Find the artificial intelligence based software for radiology that you are looking for. All products listed are available for the European market (CE marked).
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Subspeciality: MSK
Modality: X-ray
Source: Public
Subspeciality: MSK
Modality: MR
Source: Public
Classification of cartilage injuries according to ICRS, Report generation
mdknee enables AI-based evaluation of knee MRIs through automatic detection and classification of cartilage damage according to the International Cartilage Regeneration & Joint Preservation Society (ICRS) grading system. The output includes a quantitative report and visual overlays integrated into the PACS system, with affected areas mapped to specific anatomical regions such as the femur, tibia, and patella.
Subspeciality: Neuro
Modality: CT
Source: Public
ASPECT score calculation, Dynamic evaluation, Intracranial hemorrhage detection and quantification, Midline shift detection
No description available
Subspeciality: Breast
Modality: Ultrasound
Source: Public
CE
FDA
Breast abnormality detection, Region analysis
The QVCAD System is indicated for use as an aid to the radiologist during breast screening procedures for women who have negative mammograms but have had a 3D breast ultrasound (ABUS") because of her dense breast tissue. The QVCAD CAD engine employs several image pattern recognition processes and artificial neural networks to detect suspicious areas in the breast. The objective is to distinguish potential breast lesions from normal breast tissue.
Subspeciality: Abdomen
Modality: MR
Source: Public
Volumetric liver fat estimation
HepaFat-Scan is a non-invasive MRI-based solution for measuring the volume fraction of fat in liver tissue. HepaFat-Scan is provided as an image analysis service. HepaFat-Scan reports volumetric fraction of fat in the liver, a measure that can be directly compared to a volumetric biopsy fat fraction measurement.
Subspeciality: Abdomen
Modality: MR
Source: Public
Liver iron concentration estimation
FerriSmart is an automated system for measuring liver iron concentration (LIC). FerriSmart uses a specially trained convolutional neural network to analyse R2-MRI images to quantify the patient's LIC.
Subspeciality: MSK
Modality: CT
Source: Vendor
Fracture detection, Report generation
Nanox.AI's bone health algorithm detects vertebral compression fractures and can be applied to any CT of the chest or abdomen. Zebra's bone health solution integration occurs in the background as scans are being forwarded to the Zebra server for analysis. Zebra's insights are then gathered to a structured report which includes all positive cases identified by the algorithm for the medical staff to review. A sagittal image is provided per each positive case where the algorithm detected a fracture. *Previously part of Zebra Medical Vision
Subspeciality: Neuro
Modality: MR
Source: Vendor
ADC, E.g. tissue flow (perfusion) and tissue blood volume (rcbf, FLAIR), Isodwi, MTT, Rcbv, Showing properties of changes in contrast over time, Tmax, Trace, TTP, Visualization and analysis of dynamic imaging data
Brainomix 360 e-MRI provides both viewing and analysis capabilities for imaging datasets acquired with MRI including Perfusion Weighted Imaging (PWI) and Diffusion Weighted Imaging (DWI). The DWI MRI analysis capabilities are used to visualize local water diffusion properties from the analysis of diffusionweighted MRI data. The MRI PWI analysis capabilities are for visualization and analysis of dynamic imaging data, showing properties of changes in contrast over time. This functionality includes calculation of parameters related to tissue flow (perfusion) and tissue blood volume.
Subspeciality: Chest
Modality: CT
Source: Vendor
Lung nodule detection, Multi-timepoint analysis, Report generation
InferRead CT Lung is a processing solution for lung cancer screening. It recognizes the core features of lung cancer, determine the characteristics of suspected lung nodules in different image sequences and aims to aid early-stage diagnosis. This solution provides information on nodules, including position, size, density, malignancy rate and evolution.
Subspeciality: Chest
Modality: CT
Source: Vendor
Automated measurements of nodule diameter, Depth, Lung nodule detection, Volume
Riverain's CT Detect technology provides measurements for each detected region of interest including location, type, volume, maximum, minimum, average axial plane diameters, and nodule depth. All nodule types are detected (solid, part-solid, ground glass) at or above 5mm.
Subspeciality: Chest
Modality: CT
Source: Public
Lung-RADS reporting, Nodule classification, Nodule detection, Nodule measurement
It detects and quantifies pulmonary nodules, providing size, volume, nodule type, location, calcification, and spiculation. An automatic report based on the calculated Lung-RADS category is produced to assist in managing pulmonary nodules. Its follow-up registration and nodule matching aid the comparison of serial CT scans. Operation settings can be customized between sensitivity-oriented for high-risk patients and specificity-oriented for efficient screening.
* Formerly VUNO Med–LungCT AI from VUNO; recently transferred to Coreline Soft.
Subspeciality: Chest
Modality: CT
Source: Vendor
Automatic report generation, GGO etc.), Key parameter quantification, Lung nodule detection, Nodule tracking over time, Nodule type classification (solid
SenseCare Lung CT automatically detects pulmonary nodules and pneumonia (including COVID-19) lesions and provides analysis such as lesion classification, risk evaluation, quantification, and structured reports for radiologists. Based on 3D rendering technology, it can also provide a 3D reconstruction.