– The Intelligent Interactive Educational Technology from DetectedX has Shown a 34% Improvement in the Accuracy of Diagnosing Difficult Cases
DetectedX (https://detectedx.com/) announced today that it will launch its Radiology Online Learning Platform to radiologists in the United States at the SBI/ACR Breast Imaging Symposium 2022, in Savannah, GA, May 16-19 (Booth #221). Designed to improve radiologists’ ability to correctly detect breast lesions in 2D and 3D Mammography, the online self-assessment modules have been shown to improve cancer detection by over 34 percent.
As part of the U.S. launch, DetectedX will also release the next generation of its intelligent interactive educational platform, which features micro-learning tools, including quizzes and expanded educational content and videos. The new learning tools will feature new breast and lung educational content, as well as Medical Physics, Radiation and Artificial Intelligence topics.
For more information or to schedule an appointment, visit DetectedX SBI 2022 (https://detectedx.com/sbi-2022/)
DetectedX was founded to help radiologists and doctors worldwide to diagnose cases of breast cancer (https://detectedx.com/platforms/breast-cancer/), lung cancer (https://detectedx.com/chest/), and COVID-19 (https://detectedx.com/chest/) faster and more accurately. The on-demand, web-based training platform, which improves radiological detection rates based on intelligent interactive educational technology, is currently in use by more than 3,000 users in more than 150 countries, including a number of national screening services and international professional societies in Australia, Ireland, New Zealand, Slovenia, Italy and Vietnam. In addition, DetectedX has marketing and distribution partnerships with Volpara Health, Fujifilm and GE Healthcare.
“The online mammographic test sets available through DetectedX are invaluable to the education of radiologists and trainees,” said Dianne Georgian-Smith MD, FSBI, FACR.
DetectedX’s innovative educational technology enables Radiologists to review, in real time, an enriched cohort of 2D mammography and Digital Breast Tomosynthesis (3D mammography) cases with varying levels of difficulty. The on-demand, online training packages provide immediate feedback on reading performance, comparing the user’s classification to the pathology-verified ground truth for each case. Using several metrics including sensitivity, specificity, true positive, true negative, false positive and false negative scores to gauge their performance, radiologists are able to identify errors and focus future trainings on areas of need to improve performance for earlier and more accurate disease diagnoses. Upon successful completion of each training module, users receive Continuing Medical Education (CME) (https://detectedx.com/cme-cpd-points/) credits.
“High quality, accurate readings of mammograms are critical to the early detection of breast cancer. However, varying levels of skill and experience among radiologists reading mammograms can contribute to interpretation errors and variations, which can have a significant impact on patient care and outcomes,” said Professor Patrick Brennan, CEO DetectedX and Chair, Diagnostic Imaging, University of Sydney. “Our ultimate goal with the Radiology Online Learning Platform is to help radiologists diagnose breast cancer faster and more accurately, giving women worldwide a better chance of cancer survival.”
DetectedX’s Radiology Online Learning Center, focusing on diagnostic accuracy and driven by artificial intelligence, is revolutionizing disease detection in 150 countries. The on-demand, web-based training platform has been proven to improve the accuracy of diagnosing difficult cases by 34%. DetectedX was founded by Professor Patrick Brennan, a leading medical radiation scientist with $40+M in research funding and 500+ publications, Prof. Mary Rickard, Australia’s number one breast imaging clinician expert scientist who pioneered Australia’s Breast Screening program, and Dr. Moe Suleiman, one of the world’s leaders on optimizing radiologic interpretation.