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ScreenPoint Medical Presents AI Application for Detecting Breast Cancer

By MedImaging International staff writers
Posted on 28 Feb 2019
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Image: The Transpara AI system automatically identifies soft-tissue and calcification lesions and combines the findings of all available views into a single cancer suspiciousness score (Photo courtesy of ScreenPoint Medical).
Image: The Transpara AI system automatically identifies soft-tissue and calcification lesions and combines the findings of all available views into a single cancer suspiciousness score (Photo courtesy of ScreenPoint Medical).
ScreenPoint Medical (Nijmegen, Netherlands) presented the latest version of its Transpara Artificial Intelligence (AI) system at ECR 2019 in Vienna.

ScreenPoint Medical develops image analysis technology for automated reading of mammograms and digital breast tomosynthesis exams, exploiting big data, deep learning and the latest developments in AI. The company’s Transpara processes 2D, 3D and combo mammography exams and is integrated in the workstations of an increasing number of OEM partners.

Utilizing state-of-the-art image analysis and deep learning technology, Transpara automatically identifies soft-tissue and calcification lesions and combines the findings of all available views into a single cancer suspiciousness score. Breast imaging professionals can use this Transpara Score to automatically identify exams that are highly likely to be normal and the exams with features indicating highly increased risk of cancer. Interactive decision support is provided to improve assessment of lesions. Information is provided concurrent during reading and only when needed, as a result of which, it does not slow down reading.

Transpara is the first AI application for detecting breast cancer in screening mammograms to gain 510(k) Clearance from the FDA, based on its unique functionality, which interactively provides support for detection and diagnosis. The clearance was supported by the results of a multi-reader, multi-case Reader Study, which demonstrated that radiologists significantly improved detection accuracy when using Transpara for decision support without increasing reading times. In the study, the stand-alone sensitivity and specificity of Transpara was nearly at the same level as that of radiologists.

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