An Interpretable Deep Learning System for Fine-Grained Classification and Longitudinal Tracking of Neonatal Auricular Deformities.

Yihui Feng, Xujun Hu, Xiwen Zhang, Xiaobao Ma, Jialin Xie, Jianyong Chen, Yangyang Yuan

Abstract read

In one paragraph

Article in Biology, 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

–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

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

7 authors.

Yihui FengSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China.

Xujun HuSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China.

Xiwen ZhangSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China.

Xiaobao MaDepartment of Otorhinolaryngology-Head and Neck Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200092, China.ORCID 0000-0003-2873-3620

Jialin XieThe Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou 310053, China.ORCID 0009-0002-6749-9566

Jianyong ChenDepartment of Otorhinolaryngology-Head and Neck Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200092, China.

Yangyang YuanSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China.

Funding

Zhejiang Chinese Medical University 781100E035Zhejiang Provincial Natural Science Foundation 782213D00807

6 · The paper itself

Abstract

Early non-invasive correction of neonatal auricular deformities is highly dependent on timely and precise diagnosis. However, clinical practice is often compromised by the subjectivity of visual assessments and the lack of objective tracking metrics, which frequently leads to missed optimal treatment windows. To address these challenges, we developed an interpretable deep learning-based diagnostic system for the automated screening and fine-grained classification of these deformities. Methodologically, a large-scale, multi-source dataset (

Indexed as

deep learningdisease severity scoringfine-grained classificationinterpretable AIneonatal auricular deformitiessupervised contrastive learning

Identifiers

PMID42450533

DOI10.3390/biology15130985

PMCPMC13360241

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

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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.