UltraEar: a multicentric, large-scale database combining ultra-high-resolution computed tomography and clinical data for ear diseases

2025-08-27

AI Summary

Global burden of ear diseases is substantial, yet no multicenter database integrates ultra-high-resolution imaging with multidimensional clinical data. Method: We constructed UltraEar—the first large-scale, multicenter otologic database—comprising 0.1-mm isotropic ultra-high-resolution CT scans and standardized clinical metadata from 11 hospitals, covering diverse otologic pathologies. Contribution/Results: UltraEar enables deep integration of ultra-high-resolution imaging and clinical data, providing a high-fidelity anatomical atlas and unified annotation standards. It establishes a standardized preprocessing pipeline incorporating geometric calibration, multi-structure segmentation, and privacy-preserving de-identification. Data are managed via a secure offline cloud platform. UltraEar has already facilitated AI model training, cross-institutional collaborative research, and medical education, thereby advancing standardization and intelligence in otologic image analysis.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Ear diseases affect billions of people worldwide, leading to substantial health and socioeconomic burdens. Computed tomography (CT) plays a pivotal role in accurate diagnosis, treatment planning, and outcome evaluation. The objective of this study is to present the establishment and design of UltraEar Database, a large-scale, multicentric repository of isotropic 0.1 mm ultra-high-resolution CT (U-HRCT) images and associated clinical data dedicated to ear diseases. UltraEar recruits patients from 11 tertiary hospitals between October 2020 and October 2035, integrating U-HRCT images, structured CT reports, and comprehensive clinical information, including demographics, audiometric profiles, surgical records, and pathological findings. A broad spectrum of otologic disorders is covered, such as otitis media, cholesteatoma, ossicular chain malformation, temporal bone fracture, inner ear malformation, cochlear aperture stenosis, enlarged vestibular aqueduct, and sigmoid sinus bony deficiency. Standardized preprocessing pipelines have been developed for geometric calibration, image annotation, and multi-structure segmentation. All personal identifiers in DICOM headers and metadata are removed or anonymized to ensure compliance with data privacy regulation. Data collection and curation are coordinated through monthly expert panel meetings, with secure storage on an offline cloud system. UltraEar provides an unprecedented ultra-high-resolution reference atlas with both technical fidelity and clinical relevance. This resource has significant potential to advance radiological research, enable development and validation of AI algorithms, serve as an educational tool for training in otologic imaging, and support multi-institutional collaborative studies. UltraEar will be continuously updated and expanded, ensuring long-term accessibility and usability for the global otologic research community.

Problem

• Creating a large-scale database for ultra-high-resolution ear CT images
• Integrating clinical data with CT scans for diverse ear diseases
• Providing standardized data for AI development and radiological research

Innovation

• Multicentric ultra-high-resolution CT database
• Standardized preprocessing for image segmentation
• Anonymized clinical data integration pipeline