Evidence map›Paper›PMID 42212468›Full record

ArticleThe Laryngoscope2026

Synchrotron-Based Deep Learning Network of the Inner Ear: Development and Expert Validation.

Ashley Micuda, Kyle Rioux, Luke Helpard, Munir Demir Bajin, Zahra Kassam, Sachin Pandey, Jaehyun Hwang, Griselda T Romero-Sanchez, Manas Sharma, Lorne S Parnes and 2 more

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in The Laryngoscope, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Ashley MicudaDepartment of Medical Biophysics, Western University, London, Ontario, Canada.ORCID https://orcid.org/0000-0002-6096-6654
Kyle RiouxDepartment of Electrical and Computer Engineering, Western University, London, Ontario, Canada.
Luke HelpardDepartment of Otolaryngology-Head and Neck Surgery, Western University, London, Ontario, Canada.
Munir Demir BajinDepartment of Otolaryngology-Head and Neck Surgery, Western University, London, Ontario, Canada.ORCID https://orcid.org/0000-0003-1088-4367
Zahra KassamDepartment of Medical Imaging, Western University, London, Ontario, Canada.
Sachin PandeyDepartment of Medical Imaging, Western University, London, Ontario, Canada.
Jaehyun HwangDepartment of Medical Biophysics, Western University, London, Ontario, Canada.
Griselda T Romero-SanchezDepartment of Medical Imaging, Western University, London, Ontario, Canada.
Manas SharmaDepartment of Medical Imaging, Western University, London, Ontario, Canada.
Lorne S ParnesDepartment of Otolaryngology-Head and Neck Surgery, Western University, London, Ontario, Canada.
Hanif M LadakDepartment of Medical Biophysics, Western University, London, Ontario, Canada.
Sumit K AgrawalDepartment of Medical Biophysics, Western University, London, Ontario, Canada.

Funding

Natural Sciences and Engineering Research Council of Canada RGPIN-2022-04184
6 · The paper itself

Abstract

objectiveTo develop and externally validate a deep learning segmentation network capable of automatically segmenting the inner ear in preoperative clinical computed tomography (CT) scans across various resolutions and protocols.

methodsA deep learning-based segmentation network was developed using 100 cadaveric specimens that were scanned with synchrotron-radiation phase contrast imaging (SR-PCI) and various clinical CT scanners. Different acquisitions, protocols, and augmentations were used to create a total of 4,784 paired SR-PCI and clinical three-dimensional datasets used for deep learning training and model development. Performance and accuracy of the network were assessed on a separate unseen dataset and externally validated against manual segmentations from seven individual domain experts (otologists and radiologists), the mean expert performance, and a simultaneous truth and performance level (STAPLE) consensus segmentation.

resultsThe network pipeline significantly outperformed each individual expert segmentation, the average of the expert segmentations, and the STAPLE consensus segmentation. Compared to the SR-PCI ground truth data, the network achieved a Dice similarity coefficient of 0.922, a maximum absolute Hausdorff distance of 0.329 mm, and an average Hausdorff distance of 0.006 mm on cone-beam CT and helical CT with resolutions as low as 625 μm.

conclusionThis is the first automated segmentation algorithm for the inner ear that has been shown to outperform segmentations from domain experts, establishing a new clinical gold standard. LEVEL OF EVIDENCE: N/A.

Indexed as

Deep LearningEar, InnerSynchrotronsTomography, X-Ray ComputedCadaverHumansImaging, Three-Dimensionaldeep learninginner earsegmentationSTAPLEsynchrotron imaging

Identifiers

PMID42212468
PMCPMC13569625

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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.