EVIDENCE BASED INSIGHT
Automatic Classification of Arteriovenous Vascular Access Aneurysms Using Artificial Intelligence and Smartphones
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.
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).
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.
IMPLEMENT AI/ML MODEL FOR NONINVASIVE PERSONALIZED PATIENT CARE
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).
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
- 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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