Transpara is a product for mammography reading. Algorithms in Transpara™ use image analysis and deep learning technology. Key features of Transpara are: Decision support for suspicious areas, CAD, and exam selection for pre-screening.
Product specifications Information source: Vendor
Last updated: Feb. 10, 2020
Product name Transpara
Company ScreenPoint Medical
Subspeciality Breast
Modality Mammography
Disease targeted Breast cancer
Key-features Perception Aid, Region Analysis, Exam Score, Risk score
Suggested use Before: stratifying reading process (non, single, double read), adapting worklist order, flagging acute findings
During: perception aid (prompting all abnormalities/results/heatmaps), interactive decision support (shows abnormalities/results only on demand), report suggestion
After: diagnosis verification
Data characteristics
Population Asymptomatic women
Input 2D Full-Field Digital Mammography, 3D Digital Breast Tomosynthesis
Input format DICOM
Output Region findings, region scores and an exam score
Output format DICOM Mammography CAD Structured Report
Integration Integration in standard reading environment (PACS), Stand-alone third party application
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker)
Trigger for analysis Automatically, right after the image acquisition
Processing time 1 - 10 minutes
CE Certified, Class IIa
FDA 510(k) cleared, Class II
Market presence
On market since 09-2015
Distribution channels Incepto Medical, Volpara Solutions, Siemens Healthineers and Agfa Healthcare
Countries present (clinical, non-research use) Non-disclosed
Paying clinical customers (institutes) Non-disclosed
Research/test users (institutes) Non-disclosed
Pricing model Non-disclosed
Based on Non-disclosed
Peer reviewed papers on performance

  • Transpara 1.4.0: Stand-alone artificial intelligence for breast cancer detection in mammography: Comparison with 101 radiologists (read)

  • Transpara 1.3.0: Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System (read)

  • Transpara 1.4.0: Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study (read)

Non-peer reviewed papers on performance
Other relevant papers

  • Computer-aided Detection of Masses at Mammography: Interactive Decision Support versus Prompts (read)

  • Standalone computer-aided detection compared to radiologists' performance for the detection of mammographic masses (read)

  • Using Computer Aided Detection in Mammography as a Decision Support (read)