Ligence has developed a platform for automated analysis and reporting of 2D transthoracic echocardiography studies. The Ligence Heart platform reads DICOM format data, recognises image views, heart cycle, performs measurements and generates a report based on findings. It allows medical practitioners to skip manual measurements during the examination.
Product specifications Information source: Vendor
Last updated: April 29, 2022
Product name Ligence Heart
Company Ligence
Subspeciality Cardiac
Modality Ultrasound
Disease targeted Heart failure, cardiomyopathy of any type, pulmonary hypertension, valvular heart diseases
Key-features Volume quantification, area quantification, distance quantification, disease recognition, Doppler modality quantification
Suggested use During: perception aid (prompting all abnormalities/results/heatmaps), interactive decision support (shows abnormalities/results only on demand), report suggestion
Data characteristics
Population Adult, 18+ years old, no congenital malformations, no arrhythmia
Input 2D TTE DICOM images, loops
Input format DICOM loops
Output Segmentation overlay, measurements, draft examination report, table of quantified values
Output format Web, free text, CSV, PDF
Integration Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration CIS (Clinical Information System), Integration via AI marketplace or distribution platform, Stand-alone webbased
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker)
Trigger for analysis Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload, etc.
Processing time 10 - 60 seconds
Certified, Class IIa , MDR
No or not yet
Market presence
On market since 04-2022
Distribution channels Alma Medical Imaging
Countries present (clinical, non-research use) 2
Paying clinical customers (institutes) 1
Research/test users (institutes) 1
Pricing model One-time license fee
Based on Number of installations
Peer reviewed papers on performance
Non-peer reviewed papers on performance

  • Abstract: Artificial intelligence in echocardiography - Steps to automatic cardiac measurements in routine practice (read)

  • Abstract: Deep learning in segmentation and function evaluation of right ventricle in 2D echocardiography (read)

  • Abstract: Accurate prediction of left ventricular diastolic dysfunction in 2D echocardiography using ensemble of deep convolutional neural networks (read)

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