Date of Award
Master of Science (MS)
Dr. Joseph Singapogu
Dr. Jeremy Mercuri
Dr. Judy Geissler
End-stage kidney disease (EKSD) is the final, permanent stage of chronic kidney disease (CKD), where the body's kidneys cannot filter blood sufficiently. Hemodialysis treats ESKD by filtering wastes and water from your blood externally via a dialyzer. Patients on hemodialysis require a vascular access, typically an arteriovenous fistula (AVF) or arteriovenous graft (AVG) for dialysis, which is used three times a week for about four hours a session. These life-saving therapies can be quite tedious, especially for elderly patients and patients with co-morbidities. As such, successful cannulation is extremely important to ensure the longevity of the patient's vascular access and minimize the risk of complications during hemodialysis. Cannulation is a skill that requires comprehensive training and regular competency assessment. Poor clinical outcomes due to infiltration and other cannulation-related complications are often due to lack of cannulation skill training. Ultrasound-guided cannulation has emerged as a promising cannulation technique to improve the accuracy of first-time cannulations and minimizes complications by improving cannulation outcomes. While ultrasound-guided cannulation offers significant advantages over traditional cannulation, mastering this technique requires skill and can be demanding, time-consuming, and requires thorough training and practice with proper objective feedback. Given the significant challenges associated with dialysis cannulation and the lack of realistic training modalities that give objective feedback while allowing ultrasound guidance, the development of an ultrasound-guided cannulation skills training simulator becomes critical. The first aim was to develop an Infrared (IR) emitter-detector system to estimate the location of the needle tip inside the simulated AVF models. The system enables real-time tracking of the location of the needle inside the AVF models using an IR detector fabricated inside the 15 gauge (G) dialysis needle and four IR emitters actuating at different frequencies fabricated inside the AVF. A validation experiment was conducted, proving that this new system can detect the needle tip's location in near real-time with only a 4mm error out of the total 90mm length of the simulated AVF. The second aim was to develop an ultrasound-guided cannulation skills training simulator with realistic patient-specific echogenic AVF models and objective feedback. Four patient-specific AVF geometries with varying levels of complexity were designed from actual patient fistula scans. Using these fistula geometries, a novel method was developed to create sensorized echogenic AVF phantoms for the ultrasound-guided cannulation skills training simulator. Reliable sensor data, along with ultrasound imaging, were recorded during the cannulations performed on these AVF phantoms. Metrics were successfully calculated on all patient-specific echogenic AVF phantoms. In the future, the needle insertion process can be segmented from the ultrasound imaging data to validate our metrics and develop new ultrasound-related metrics to help with ultrasound-guided cannulation skills training and assessment. In conclusion, the ultrasound-guided cannulation skills training simulator could be a valuable tool for the training and competency assessment of healthcare professionals in the cannulation of AVF’s, thus helping to improve overall patient outcomes.
Shukla, Devansh, "Medical Simulation-Based Sensor Methods for Ultrasound-Guided Dialysis Cannulation Skill Assessment" (2023). All Theses. 4205.
Author ORCID Identifier
Available for download on Tuesday, December 31, 2024