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: Chest
Modality: CT
Source: Vendor
Growth calculation (prior study retrieval), Nodule classification, Nodule detection, Volume quantification
This AI assistant supports radiologists with detecting, classifying and tracking the growth of pulmonary nodules. DeepHealth Lung Check (previously Saige Lung and Veye Lung Nodules by Aidence) integrates into the PACS and is currently in use across Europe in both routine clinical practice and lung cancer screening programs.
Subspeciality: Chest
Modality: X-ray
Source: Vendor
Abnormality score, Current-prior comparison, Flagging normal exams, Radiologic finding detection, Text interpretation
Lunit INSIGHT CXR is deep learning based software that assists radiologists or clinicians in the interpretation of chest x-ray (PA/AP). The AI solution automatically detects 11 radiologic findings including atelectasis, calcification, cardiomegaly, consolidation, fibrosis, mediastinal widening, nodule, pleural effusion, pneumoperitoneum, acute bone fracture, and pneumothorax, and also supports tuberculosis screening.
Subspeciality: Cardiac
Modality: Ultrasound
Source: Vendor
All 4 chambers of the heart), Automated measurements include 2-dimensional (cardiac volumes, Both pw and cw measurements) and tissue doppler, M-mode (e.g. tricuspid annular plane systolic excursion), Spectral doppler (blood flow across all valves
Us2.ai supports cardiovascular disease diagnostics by providing machine learning based solutions that analyze and interpret echocardiograms, which are ultrasound images of the heart, creating patient reports, at scale, on mobile, on premises and cloud-based platforms.
Subspeciality: Breast
Modality: Mammography
Source: Vendor
Individual 1y/2y/3y breast cancer risk score
ProFound AI risk is a clinical decision support tool that provides personalized one-, two-, and three-year breast cancer risk estimates based on 2D screening mammograms or 3D breast tomosynthesis. ProFound AI Risk combines a range of risk factors (age, density, subtle mammographic features) to produce short-term risk assessments that physicians can use to create customized screening plans and catch early-stage cancers.
Subspeciality: Chest, MSK
Modality: X-ray
Source: Vendor
Detection of appendicular skeleton fracture, Elbow joint effusion, Foot and spine. determine bone age., Joint dislocation., Leg, Pelvis, Pleural effusion, Pulmonary nodules, Pulmonary opacities and pneumothorax. measurement of shoulder, Rib cage fracture
TechCare Kids performs detection, measurements and bone age assessment in pediatric imaging.
- Detection: Aims to identify abnormalities across the thoracic and appendicular skeleton pathologies in pediatric patients.
- Measurements: Analyzes skeletal structures, including the shoulder, spine, hip/pelvis, leg, and foot.
- It aims to determine the bone age.
Subspeciality: Breast
Modality: Mammography
Source: Vendor
Detection Aid, Exam Score, Region Analysis, Risk score
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. Transpara is intended to support healthcare professionals with detection and diagnosis of breast lesions or for assessment of breast density and breast cancer risk using radiological images of the breast. Version 2.1 includes temporal comparison up to 3 exams.
Subspeciality: MSK
Modality: X-ray
Source: Vendor
Fracture detection
AZtrauma is a computer-assisted detection and diagnosis (CAD) software device to assist radiologists and emergency physicians in detecting fractures during the review of radiographs of the musculoskeletal system for ankle, clavicle, elbow, forearm, hip, humerus, knee, pelvis, shoulder, tibia, fibula, wrist, hand, foot.
Subspeciality: Breast
Modality: Mammography
Source: Vendor
Breast density classification (ACR BI-RADS), Lesion and microcalcification detection, Population summary dashboard, Real time image quality assessment
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.
Subspeciality: Chest
Modality: CT
Source: Vendor
CAC-score, Emphysema score, Lung-RADS score, Nodule classification, Nodule detection, Nodule volume quantification, VDT
The LCS+ software from Coreline Soft provides information on the 3 main lung diseases: lung cancer, COPD and coronary artery calcification based on a Low Dose, non-enhanced, non-gated CT-scan
Subspeciality: Breast
Modality: Mammography
Source: Vendor
Abnormality score, Breast cancer detection, Current-prior comparison, Density assessment, Lesion type
Lunit INSIGHT MMG is deep learning based software that assists radiologists in the interpretation of mammograms. The AI solution automatically detects suspicious areas for breast cancer on mammograms including mass, calcification, distortion and asymmetry. The analysis result contains (1) localization of suspicious areas in color or outline and (2) abnormality scores reflecting the probability that the detected area is malignant.
Subspeciality: MSK
Modality: X-ray
Source: Vendor
Detection of fractures, Dislocations and bone lesions, Effusions, Worklist prioritization
BoneView detects fractures, effusions, dislocations and bone lesions, and gives 3 different pre-diagnosis labels on the images: - POSITIVE when the confidence for the presence of a lesion is above 90% (plain bounding box around the region of interest) - DOUBT when the confidence for the presence of a lesion is between 50% and 90% (dotted bounding box around the region of interest) - NEGATIVE otherwise BoneView provides a summary table for quality check and overview of AI outputs at a glance, and can perform worklist prioritization according to the different pre-diagnoses. *In the United States, BoneView has been 510k FDA-cleared for pediatric use and adults (>2 years old for limbs).
Subspeciality: Multiple
Modality: MR
Source: Vendor
CE
FDA
MRI image enhancement, MRI scan acceleration
SwiftMR aims to accelerate MRI scans by up to 50% by enhancing low-quality initial outputs from accelerated scans. The deep-learning model improves SNR and resolution of MRI inputs, irrespective of body part or vendor type. Operating in DICOM format, SwiftMR receives assigned scans from connected MRIs and dispatches results to PACS. Installable via cloud or on-premise. *Product coverage and supported types may vary by region depending on regulatory status.