# Next-generation image-guided endoscopic sinus surgery using novel computer vision techniques

> **NIH EB R01** · UNIVERSITY OF WASHINGTON · 2026 · $413,944

## Abstract

Chronic rhinosinusitis (CRS) is a persistent inflammatory disease affecting 1 in 8 adults in the US. CRS
profoundly affects health-related quality-of-life and is commonly treated with endoscopic sinus surgery (ESS).
ESS fails to induce durable symptom improvements in 25% of CRS patients and subsequent revision ESS is
needed in 15-46% of cases due to persistent or recurrent symptoms after incomplete surgical dissection.
Revision ESS has significantly lower success rates than primary ESS and independently predicts poorer clinical
outcomes. Complete surgical dissection, ideally during the first ESS, is less costly and is most likely to improve
symptoms. Therefore, tools that enable complete ESS are critical. Image-guided surgery (IGS) can facilitate
surgical dissection in ESS by mapping the location of surgical instruments to preoperative computed tomography
(CT) images. However, IGS systems have significant limitations: 1) high costs, driving lower IGS usage,
especially in underserved groups; 2) loss of tracking accuracy during ESS; and 3) disparity between the static
CT images and reality as ESS progresses. We aim to establish a computer vision-based navigation system that
will: 1) greatly reduce the cost of surgical navigation by eliminating expensive IGS hardware, thereby
democratizing the use of navigation; 2) achieve consistent submillimeter accuracy during ESS; and 3) enhance
surgical completeness to drive better clinical outcomes in CRS patients. The objective of this proposal is to
develop a low-cost vision-based navigation system that reflects dynamic changes in surgical anatomy on the CT,
maps critical anatomy from the CT onto a 3D reconstruction of the surgical field, and continuously tracks surgical
instruments that are in view, aggregating these data in real-time for visualization. The central hypothesis is that
vision-based navigation will maintain accuracy during ESS and will display critical anatomic structures and up-
to-date anatomic changes to the s

## Key facts

- **NIH application ID:** 11339957
- **Project number:** 1R01EB039035-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Waleed M Abuzeid
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** EB
- **Fiscal year:** 2026
- **Award amount:** $413,944
- **Award type:** 1
- **Project period:** 2026-05-08T00:00:00 → 2030-02-28T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11339957

## Citation

> US National Institutes of Health, RePORTER application 11339957, Next-generation image-guided endoscopic sinus surgery using novel computer vision techniques (1R01EB039035-01). Retrieved via AI Analytics 2026-05-20 from https://api.ai-analytics.org/grant/nih/11339957. Licensed CC0.

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