General |
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 |
Technology |
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 |
Certification |
|
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 |
Pricing model |
|
Based on |
|
Evidence |
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 122 969 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)
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