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).

13/181 results

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Subspeciality: Chest
Modality: CT

Segmentation and volume quantification of lungs, lung lobes, lung lesions, heart, thoracic aorta, ...

The AI-Rad Companion Chest CT solution offers a multiorgan approach with pulmonary, cardiovascular and musculoskeletal functionalities. Major functionalities: lung lobe segmentation, lung lesion ...

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Subspeciality: Chest
Modality: CT

lung nodule detection, volume quantification, nodule tracking over time, Lung-RADS support, ...

Automatic detection of solid, part solid, and ground glass nodules. The user can add, edit, or delete detections with automatic updates to quantitative information. Lung AI supports Lung-RADS ...

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Subspeciality: Chest
Modality: CT

volume segmentation and quantification, volume density analysis, airway morphology , vascular ...

Thirona’s lung quantification software offers analysis of chest CT scans based on artificial intelligence, aiding in the detection, evaluation, and treatment planning of lung diseases. Analysis ...

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Subspeciality: Chest
Modality: CT

Lung lobe segmentation, Low Attenuation Analysis (LAA), Emphysema, Vessel Analysis, Fissure ...

AVIEW COPD gives COPD information based on a non-enhanced CT-scan of the thorax. Evaluation includes segmentation of lungs and lung lobes, low attenuation analysis, emphysema, vessel analysis, ...

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Subspeciality: Chest
Modality: CT

Nodule detection, nodule classification, nodule volume quantification, VDT, Lung-RADS score, ...

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

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Subspeciality: Chest
Modality: CT

lung nodule detection, report generation, multi-timepoint analysis

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 ...

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Subspeciality: Chest
Modality: CT

Lung density quantification, abnormal lung region mapping, report generation

Lung Density Analysis™ applies big data analysis to paired inspiratory / expiratory chest CT studies to provide visualization and quantification of lung regions with abnormal tissue density. ...

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Subspeciality: Neuro, Cardiac, MSK, Chest, Abdomen, Vascular Analysis
Modality: CT

Noise reduction of low dose CT

PixelShine is an algorithm for de-noising CT datasets supporting new CT protocols to drive patient dose down, while image quality is maintained. PixelShine may aid detectability of subtle pathologies ...

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Subspeciality: Chest
Modality: CT

nodule detection, segmentation and quantification, temporal registration and nodule comparison, ...

Veolity is a reading platform dedicated to lung screening program. It combines lung CAD of solid pulmonary nodules, integration and automatic registration of prior studies and creates the associated ...

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Yes
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Subspeciality: Chest
Modality: CT

Lung disease biomarkers, lung (lobe) volume quantification, scan suitability quality check, ...

VIDA Insights™ is a lung intelligence solution providing quantitative data and visualizations. VIDA’s biomarkers measure lung destruction, inflammation, function and more, to aid the detection, ...

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Subspeciality: Chest
Modality: CT

multiple time-point analysis

Nodule tracking for differential measurements. Utilizing ClearRead CT Detect, ClearRead CT Compare tracks nodules and provides nodule volumetric changes over time for solid, part-solid, and ground ...

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Subspeciality: Chest
Modality: CT

lung nodule detection

Riverain’s ClearRead CT Detect detects lung nodules in CT images.

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Subspeciality: Chest
Modality: CT

vessel suppression

Riverain’s ClearRead CT Vessel Suppress is powered by machine learning and advanced modeling and suppresses vascular structures. The vessel suppression algorithm works with contrast and non-contrast ...

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