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FAQ topics

Aidence FAQ

How much does Veye Chest cost?

Our pricing model uses a volume-based fee per report (incl. prior scans and multiple users).

Where is Veye Chest in use?

Veye Chest is used in clinical practice and lung cancer screening in multiple hospitals in Europe and the UK, processing thousands of scans each month.

Does Veye Chest continue learning from my input once deployed?

No. Veye Chest only ‘learns’ on historic clinical data, which has been fully checked and labelled. Aidence will not implement continuous learning systems in the near future, and would never implement those without approval from a healthcare institution.

To improve the solution, we regularly release updates, often based on user feedback and requests.

Are the benefits of using Veye Chest supported by research?

The clinical performance of Veye Chest has been validated in a study performed by the University of Edinburgh and NHS Lothian.

We are gathering additional clinical evidence of the efficiency and quality gains of using Veye Chest, for example as part of a recently granted AI award. We are also in the process of completing an efficiency study with a hospital in the Netherlands.  

Veye Chest users have indicated that Veye Chest helps them review chest CT scans in a timely manner without compromising on accuracy.

How accurate is Veye Chest?

On default settings, Veye Chest detects nodules at a sensitivity of 90%.

How is Veye Chest trained and validated?

Veye Chest’s detection feature was trained with 45,000 chest CT scans originating from the American National Lung Screening Trial (NLST). 

The clinical performance of Veye Chest has been validated using two databases: the LIDC/IDRI, a database created to support the development and evaluation of CAD software, and a database developed in cooperation with the University of Edinburgh during a clinical trial funded by the NHS.  

Has Aidence obtained certification against ISO standards?

Yes, Aidence is ISO 27001 (regarding information security) and ISO 13485 (regarding quality management for medical devices) certified. The certificates are available upon request.

Which regulatory approvals does Veye Chest have?

Veye Chest is CE marked as a class IIa medical device under the Medical Device Directive. We have recently obtained certification for Veye Chest under the Medical Device Regulation. Under the new legislative framework, the device will be classified as a class IIb medical device.

Do you use our hospital’s patient data to improve Veye Chest?

No. Our data processing policy explains how we use and protect patient information. 

Are your AI applications GDPR compliant?

Yes. All our clinical applications are developed taking all relevant legislation into consideration. The solutions comply with the European Data Protection Legislation, and with requirements set out in the ISO 27001 regarding the secure handling of information. Further details on how Aidence processes patient data is clarified in the Data Processing Agreement signed with each customer.

Do I need specific systems to integrate Veye Chest?

Veye Chest is easy to set-up and can be integrated on-premise or on a hosted server. The Veye Chest hosted implementation demands no specific hardware and a minimal amount of work from your IT team.

Where do I start adopting AI in my hospital?

Start by reaching out – we’ve deployed AI in several hospitals over the past years and we can see you through! 

If you’re just looking for some tips, read this practical guide to AI adoption. 

Does the Veye Chest suite work with any PACS?

Yes. The Veye Chest suite works with any DICOM compliant PACS.

How do your AI solutions integrate with the radiology workflow?

Our solutions seamlessly integrate with the existing IT infrastructure. Veye Chest automatically queries your PACS for new eligible studies, including the most recent prior.

The analysis is sent back to the PACS, into the original study, using the DICOM standards. Results are available to anyone with PACS access, from any location and at any time. 

What type of artificial intelligence techniques are you applying?

We use supervised deep learning.

Deep learning is a subset of machine learning, which is a subset of artificial intelligence (AI). The term ‘deep’ comes from the use of multiple layers in the learning network. At its simplest, it is a way to automate predictive analytics. The learning is ‘supervised’ because the training dataset for the algorithm is fully labelled by humans and contains the ‘ground truth’.

Do any of your team members have a medical background?

Dr. Joris Wakkie is Aidence’s Chief Medical Officer. He worked as a doctor at the St. Antonius hospital, spending one year in cardiology and two years working in internal medicine. 

Dr. Lizzie Barclay is our Medical Director. She spent four years working as a doctor in NHS Trusts, including two years in clinical radiology. 

Leon Doorn, M.Sc., our Head of Regulatory Compliance, is a certified nurse, has a master’s in health sciences and more than 15 years of experience in the (QA/RA) medical device field. One of our engineers, Sander Smits, has completed his medical studies. 

Additionally, we are advised by a board of leading clinical experts, including Edwin J.R. van Beek, Director at Edinburgh Imaging facility QMRI, University of Edinburgh, SINAPSE Chair of Clinical Radiology; Arjun Nair, Consultant Radiologist, University College London Hospitals NHS Foundation Trust; and Alexander Scholtens, Radiologist at Tergooi.

What do you mean by ‘human sense’ in artificial intelligence?

We believe that understanding physicians’ needs and challenges is essential to building clinically valuable solutions. Thus, we work closely with healthcare professionals throughout the development of our AI solutions. 

We are a team driven by a humane purpose: contributing to better, more affordable healthcare for all.  

What’s behind the names ‘Aidence’ and ‘Veye’?

‘Aidence’ reflects our mission to aid healthcare with data science solutions. 

‘Veye’ is the extra part of virtual eyes in the search for subtle abnormalities on medical images.

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