value of USD 1.5 billion by the end of 2024, propelled mainly by the rising incidence of diabetes and urinal infections, globally.
Read More
31 Dec 2018
Bruker Completes Acquisition of Alicona Imaging
Bruker Corporation has completed its previously announced acquisition of Alicona Imaging GmbH, a provider of optical-based metrology products.
Read More
25 Dec 2018
New AI-Based Solutions Create Possibility for Early Cancer Screening
Two new Artificial Intelligence- (AI) based products released at this year’s Radiological Society of North America (RSNA) annual meeting in Chicago, USA, could create possibility for large-scale early cancer screening globally.
Read More
25 Dec 2018
Network-Based AI Engine Performs Airway Segmentation from CT Images
A new 2.5D convolutional neural network (CNN)-based artificial intelligence (AI) engine enables accurate airway segmentation from computed tomography (CT) images without any human interaction.
Read More
Copyright © 2000-2019 TradeMed.com. All rights reserved. | Terms And Conditions