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A publicly available newborn ear shape dataset for medical diagnosis of auricular deformities.
Ren, Liu-Jie; Luo, Fei; Yang, Zhi-Wei; Chen, Li-Li; Wang, Xin-Yue; Li, Chen-Long; Xie, You-Zhou; Wang, Ji-Mei; Zhang, Tian-Yu; Wang, Shuo; Fu, Yao-Yao.
Afiliación
  • Ren LJ; FPRS Department/ENT Institute, Eye and ENT Hospital, Fudan University, Shanghai, China.
  • Luo F; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China.
  • Yang ZW; Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China.
  • Chen LL; Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China.
  • Wang XY; Academy for Engineering & Technology, Fudan University, Shanghai, China.
  • Li CL; FPRS Department/ENT Institute, Eye and ENT Hospital, Fudan University, Shanghai, China.
  • Xie YZ; FPRS Department/ENT Institute, Eye and ENT Hospital, Fudan University, Shanghai, China.
  • Wang JM; FPRS Department/ENT Institute, Eye and ENT Hospital, Fudan University, Shanghai, China.
  • Zhang TY; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China.
  • Wang S; FPRS Department/ENT Institute, Eye and ENT Hospital, Fudan University, Shanghai, China.
  • Fu YY; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China.
Sci Data ; 11(1): 13, 2024 Jan 02.
Article en En | MEDLINE | ID: mdl-38167545
ABSTRACT
Early and accurate diagnosis of ear deformities in newborns is crucial for an effective non-surgical correction treatment, since this commonly seen ear anomalies would affect aesthetics and cause mental problems if untreated. It is not easy even for experienced physicians to diagnose the auricular deformities of newborns and the classification of the sub-types, because of the rich bio-metric features embedded in the ear shape. Machine learning has already been introduced to analyze the auricular shape. However, there is little publicly available datasets of ear images from newborns. We released a dataset that contains quality-controlled photos of 3,852 ears from 1,926 newborns. The dataset also contains medical diagnosis of the ear shape, and the health data of each newborn and its mother. Our aim is to provide a freely accessible dataset, which would facilitate researches related with ear anatomies, such as the AI-aided detection and classification of auricular deformities and medical risk analysis.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Oído Externo / Aprendizaje Automático Tipo de estudio: Diagnostic_studies / Etiology_studies / Risk_factors_studies Límite: Humans / Newborn Idioma: En Revista: Sci Data Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
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XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Oído Externo / Aprendizaje Automático Tipo de estudio: Diagnostic_studies / Etiology_studies / Risk_factors_studies Límite: Humans / Newborn Idioma: En Revista: Sci Data Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido