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 hp
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




AI-Driven Software Assists Radiologists in Reading Exams

By MedImaging International staff writers
Posted on 01 Mar 2018
Print article
Image: The software engine named Transpara DBT is intended to assist radiologists in reading digital breast tomosynthesis and mammography exams (Photo courtesy of ScreenPoint Medical).
Image: The software engine named Transpara DBT is intended to assist radiologists in reading digital breast tomosynthesis and mammography exams (Photo courtesy of ScreenPoint Medical).
An Artificial Intelligence (AI)-driven decision support software engine, which assists radiologists in reading digital breast tomosynthesis (DBT) and mammography exams on breast-reading workstations, was launched at the European Congress of Radiology (ECR), Vienna, Austria, February 28 – March 4, 2018. The software engine named Transpara DBT was launched by ScreenPoint Medical (Nijmegen, Netherlands), which develops and markets image analysis technology and services for automated reading of mammograms and digital breast tomosynthesis exams, exploiting Big Data, Deep Learning and the latest developments in AI.

Transpara DBT utilizes breakthrough image analysis and deep learning technologies for providing information to significantly improve reading workflow for DBT on breast reading workstations. It allows the reader to automatically jump to a relevant DBT slice in both the CC and MLO 3D data, by simply clicking on a suspicious region in a synthetic mammogram. Transpara DBT marks the lesion in the relevant slices and provides quantitative decision support for individual soft tissue lesions and calcifications, thereby improving the radiologists’ reading accuracy and confidence in their assessment.

Additionally, Transpara DBT combines the analysis of soft tissue lesions and calcifications, if present, from all available views of an exam to compute a single score for the case on a scale of 1 to 10. This represents categories with increasing occurrence of cancer. The Transpara Score can be used by healthcare professionals and organizations to automatically identify exams that are highly likely to be normal and to help identify cases that need increased attention.

The software engine is multi-vendor and communicates via DICOM, allowing integration into PACS and mammography reading workstations. The Transpara algorithms use the full 3D information in DBT data and have been trained on very large databases, including thousands of examples of breast cancer and false positives.

“Transpara DBT was developed with the goal of improving the efficiency of reading tomosynthesis exams,” said Prof. Nico Karssemeijer, PhD, CEO of ScreenPoint Medical. “By providing interactive decision support to radiologists, we aim to help all readers achieve better performance and improve their workflow, representing a significant innovation in breast cancer screening.”

Related Links:
ScreenPoint Medical

New
Gold Member
X-Ray QA Meter
T3 AD Pro
New
DRF DR & Remote Fluoroscopy Solution
CombiDiagnost R90
Portable Color Doppler Ultrasound Scanner
DCU10
New
Digital X-Ray Detector Panel
Acuity G4

Print article
Radcal

Channels

Radiography

view channel
Image: The new X-ray detector produces a high-quality radiograph (Photo courtesy of ACS Central Science 2024, DOI: https://doi.org/10.1021/acscentsci.4c01296)

Highly Sensitive, Foldable Detector to Make X-Rays Safer

X-rays are widely used in diagnostic testing and industrial monitoring, from dental checkups to airport luggage scans. However, these high-energy rays emit ionizing radiation, which can pose risks after... Read more

MRI

view channel
Image: Artificial intelligence models can be trained to distinguish brain tumors from healthy tissue (Photo courtesy of 123RF)

AI Can Distinguish Brain Tumors from Healthy Tissue

Researchers have made significant advancements in artificial intelligence (AI) for medical applications. AI holds particular promise in radiology, where delays in processing medical images can often postpone... Read more

Nuclear Medicine

view channel
Image: Example of AI analysis of PET/CT images (Photo courtesy of Academic Radiology; DOI: 10.1016/j.acra.2024.08.043)

AI Analysis of PET/CT Images Predicts Side Effects of Immunotherapy in Lung Cancer

Immunotherapy has significantly advanced the treatment of primary lung cancer, but it can sometimes lead to a severe side effect known as interstitial lung disease. This condition is characterized by lung... Read more

General/Advanced Imaging

view channel
Image: Cleerly offers an AI-enabled CCTA solution for personalized, precise and measurable assessment of plaque, stenosis and ischemia (Photo courtesy of Cleerly)

AI-Enabled Plaque Assessments Help Cardiologists Identify High-Risk CAD Patients

Groundbreaking research has shown that a non-invasive, artificial intelligence (AI)-based analysis of cardiac computed tomography (CT) can predict severe heart-related events in patients exhibiting symptoms... 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
Copyright © 2000-2024 Globetech Media. All rights reserved.