Evidence map›Paper›PMID 42054756›Full record

ArticleBrazilian journal of otorhinolaryngology

Deep learning for discriminating cochlear malformations on temporal bone CT.

Zhenhua Li, Langtao Zhou, Xiang Bin, Bin Liu, Anzhou Tang, Songhua Tan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Brazilian journal of otorhinolaryngology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

6 authors.

Zhenhua LiThe First Affiliated Hospital of Hunan Normal University, Hunan Provincial People's Hospital, Department of Otorhinolaryngology-Head and Neck Surgery, Changsha, Hunan, China.
Langtao ZhouBeijing Institute of Technology, The School of Optics and Photonics, Beijing, China.
Xiang BinThe First Affiliated Hospital of Guangxi Medical University, Department of Otorhinolaryngology Head and Neck Surgery, Nanning, Guangxi, China.
Bin LiuThe First Affiliated Hospital of Hunan Normal University, Hunan Provincial People's Hospital, Department of Otorhinolaryngology-Head and Neck Surgery, Changsha, Hunan, China.
Anzhou TangThe First Affiliated Hospital of Guangxi Medical University, Department of Otorhinolaryngology Head and Neck Surgery, Nanning, Guangxi, China.
Songhua TanThe First Affiliated Hospital of Guangxi Medical University, Department of Otorhinolaryngology Head and Neck Surgery, Nanning, Guangxi, China. Electronic address: songhuatan@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesDiagnosis of cochlear malformation on temporal bone CT images is often difficult because the imaging findings are frequently subtle. Our aim was to assess the utility of deep learning analysis in diagnosing cochlear malformation on temporal bone CT images.

methodsA total of 373 temporal bone CTs (187 with normal and 186 with malformed cochlea) were included in this study. The network Swin UNETR was used for cochlea automatic segmentation, then we divided it into left and right sides. three classification networks, ResNet50, EfficientNet-B0 and DenseNet121, were used for training to diagnose cochlear malformation (590 sides). The test set (149 sides) was analyzed using deep learning models and by otologists.

resultsThe average Dice coefficient of Swin UNETR was 0.93. The AUCs for ResNet50, EfficientNet-B0 and DenseNet121 were 0.93, 0.89 and 0.93, respectively, with DenseNet121 achieving the highest AUC among the four models. ResNet50 and DenseNet121 are better than otologists (1‒5 y).

conclusionThe deep learning technique may be a useful supportive tool for diagnosing cochlear malformation on temporal bone CT images.

Indexed as

CochleaDeep LearningTemporal BoneTomography, X-Ray ComputedConvolutional Neural NetworksHumansCochlear implantCochlear malformationDeep learningTemporal bone

Identifiers

PMID42054756
PMCPMC13140023

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