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FAQ topics

Aidence FAQ

How much does Veye Lung Nodules cost?

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

Where is Veye Lung Nodules in use?

Veye Lung Nodules 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 Lung Nodules continue learning from my input once deployed?

No. Veye Lung Nodules only “learns” from historical clinical data, which has been fully checked and labelled. We 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 Lung Nodules supported by research?

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

Results from clinical research with two radiologists show an average ~ 40% reduction in reading time when reporting on pulmonary nodules with Veye. The findings have been published in the European Journal of Radiology Open.

We are gathering additional clinical evidence of the effect of using Veye Lung Nodules on radiologists’ decision-making, as part of the INPACT project.

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

How accurate is Veye Lung Nodules?

On default settings, Veye Lung Nodules detects nodules at a sensitivity of 91% at the cost of one false positive on average per scan.

How is Veye Lung Nodules trained and validated?

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

The clinical performance of Veye Lung Nodules 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.  

To access the clinical validation study, visit this page.

Has Aidence obtained certification against ISO standards?

Yes: ISO 27001 (regarding information security) and MDSAP (which extends the requirements of ISO 13485 regarding quality management for medical devices). The certificates are available upon request.

Which regulatory approvals does Veye Lung Nodules have?

Veye Lung Nodules is CE marked as a class IIb medical device under the Medical Device Regulation.

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

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 we process patient data are clarified in the Data Processing Agreement signed with each customer.

Do I need specific systems to integrate Veye Lung Nodules?

Veye Lung Nodules is easy to set-up and can be integrated on a hosted server. The Veye Lung Nodules 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 Veye Lung Nodules work with any PACS?

Yes, Veye Lung Nodules 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 Engine 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?

Yes, several.

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. 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?

As a team, we are driven by a humane purpose: giving lung cancer patients a fighting chance.

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.

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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