ArticlePloS one2026
A deep learning framework for the localization of landmarks on the lateral semi circular canals.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper introduces a Deep Learning (DL) framework to localize landmark coordinates within the semicircular canals in Computed Tomography (CT) scans of the temporal bone. These landmarks can be consistently defined across patients and imaging modalities and as such can serve as a means of forming a common coordinate system. We propose a DL based framework for automating the landmark selection process. We establish the accuracy of the methods using Bone Beam CT scans of the temporal bone of 20 patients and landmarks selected by 3 human experts as the ground truth. We show that the error rates are similar to the levels of variation in landmark selection achieved by human experts. We further validated the method on CT scans from 14 additional patients, demonstrating that the accuracy remains within clinically acceptable parameters.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.