ArticleBiomedical optics express2017
ELHnet: a convolutional neural network for classifying cochlear endolymphatic hydrops imaged with optical coherence tomography.
Article in Biomedical optics express, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed, 53 citations in OpenAlex.
- Optical coherence tomography imaging demonstrates endolymphatic hydrops in the lateral and posterior semicircular canals in noise-exposed mice.Hearing research · 2025Article
- Human inner ear fluid imbalance detected by optical coherence tomography correlates with hearing loss.Science translational medicine · 2025Article
- Scoping review of deep learning research illuminates artificial intelligence chasm in otolaryngology-head and neck surgery.NPJ digital medicine · 2025Article
- End-to-end deep learning classification of vocal pathology using stacked vowels.Laryngoscope investigative otolaryngology · 2023Article
- In Vivo Cochlear imaging provides a tool to study endolymphatic hydrops.Journal of vestibular research : equilibrium & orientation · 2021Article
- Deep learning architecture "LightOCT" for diagnostic decision support using optical coherence tomography images of biological samples.Biomedical optics express · 2020Article
- Phase unwrapping based on a residual en-decoder network for phase images in Fourier domain Doppler optical coherence tomography.Biomedical optics express · 2020Article
- The use of optical coherence tomography and convolutional neural networks to distinguish normal and abnormal oral mucosa.Journal of biophotonics · 2020Article
- Computer-aided diagnosis of external and middle ear conditions: A machine learning approach.PloS one · 2020Article
- Otorhinolaryngology and Artificial Intelligence.Turkish archives of otorhinolaryngology · 2019Article
- Automated classification of osteomeatal complex inflammation on computed tomography using convolutional neural networks.International forum of allergy & rhinology · 2019Article
- Evaluation of segmentation algorithms for optical coherence tomography images of ovarian tissue.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Deep longitudinal transfer learning-based automatic segmentation of photoreceptor ellipsoid zone defects on optical coherence tomography images of macular telangiectasia type 2.Biomedical optics express · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detection of endolymphatic hydrops is important for diagnosing Meniere's disease, and can be performed non-invasively using optical coherence tomography (OCT) in animal models as well as potentially in the clinic. Here, we developed ELHnet, a convolutional neural network to classify endolymphatic hydrops in a mouse model using learned features from OCT images of mice cochleae. We trained ELHnet on 2159 training and validation images from 17 mice, using only the image pixels and observer-determined labels of endolymphatic hydrops as the inputs. We tested ELHnet on 37 images from 37 mice that were previously not used, and found that the neural network correctly classified 34 of the 37 mice. This demonstrates an improvement in performance from previous work on computer-aided classification of endolymphatic hydrops. To the best of our knowledge, this is the first deep CNN designed for endolymphatic hydrops classification.
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.