Oral surgery, oral medicine, oral pathology and oral radiology · 2023
Development of a dental digital data set for research in artificial intelligence: the importance of labeling performed by radiologists.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Costa Eliana Dantas, Gaêta-Araujo Hugo, Carneiro José Andery, Zancan Breno Augusto Guerra, Baranauskas José Augusto, Macedo Alessandra Alaniz, Tirapelli Camila
Аннотация
OBJECTIVE: The aim of this study was to present the development of a database (dataset) of panoramic radiographs.
STUDY DESIGN: Three radiologists labeled an image set consisting of 936 panoramic radiographs. Labeling includes tooth numbering (including teeth present and missing) and annotation of dental conditions (e.g., caries, dental restoration, residual root, endodontic treatment, implant, fixed prosthesis, incisal wear). The annotation process was performed in a Picture Archive and Communication System software customized for the study purposes using a small bounding box to delimit the entire tooth and items for radiographic diagnosis and a large bounding box to simultaneously delimit the 2 dental arches (maxilla and mandible). A JSON file was generated for each annotation.
RESULTS: The database encompassed 23,619 annotations; disagreement between radiologists occurred in 0.7% of the notes.
CONCLUSIONS: This work aimed to inform researchers about the importance of the labeling process, in addition to providing the scientific community with a bank of labeled images to implement artificial intelligence systems in clinical practice.
STUDY DESIGN: Three radiologists labeled an image set consisting of 936 panoramic radiographs. Labeling includes tooth numbering (including teeth present and missing) and annotation of dental conditions (e.g., caries, dental restoration, residual root, endodontic treatment, implant, fixed prosthesis, incisal wear). The annotation process was performed in a Picture Archive and Communication System software customized for the study purposes using a small bounding box to delimit the entire tooth and items for radiographic diagnosis and a large bounding box to simultaneously delimit the 2 dental arches (maxilla and mandible). A JSON file was generated for each annotation.
RESULTS: The database encompassed 23,619 annotations; disagreement between radiologists occurred in 0.7% of the notes.
CONCLUSIONS: This work aimed to inform researchers about the importance of the labeling process, in addition to providing the scientific community with a bank of labeled images to implement artificial intelligence systems in clinical practice.