# Non-invasive Detection of Cerebral Aneurysm Recurrence after Endovascular Treatment Using Automated Image Processing

> **NIH NIH R41** · MEDICAL INNOVATORS COMPANY, LLC · 2020 · $224,981

## Abstract

PROJECT SUMMARY
Hemorrhage due to cerebral aneurysm rupture is a devastating condition with high mortality. For the more than
30,000 patients in the US who are diagnosed annually with an aneurysm, treatment consists of preventing
rupture, and increasingly relies of endovascular techniques. However, treatment durability is unknown with
recurrence estimated at 16-40% and the re-treatment of 10-20%. The current gold standard to ensure aneurysm
obliteration is catheter-based digital subtraction angiography (DSA), an invasive method with significant side
effects. Here, we propose an alternative that uses simple skull x-rays and automated image processing to identify
patients who are high likelihood of recurrence and select them for further investigation. Development of this
technique is the result of a collaboration between the Medical Innovations Company (MIC) and the UTHealth
and Memorial Hermann Hospital (UTH/MHH). We plan to test the hypothesis that aneurysm recurrence can
be detected using standard skull x-rays. In Aim 1, we will develop an automated computer algorithm that
detects aneurysm recurrence after coiling. Data from an established cohort of patients treated at UTH/MHH.
Automated computer analysis of the x-rays (at initial treatment and 6-month follow) will predict aneurysm
recurrence using coil morphometry (size, shape, orientation). The algorithm will be trained by comparing it to the
gold standard for follow up (DSA). In Aim 2, preliminary validation of algorithm performance will be tested in a
novel dataset. A validation dataset (n=150) of similar patients treated with the same protocol as the training
dataset will be processed using the automated algorithm. The performance of the algorithm will be assessed
using receiver operator characteristics to determine optimal sensitivity/specificity. If successful, such an
approach could stratify risk in patients and determine which should undergo angiography. Reducing utilization
of angiography will significantly reduce complications and medical cost at an immense benefit to the public. This
Phase I STTR grant will allow for algorithm development and testing prior to a Phase II application and broader
clinical trials. The partnership between MIC and UTH/MHH combines experience commercializing medical
software with clinical neurosurgery.
CONFIDENTIAL- UTHEALTH

## Key facts

- **NIH application ID:** 9907673
- **Project number:** 1R41NS115253-01
- **Recipient organization:** MEDICAL INNOVATORS COMPANY, LLC
- **Principal Investigator:** Peng Roc Chen
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $224,981
- **Award type:** 1
- **Project period:** 2020-01-01 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9907673

## Citation

> US National Institutes of Health, RePORTER application 9907673, Non-invasive Detection of Cerebral Aneurysm Recurrence after Endovascular Treatment Using Automated Image Processing (1R41NS115253-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9907673. Licensed CC0.

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