Evidence map›Paper›PMID 40847589›Full record

ArticleInternational forum of allergy & rhinology2026

Vision-Guided Surgical Navigation Using Computer Vision for Dynamic Intraoperative Imaging Updates.

Jeremy Ruthberg, Nicole Gunderson, Pengcheng Chen, Graham Harris, Hannah Case, Randall Bly, Eric J Seibel, Waleed M Abuzeid

Abstract read
In one paragraph

Article in International forum of allergy & rhinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jeremy RuthbergDepartment of Otolaryngology-Head and Neck Surgery, University of Washington, Seattle, Washington, USA.
Nicole GundersonDepartment of Mechanical Engineering, University of Washington, Seattle, Washington, USA.
Pengcheng ChenDepartment of Mechanical Engineering, University of Washington, Seattle, Washington, USA.
Graham HarrisDepartment of Otolaryngology-Head and Neck Surgery, University of Washington, Seattle, Washington, USA.
Hannah CaseDepartment of Otolaryngology-Head and Neck Surgery, University of Washington, Seattle, Washington, USA.ORCID https://orcid.org/0000-0003-4097-9500
Randall BlyDepartment of Otolaryngology-Head and Neck Surgery, University of Washington, Seattle, Washington, USA.
Eric J SeibelDepartment of Mechanical Engineering, University of Washington, Seattle, Washington, USA.
Waleed M AbuzeidDepartment of Otolaryngology-Head and Neck Surgery, University of Washington, Seattle, Washington, USA.ORCID https://orcid.org/0000-0003-2489-0514

Funding

Research Training in OtolaryngologyT32DC000018 · NIDCD · UNIVERSITY OF WASHINGTON · PI STONE, JENNIFER S. · 1989 to 2023
$7.8M
Endoscope development for the clinical use of near infrared fluorescence molecular probes in the GI tractR44CA261318 · NCI · VERAVANTI, INC. · PI MCMORROW, GERALD · 2021 to 2022
$2.0M
Clinician-Scientist Research Education and Training in OtolaryngologyR25DC021791 · NIDCD · UNIVERSITY OF WASHINGTON · PI Jay T. Rubinstein, Jennifer S. Stone · 2024 to 2026
$1.6M
NCI NIH HHS R44 CA261318NIDCD NIH HHS R25 DC021791NIDCD NIH HHS T32 DC000018NIH HHS R44CA261318NIH R25 training grant mechanism 1R25DC021791-01
6 · The paper itself

Abstract

backgroundResidual disease after endoscopic sinus surgery (ESS) contributes to poor outcomes and revision surgery. Image-guided surgery systems cannot dynamically reflect intraoperative changes. We propose a sensorless, video-based method for intraoperative CT updating using neural radiance fields (NeRF), a deep learning algorithm used to create 3D surgical field reconstructions.

methodsBilateral ESS was performed on three 3D-printed models (n = 6 sides). Postoperative endoscopic videos were processed through a custom NeRF pipeline to generate 3D reconstructions, which were co-registered to preoperative CT scans. Digitally updated CT models were created through algorithmic subtraction of resected regions, then volumetrically segmented, and compared to ground-truth postoperative CT. Accuracy was assessed using Hausdorff distance (surface alignment), Dice similarity coefficient (DSC) (volumetric overlap), and Bland‒Altman analysis (BAA) (statistical agreement).

resultsComparison of the updated CT and the ground-truth postoperative CT indicated an average Hausdorff distance of 0.27 ± 0.076 mm and a 95th percentile Hausdorff distance of 0.82 ± 0.165 mm, indicating sub-millimeter surface alignment. The DSC was 0.93 ± 0.012 with values >0.9 suggestive of excellent spatial overlap. BAA indicated modest underestimation of volume on the updated CT versus ground-truth CT with a mean difference in volumes of 0.40 cm

conclusionsComputer vision can enable dynamic intraoperative imaging by generating highly accurate CT updates from monocular endoscopic video without external tracking. By directly visualizing resection progress, this software-driven tool has the potential to enhance surgical completeness in ESS for next-generation navigation platforms.

Indexed as

EndoscopyParanasal SinusesSurgery, Computer-AssistedAlgorithmsDeep LearningHumansImaging, Three-DimensionalPrinting, Three-DimensionalTomography, X-Ray Computed3D reconstructionartificial intelligencechronic sinusitiscomputer‐assisted surgerydeep learningimage‐guided surgery

Identifiers

PMID40847589
PMCPMC12616888

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.