A deep learning model based on concatenation approach to predict the time to extract a mandibular third molar tooth

BMC Oral Health

Research

Jaemyung Ahn¹  Dohyun Kwon¹  Dong ohk Kang¹  Jun-Young Paeng¹  Chang-Soo Kim¹ 

[1] Department of Oral and Maxillofacial Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Irwon-Dong, Gangnam-Gu, Seoul, Republic of Korea;

关键词: Mandibular third molar;    Extraction time;    Predictive model;    Concatenation approach;    Artificial intelligence;   

DOI  :  10.1186/s12903-022-02614-3

 received in 2022-08-17, accepted in 2022-11-23,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundAssessing the time required for tooth extraction is the most important factor to consider before surgeries. The purpose of this study was to create a practical predictive model for assessing the time to extract the mandibular third molar tooth using deep learning. The accuracy of the model was evaluated by comparing the extraction time predicted by deep learning with the actual time required for extraction.MethodsA total of 724 panoramic X-ray images and clinical data were used for artificial intelligence (AI) prediction of extraction time. Clinical data such as age, sex, maximum mouth opening, body weight, height, the time from the start of incision to the start of suture, and surgeon’s experience were recorded. Data augmentation and weight balancing were used to improve learning abilities of AI models. Extraction time predicted by the concatenated AI model was compared with the actual extraction time.ResultsThe final combined model (CNN + MLP) model achieved an R value of 0.8315, an R-squared value of 0.6839, a p-value of less than 0.0001, and a mean absolute error (MAE) of 2.95 min with the test dataset.ConclusionsOur proposed model for predicting time to extract the mandibular third molar tooth performs well with a high accuracy in clinical practice.

【 授权许可】

CC BY   
© The Author(s) 2022

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