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Diagnostic algorithm ProFound AI®
analysistreatmentscreening

Diagnostic algorithm - ProFound AI® - iCAD - analysis / treatment / screening
Diagnostic algorithm - ProFound AI® - iCAD - analysis / treatment / screening
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Diagnostic algorithm - ProFound AI® - iCAD - analysis / treatment / screening - image - 3
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Diagnostic algorithm - ProFound AI® - iCAD - analysis / treatment / screening - image - 7
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Characteristics

Function
diagnostic, analysis, treatment, screening
Applications
clinical, for mammography
Area of the body
breast
Type
3D, AI-assisted
Deployment mode
for tablet PC

Description

Expertly engineered to enhance cancer screening and diagnostics, augment radiologist performance, and improve patient lives, ProFound AI offers concurrent-read, cancer-detection, and workflow solutions that rapidly and accurately analyzes each tomosynthesis image, detecting both malignant soft tissue densities and calcifications with unrivaled accuracy. Clinical Confidence Superior cancer detection drives improved outcomes Reduced false positives improves patient satisfaction Earlier cancer detection improves treatment options Operational Superiority ~52.7 reduction in radiologist reading time 6-7% improvement in radiologist AUC performance Faster image processing vs. other AI solutions Competitive Differentiation Differentiate your center with the most advanced clinical technology Enhanced patient satisfaction with improved screening service Aligns to quality improvement and accreditation programs How the Algorithm works. The ProFound AI algorithm is a highly accurate and clinically proven early cancer detection tool designed to be used concurrently while reading mammogram images. The algorithm has undergone extensive training using one of the most comprehensive 3D image datasets available. Developed from: 6+ million training images ~8,000 biopsy-proven cancers 100+ contributing centers ensuring data diversity A comprehensive ally to cancer detection: Powered by deep-learning technology, the algorithm leverages convolutional neural networks for image processing and pattern recognition. It meticulously examines mammography slices to localize, segment, and classify areas that warrant attention.

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