ProFound AI for 2D Mammography

Designed for radiologists, ProFound AI™ for 2D Mammography analyzes each image, detecting both malignant soft tissue densities and calcifications.
Information source: Vendor
Last updated: June 20, 2023

General Information

Product name ProFound AI for 2D Mammography
Company iCAD
Subspeciality Breast
Modality Mammography
Disease targeted Breast cancer
Key-features Malignancy detection, calcification detection, certainty scoring, case scoring
Suggested use Before: adapting worklist order
During: perception aid (prompting all abnormalities/results/heatmaps)

Technical Specifications

Data characteristics
Population Asymptomatic screening population
Input full field 2D digital mammography
Input format DICOM
Output Leasion score with segmentation overlay, case score, choice in operating point (high/med/low)
Output format DICOM Structured Report, GSPS, Secondary Capture
Integration Integration in standard reading environment (PACS), Stand-alone third party application
Deployment Locally virtualized (virtual machine, docker)
Trigger for analysis Automatically, right after the image acquisition
Processing time 1 - 10 minutes


Certified, Class IIa , MDD
FDA No or not yet
Intended Use Statements
Intended use (according to CE) iCAD ProFound AI for FFDM or 2D software is a computer-assisted detection and diagnosis (CAD) Artificial Intelligence (AI) software device intended to be used concurrently by interpreting physicians while reading 2D mammography exams from compatible 2D mammography systems. The system detects soft tissue densities (masses, architectural distortions and asymmetries) and calcifications in mammography images. The detections and Certainty of Finding and Case Scores assist interpreting physicians in identifying soft tissue densities and calcifications that may be confirmed or dismissed by the interpreting physician.


Market presence
On market since 06-2019
Distribution channels Tempus Pixel, RMS Medical Devices, Sectra Amplifier Store, GE and local Distributors
Countries present (clinical, non-research use) 25
Paying clinical customers (institutes) 1300
Research/test users (institutes) 30+
Pricing model
Based on


Peer reviewed papers on performance

  • Using artificial intelligence in the diagnosis of breast cancer: First results after implementation in a radiology department of a breast clinic (read)

Non-peer reviewed papers on performance
Other relevant papers

  • Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An Observational Study (read)