Concurrent-Read AI System From QView Medical Reduces ABUS Exam Interpretation Time While Maintaining Diagnostic Accuracy

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October 11, 2018

QV Medical reports the first study evaluating reader performance of a concurrent-read AI CAD system, conducted by the University of Chicago, demonstrated that QVCAD from QView Medical reduced reader interpretation time of screening Automated Breast Ultrasound (ABUS) exams by 33 percent while maintaining diagnostic accuracy.

QV Medical notes QVCAD is FDA-Approved for concurrent reading of ABUS exams, the first approval for concurrent reading of radiology exams. Based on deep learning algorithms, the AI system is designed to detect suspicious areas of breast tissue that have characteristics similar to breast lesions and highlight suspicious area to distinguish potentially malignant lesions from normal breast tissue.

Results from several clinical studies have shown that the addition of ABUS to screening mammography results in a significant increase in cancer detection in women with dense breasts. However, the interpretation of ABUS exams, with up to 2,000 images per case, is complex and time consuming, particularly for new users.

In the study, “Interpretation time using a concurrent-read computer-aided detection system for automated breast ultrasound in breast cancer screening of women with dense breast tissue,” researchers conducted a Reader Study to compare diagnostic accuracy and interpretation time of screening ABUS for asymptomatic women with dense breast tissue with and without the use of the concurrent-read CAD system. In the study, published recently in the American Journal of Radiology (doi/10.2214/AJR.18.19516), 18 radiologists interpreted 185 screening ABUS studies from a large cohort of ABUS screened patients interpreted as BI-RADS density C or D. Each reader interpreted each case twice, once without the CAD system and once with it, four weeks apart. Case interpretation time was recorded as readers identified abnormal findings and reported BI-RADS assessment categories and level of suspicion for breast cancer.

According to Yulei Jiang PhD, the University of Chicago, Associate Professor of Radiology and Principal Investigator, the results demonstrate that the use of the concurrent-read CAD system can make interpretation of screening ABUS studies significantly faster without negatively affecting diagnostic accuracy. “A reduction in interpretation time would provide little value to radiologists and patients if accuracy suffered. However, sensitivity was preserved and the results also showed significant specificity improvements in some readers, which could translate to improved clinical confidence as well as productivity gains when used in a clinical environment,” said Jiang.

To improve reader productivity, QVCAD provides synthetic 2D images of all six volumetric datasets in a standard ABUS exam to provide an immediately visual overview of the case. The C-thru images, which are minimum intensity projections (MinIP), summarize each 3D ABUS volume in a 2D image and bring attention to specific areas of interest by enhancement of radial spiculations and retraction patterns in coronal reconstructions, which are highly suggestive of breast cancer in ABUS. Users may select any CAD mark or area of interest on the C-thru image and the corresponding original ABUS images will be displayed, enabling users to efficiently review the entire ABUS case.

“The Reader Study results show that QVCAD may be an important tool in reducing the learning curve for new ABUS users as well as increasing their confidence in interpreting ABUS exams – critical components to continue driving adoption of this very important adjunctive screening modality. In addition, we believe that use of QVCAD will reduce the incidence of “obvious oversight” cases, which are cancers that should have been detected but were missed due to fatigue, distractions or large caseloads,” said Bob Wang, QView Medical CEO.



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