Products
Find the artificial intelligence based software for radiology that you are looking for.
All products listed are available for the European market (CE marked).
14/210 results
Modality: Mammography
Breast density classification (ACR BI-RADS), real time image quality assessment, lesion and ...
Stand-alone AI system for assessment of mammographic breast density, real-time evaluation of image quality, lesion detection and mechanism for personalized suggestion of supplemental diagnostics.
Modality: Mammography
Lesion detection, segmentation, breast cancer risk estimation, automated breast density assessment
Breast AI provides solutions for breast cancer detection, measure breast density and assess personalized risk that are aimed at to optimizing efficiency, enhancing the patient experience, and ...
Modality: Mammography
Breast density classification
**Only available with Visage 7 Enterprise Imaging Platform and Hologic equipment** Visage Breast Density is a software application intended for use with compatible full field digital mammography and ...
Modality: Mammography
malignancy detection, calcification detection, certainty scoring, case scoring
Designed for radiologists, ProFound AI™ for 2D Mammography analyzes each image, detecting both malignant soft tissue densities and calcifications.
Modality: Mammography
malignancy detection, calcification detection, certainty scoring, case scoring
The concurrent-read, cancer detection and workflow solution from iCAD analyzes each tomosynthesis image, detecting both malignant soft tissue densities and calcifications. Built on deep-learning and ...
Modality: Mammography
Lesion detection and characterization, level-of-suspicion score
MammoScreen helps radiologists to identify findings on screening mammograms and assess their level of suspicion. MammoScreen® uses a color coded MammoScreen Score™ to provide an at a glance ...
Modality: Mammography
Independent second read, recall suggestion, suspicious region marking, image indications
Mia supports radiologists in making the recall decision in breast cancer screening. Available to use in various configurations such as an independent second reader, a concurrent reader or in double ...
Modality: MR
T2 values, T2 Histogram
Quibim has developed this suite to automatically obtain the T2 mapping of tumors and depict changes and abnormalities with AI segmentation. The analysis of T2 mapping provides imaging biomarkers for ...
Modality: MR
Lesion segmentation and volumetry, DCE and ADC quantification, lesion follow-up, BI-RADS report, ...
breastscape contains 2 different applications: BreastApp and BreastLoc BreastApp: Semi automatic solution to aid breast lesion diagnosis in MRI with dedicated viewing tools, automatic lesion ...
Modality: Mammography
exam suspicion score, worklist prioritization
An automatic AI tool that indicates ‘suspicious’ or ‘not suspicious’ for every 2D mammography performed (that matches the criteria, e.g. no previous cancer, no implants etc.) *Previously part of ...
Modality: MR
ADC Map, Histogram, Volume of smallest bvalue, values of ADC parameters
Quibim’s Diffusion Weighted Imaging (DWI) - ADC enables to automatically calculate the apparent diffusion coefficient (ADC) to quantify the diffusion of water molecules in tissues. The suite provides ...
Modality: X-ray, CT, MR, PET, Ultrasound, SPECT
Quantification, Radiomics, imaging biomarker discovery
Quibim has developed the Texture Analysis to quantify features related to the heterogeneity of the tissues and obtain texture parameters.
Modality: MR
Parametric maps, Diffusion coefficient, Psuedodiffusion coefficient, Perfusion fraction
Quibim’s Diffusion Weighted Imaging (DWI) – IVIM automatically performs intra-voxel incoherent motion (IVIM) analysis to detect changes in the microstructure of tissues to assess cancer processes in ...
Modality: Mammography
Images negativation, risk mapping, breast density.
Breast-SlimView is a clinical decision support solution for 2D/3D mammography. It has learnt to detect what is normal from a database of mammograms, then, based on this normalized database, it learns ...