Transpara provides decision support during reading of 2D Full-Field Digital Mammography (FFDM) and 3D Digital Breast Tomosynthetis (DBT) to help improve clinical accuracy, reduce workload and optimize workflow.
Product specifications Information source: Vendor
Last updated: Sept. 27, 2023
Product name Transpara
Company ScreenPoint Medical
Subspeciality Breast
Modality Mammography
Disease targeted Breast cancer
Key-features Detection Aid, Region Analysis, Exam Score, Risk score
Suggested use Before: stratifying reading process (non, single, double read), adapting worklist order
During: perception aid (prompting all abnormalities/results/heatmaps), interactive decision support (shows abnormalities/results only on demand)
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
Certified, Class IIb , MDR
510(k) cleared, Class II
Market presence
On market since 09-2015
Distribution channels Incepto Medical, Volpara Health, Siemens Healthineers, Agfa Healthcare, Fujifilm, HumanBytes, Sectra Amplifier Store, Aidoc aiOS, Fomei and Medical Solutions, Calantic
Countries present (clinical, non-research use) 30+
Paying clinical customers (institutes) Non-disclosed
Research/test users (institutes) Non-disclosed
Pricing model
Based on
Peer reviewed papers on performance

  • Transpara 2D, 1.7.0: Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study (read)

  • Transpara 2D, 1.7.0: Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases (read)

  • Transpara 2D, 1.7.0: Multi-modal artificial intelligence for the combination of automated 3D breast ultrasound and mammograms in a population of women with predominantly dense breasts (read)

  • Transpara 2D, 1.7.0: Artificial Intelligence Evaluation of 122‚ÄČ969 Mammography Examinations from a Population-based Screening Program (read)

  • Transpara 2D+3D, 1.7.0: Stand-Alone Use of Artificial Intelligence for Digital Mammography and Digital Breast Tomosynthesis Screening : A Retrospective Evaluation (read)

  • Transpara 3D, 1.7.0: AI Detection of Missed Cancers on Digital Mammography That Were Detected on Digital Breast Tomosynthesis (read)

  • Transpara 3D, 1.6.0: Impact of Artificial Intelligence Decision Support Using Deep Learning on Breast Cancer Screening Interpretation with Single-View Wide-Angle Digital Breast Tomosynthesis (read)

  • Transpara 2D+3D, 1.6.0: AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation (read)

  • Transpara 3D, 1.6.0: Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study (read)

  • Transpara 2D, 1.5.0: Can artificial intelligence reduce the interval cancer rate in mammography screening? (read)

  • Transpara 2D, 1.3.0: Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women Breast Cancer (read)

  • Transpara 2D, 1.4.0: Identifying normal mammograms in a large screening population using artificial intelligence (read)

  • Transpara 2D, 1.4.0: Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study (read)

  • Transpara 2D, 1.3.0: Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System (read)

  • Transpara 2D, 1.4.0: Stand-alone artificial intelligence for breast cancer detection in mammography: Comparison with 101 radiologists (read)

  • All papers can be found here.

Non-peer reviewed papers on performance
Other relevant papers

  • Assessing Breast Cancer Risk by Combining AI for Lesion Detection and Mammographic Texture (read)

  • Impact of Artificial Intelligence System and Volumetric Density on Risk Prediction of Interval, Screen-Detected, and Advanced Breast Cancer (read)

  • 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)