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Whole Neonatal Cochlear Explants as an In vitro Model

Whole Neonatal Cochlear Explants as an In vitro Model

Published on: July 28, 2023

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

Yihui Feng¹, Xujun Hu¹, Xiwen Zhang¹

¹School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China.

|July 15, 2026

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This study introduces an AI diagnostic system for early, objective screening of neonatal ear deformities, improving treatment timing and precision management for better patient outcomes.

Area of Science:

Medical Imaging
Artificial Intelligence in Healthcare
Neonatal Medicine

Background:

Accurate diagnosis of neonatal auricular deformities is crucial for timely non-invasive correction.
Clinical assessment relies on subjective visual evaluation, leading to missed treatment windows.
Objective metrics for tracking deformity progression and treatment efficacy are lacking.

Purpose of the Study:

To develop an interpretable deep learning system for automated screening and classification of neonatal ear deformities.
To enable objective, quantitative tracking of treatment response.
To provide a standardized tool for early intervention and precision management.

Main Methods:

Curated a large-scale dataset (n=4644) for training.

Keywords:

deep learning disease severity scoring fine-grained classification interpretable AI neonatal auricular deformities supervised contrastive learning

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Employed YOLOv11 for object detection and ConvNeXt-Tiny for classification.

Integrated supervised contrastive learning for a continuous severity score and Grad-CAM for interpretability.

Main Results:

Achieved 88.2% accuracy in binary screening (AUC: 0.949) and 87.4% accuracy in multi-class subtyping (macro-AUC: 0.976).
Demonstrated robust generalization across three independent cohorts.
Severity scores effectively quantified post-intervention improvements (p=0.0004).

Conclusions:

The developed AI system offers an objective and standardized approach to neonatal ear deformity diagnosis.
It facilitates early intervention and precision management by enabling quantitative tracking of therapeutic efficacy.
Further clinical calibration of the severity score is needed for rare subtypes and broader application.