# Towards a Convenient, Point-of-Care Device for Screening and Surveillance of Aortic Aneurysms

> **NIH NIH R21** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2021 · $410,344

## Abstract

PROJECT SUMMARY/ABSTRACT
An aortic aneurysm carries increasing risk of rupture with growing aneurysm diameter. This condition
is typically asymptomatic, so screening and surveillance are essential. Ultrasound and other imaging
methods are employed for such monitoring at high accuracy. However, these methods require an
expert-operator and are expensive, and aortic aneurysms are considerably underdetected at present
and may become even more underdetected in the future as the disease prevalence increases with
societal aging. The broad goal is to establish a point-of-care device that is convenient in use and cost
(e.g., in a smartphone form factor) for aortic aneurysm screening and surveillance. The underlying
hypothesis is that arterial waveforms, which can be obtained with such a device, constitute a non-
imaging solution for indicating aneurysm size. This new hypothesis will be efficiently and effectively
tested by leveraging a wealth of existing patient data. These data include high-fidelity, non-invasive
waveforms from four arterial sites and reference imaging measurements of aneurysm size from at
least 217 patients and 217 matchable controls. The specific aims are to develop and validate (1)
physics-based methods and (2) machine learning methods for predicting aneurysm size from arterial
waveforms. The physics-based methods will fit tube-load models of arterial wave transmission and
reflection to the waveforms to estimate parameters indicative of aneurysm size. The machine
learning methods will employ linear regression or a neural network and take inputs comprising
waveform features such as pulse wave velocity, which decreases with increasing aneurysm diameter
as predicted by the well-known Moens-Korteweg equation, and number of local waveform maxima,
which may increase with enhanced negative wave reflection induced by aneurysm growth, as well as
confounding factors such as blood pressure and age, which also alter pulse wave velocity, to predict
aneurysm diameter. These methods will be optimized using a portion of the patient data for
application to various combinations of the arterial waveforms (e.g., proximal and distal waveforms or
single waveforms). The developed methods will then be tested using the remaining data, while
blinded to the reference aneurysm measurements, in terms of their ability to track aneurysm diameter
and classify patients versus controls and patients before versus after endovascular repair. The final
method will be chosen as the one that best balances accuracy of aneurysm monitoring with
convenience of arterial waveform measurements. Successful completion of this project will guide the
design and justify the development of a point-of-care device that may ultimately help mitigate aortic
aneurysm mortality.

## Key facts

- **NIH application ID:** 10125246
- **Project number:** 1R21EB029376-01A1
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** RAMAKRISHNA MUKKAMALA
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $410,344
- **Award type:** 1
- **Project period:** 2021-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10125246

## Citation

> US National Institutes of Health, RePORTER application 10125246, Towards a Convenient, Point-of-Care Device for Screening and Surveillance of Aortic Aneurysms (1R21EB029376-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10125246. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*