Automatic Classification of Arteriovenous Vascular Access Aneurysms Using Artificial Intelligence and Smartphones

Figure 1 | Stages of AVF aneurysms and recommended actions (staging system used based on the British Renal Society’s scoring system)

Stages of AVF aneurysms and recommended actions

To classify AVF aneurysm stages, the following process is performed (Figure 2). First, images of a diverse range of AVF accesses are collected using devices (iPhone, iPad, etc.). The following guidelines assist in image capturing:

  • Take a picture of an AVF that includes the surrounding skin area.
  • Use the default resolution on the iPhone, Android phone, or tablet, as it is sufficient.
  • Use a white background.
  • Collect pictures in a diverse patient population (skin tone, age, etc.). Diversity is important for the CNN to improve its diagnostic acumen.

Figure 2 | The process for automatic AVF aneurysm classification

Stages of AVF aneurysms and recommended actions

Azura’s vascular access experts review these images and adjudicate AVF aneurysm stages. RRI uses 80 percent of the patients’ images for training purposes to optimize the CNN; the remaining 20 percent are used for CNN validation. Since images collected from different devices may have different resolutions, images are standardized before the next step. The CNN analyzes the images and, in split seconds, computes probabilities for each aneurysm stage (Figure 3).

Figure 3 | The mobile app solution 

Diagram of the mobile app solution

RRI has already collected and analyzed 15-to-20-second “panning” videos from 30 patients with aneurysms, 23 in stage 2, and seven in stage 3. The video frames that comprised the image set were extracted. Each image included three color channels with the image resolution set to 960 x 540. In this early phase, RRI collected video instead of pictures because each video frame helped to build a large training and validation image data set. Eighty percent of the patients’ videos were used for CNN training and the remainder for validation. Amazon Web Services’ SageMaker machine learning platform was used to build the CNN; it had a more than 90 percent classification accuracy using validation images.


RRI built an application solution to facilitate the image capturing and seamless image transfer to Amazon’s cloud in a safe, HIPPAcompliant manner. The app is built for Android and iOS tablets and smartphones.

The solution architecture has two process flows: model training and model integration (Figure 4).

Figure 4 | Architecture of the app solution

Architecture of the app solution: clinic to the Fresenius Medical Care secured ML/AI analytics data platform

Model training (process flow 1): Image collection via the mobile app is based on one image per patient-month. Vascular access specialists provide an aneurysm classification, which will be used for CNN training and validation. The CNN is trained to classify images on a scale of 0 (no aneurysm) to 3 (severe aneurysm).

Model execution (process flow 2): The CNN relays the probabilities for each of the four aneurysm stages almost instantaneously to the healthcare professional (see Figure 3).


In the future, additional clinical data elements (prolonged bleeding time, pain, etc.) could be collected in addition to the images by enabling a clinician feature in the mobile app interface.

This innovative solution is designed to minimize the burden on patients and the care team, further advance AI/ML approaches in Fresenius Medical Care, and provide precision and personalized care for dialysis patients. Furthermore, image analysis is a technology that can be leveraged beyond aneurysm classifications.

After the solution has been appropriately tested and cleared for use, the goal is to make it available to stakeholders in the United States and abroad. As identified by the Standardized Outcomes in Nephrology (SONG) initiative, the most important patient reported outcomes focus on quality of life, maintaining lifestyle, and self-management. RRI is confident that this new aneurysm classification app has the potential to be meaningful to patients, their families, and health professionals.

Meet The Experts


Peter Kotanko head shot

Research Director, Renal Research Institute

Zuwen Kuang head shot

Vice President, BI Analytics and Data Management, Fresenius Medical Care North America

Murat Sor head shot

Chief Medical Officer, Azura Vascular Care

Hanjie Zhang head shot

Supervisor, Biostatistics and Applied Artificial Intelligence/Machine Learning, Renal Research Institute


  1. Gill JR, Storck K, Kelly S. Fatal exsanguination from hemodialysis vascular access sites. Forensic Sci Med Pathol 2012 Sep;8(3):259-62. doi: 10.1007/s12024-011-9303-0.

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