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

Download Mobile App

New AI Tool Reduces Radiologists Read Times by 40%

By MedImaging International staff writers
Posted on 22 May 2023
Print article
Image: The new AI tool helps radiologists reduce read times by up to 40% (Photo courtesy of Freepik)
Image: The new AI tool helps radiologists reduce read times by up to 40% (Photo courtesy of Freepik)

Locating patients' previous examinations can be a significant time drain in a radiologist's workflow. Now, a new study suggests that deep learning technology could help considerably lessen this load.

A study by researchers with the Department of Radiology at University Hospital Basel (Basel, Switzerland) has shown that employing a deep learning tool to identify patients' relevant past imaging can reduce radiologists' interpretation times by about 40%. The tool named TimeLens proved effective in identifying exams and highlighting significant findings from past exams for radiologists to examine. TimeLens is built on natural language processing and descriptor-based image-matching algorithms. The findings suggest that reducing the time spent by radiologists to manually search for previous exams could greatly benefit both patients and healthcare providers.

For the study, the researchers assessed the reading times and habits (clicks, mouse movements, etc.) of radiologists using the tool on 3,872 series of 246 radiology examinations from 75 patients (189 CTs, 95 MRIs). With the use of TimeLens, the average time needed to examine a finding at any given timepoint dropped from 107 to 65 seconds, with the most significant reductions observed in pulmonary nodule assessments. Since the tool identified relevant past exams for the readers, its use also resulted in over 17% fewer clicks and a 38% reduction in mouse travel.

Another notable observation was that younger, less experienced radiologists were quicker when using the tool compared to their more experienced counterparts. The researchers believe this is due to the younger generation being more accustomed to digital technology, hence adapting to new technologies with ease. While the exact financial impact of time savings from not having to locate past imaging is challenging to determine, the team estimated, using NHS data, that utilized the deep learning tool could have saved 1,145 days of reading time within the NHS system alone in August 2021, based on 780,000 cross-sectional imaging examinations with an average of three findings per study requiring comparison.

“The time-intensive nature of this longitudinal assessment is not only suboptimal in the setting of increasing radiology workloads, but can also result in limited review of relevant prior examinations, putting interpretive accuracy and report quality at risk,” noted Thomas Weikert, MD, corresponding author with the Department of Radiology at University Hospital Basel.

Related Links:
University Hospital Basel 

Gold Supplier
Electrode Solution and Skin Prep
Gold Supplier
Ultrasound System
Ferromagnetic Hand-Held Detector
FerrAlert Target Scanner
Forensic Imaging System

Print article



view channel
Image: Intelligent NR provides high-quality diagnostic images containing significantly less grainy noise (Photo courtesy of Canon)

AI-Driven DR System Produces Higher Quality Images While Limiting Radiation Doses in Pediatric Patients

Ionizing radiation is a fundamental element in producing diagnostic X-rays, yet it's widely acknowledged for its cancer risk potential. Digital projection radiography, a vital imaging modality, accounts... Read more


view channel
Image: The researchers are using MRI-guided radiation therapy that pairs daily MRIs with radiation treatment (Photo courtesy of Sylvester)

AI Technique Automatically Traces Tumors in Large MRI Datasets to Guide Real-time Glioblastoma Treatment

Treating glioblastoma, a prevalent and aggressive brain cancer, involves the use of radiation therapy guided by CT imaging. While this method is effective in targeting radiation, it doesn't provide real-time... Read more


view channel
Image: The new ultrasound patch can measure how full the bladder is (Photo courtesy of MIT)

Ultrasound Patch Designed to Monitor Bladder and Kidney Health Could Enable Earlier Cancer Diagnosis

Bladder dysfunction and related health issues affect millions worldwide. Monitoring bladder volume is crucial for assessing kidney health. Traditionally, this requires a visit to a medical facility and... Read more

Nuclear Medicine

view channel
Image: A novel PET radiotracer facilitates early, noninvasive detection of IBD (Photo courtesy of Karmanos)

New PET Radiotracer Aids Early, Noninvasive Detection of Inflammatory Bowel Disease

Inflammatory bowel disease (IBD), which includes Crohn’s disease and ulcerative colitis, is an inflammatory condition of the gastrointestinal tract affecting roughly seven million individuals globally.... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more

Industry News

view channel
Image: Attendees can discover innovative products and technology in the RSNA 2023 Technical Exhibits (Photo courtesy of RSNA)

RSNA 2023 Technical Exhibits to Offer Innovations in AI, 3D Printing and More

The 109th Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA, Oak Brook, IL, USA) to be held in Chicago, Nov. 26 to 30 is all set to offer a vast array of medical... Read more
Copyright © 2000-2023 Globetech Media. All rights reserved.