The Journal of prosthetic dentistry · 2023
Artificial intelligence in the detection and classification of dental caries.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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Ahmed Walaa Magdy, Azhari Amr Ahmed, Fawaz Khaled Ahmed, Ahmed Hani M, Alsadah Zainab M, Majumdar Aritra, Carvalho Ricardo Marins
Аннотация
STATEMENT OF PROBLEM: Automated detection of dental caries could enhance early detection, save clinician time, and enrich treatment decisions. However, a reliable system is lacking.
PURPOSE: The purpose of this study was to train a deep learning model and to assess its ability to detect and classify dental caries.
MATERIAL AND METHODS: Bitewings radiographs with a 1876×1402-pixel resolution were collected, segmented, and anonymized with a radiographic image analysis software program and were identified and classified according to the modified King Abdulaziz University (KAU) classification for dental caries. The method was based on supervised learning algorithms trained on semantic segmentation tasks.
RESULTS: The mean score for the intersection-over-union of the model was 0.55 for proximal carious lesions on a 5-category segmentation assignment and a mean F1 score of 0.535 using 554 training samples.
CONCLUSIONS: The study validated the high potential for developing an accurate caries detection model that will expedite caries identification, assess clinician decision-making, and improve the quality of patient care.
PURPOSE: The purpose of this study was to train a deep learning model and to assess its ability to detect and classify dental caries.
MATERIAL AND METHODS: Bitewings radiographs with a 1876×1402-pixel resolution were collected, segmented, and anonymized with a radiographic image analysis software program and were identified and classified according to the modified King Abdulaziz University (KAU) classification for dental caries. The method was based on supervised learning algorithms trained on semantic segmentation tasks.
RESULTS: The mean score for the intersection-over-union of the model was 0.55 for proximal carious lesions on a 5-category segmentation assignment and a mean F1 score of 0.535 using 554 training samples.
CONCLUSIONS: The study validated the high potential for developing an accurate caries detection model that will expedite caries identification, assess clinician decision-making, and improve the quality of patient care.