Tooth Localization using YOLOv3 for Dental Diagnosis on Panoramic Radiographs

Bui, Toan Huy; Hamamoto, Kazuhiko; Paing, May Phu

doi:10.1541/ieejeiss.142.557

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…The images are scaled to fit the Yolov3 model’s input size. The YOLOv3 model is used to suggest a tooth region, with Squeeze Net as the network’s base [ 28 , 29 , 30 ]. We increase the number of detection heads and concatenate the output of each detection head with a suitable layer to generate better results.…”

Section: Methodsmentioning

confidence: 99%

Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs

Bui

Hamamoto

Paing

2022

Entropy

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Caries prevention is essential for oral hygiene. A fully automated procedure that reduces human labor and human error is needed. This paper presents a fully automated method that segments tooth regions of interest from a panoramic radiograph to diagnose caries. A patient’s panoramic oral radiograph, which can be taken at any dental facility, is first segmented into several segments of individual teeth. Then, informative features are extracted from the teeth using a pre-trained deep learning network such as VGG, Resnet, or Xception. Each extracted feature is learned by a classification model such as random forest, k-nearest neighbor, or support vector machine. The prediction of each classifier model is considered as an individual opinion that contributes to the final diagnosis, which is decided by a majority voting method. The proposed method achieved an accuracy of 93.58%, a sensitivity of 93.91%, and a specificity of 93.33%, making it promising for widespread implementation. The proposed method, which outperforms existing methods in terms of reliability, and can facilitate dental diagnosis and reduce the need for tedious procedures.

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Section: Methodsmentioning

confidence: 99%

Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs

Bui

Hamamoto

Paing

2022

Entropy

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“…The results demonstrate that using CNN models to locate tooth positions stands out as a remarkably efficient approach. For another tooth localization method using YOLOv3, image enhancement techniques were utilized, reaching 95.58% and 94.90% for precision and recall [8]. This indicates the beneficial impact of this approach on tooth positioning and suggests its viability for continued refinement.…”

Section: Introductionmentioning

confidence: 93%

Detection of Tooth Position by YOLOv4 and Various Dental Problems Based on CNN With Bitewing Radiograph

Li,

Mao,

Lin

et al. 2024

IEEE Access

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Periodontitis is a high prevalence dental disease caused by bacterial infection of the bone that surrounds the tooth. Early detection and precision treatment can prevent more severe symptoms such as tooth loss. Traditionally, periodontal disease is identified and labeled manually by dental professionals. The task requires expertise and extensive experience, and it is highly repetitive and time-consuming. The aim of this study is to explore the application of AI in the field of dental medicine. With the inherent learning capabilities, AI exhibits remarkable proficiency in processing extensive datasets and effectively managing repetitive tasks. This is particularly advantageous in professions demanding extensive experiential knowledge, such as dentistry. By harnessing AI, the potential arises to amplify process efficiency and velocity. In this study, bitewing radiographs are used as the image source, and there are two major steps to detect the dental symptoms including (1) tooth position identification, and (2) symptom identification. The study combines image enhancement techniques and tooth position identification using Gaussian filtering and adaptive binarization for data preprocessing, facilitated by the YOLOv4 model to precisely mark tooth positions. The subsequent step enhances symptom area visibility via contrast enhancement, utilizing a CNN model, particularly the AlexNet model, with significant improvements in caries recognition accuracy (92.85%) and restorations recognition accuracy (96.55%) compared to prior research. Moreover, the inclusion of periodontal disease symptoms achieves an accuracy of 91.13%. By harnessing deep learning techniques based on CNN models, this research enhances diagnostic precision, reduces errors, and increases efficiency for dentists, thereby providing meticulous and swift patient care. This innovation not only saves time but also has the potential for widespread implementation in remote and preventive medicine, aligning with the aspiration of universal health care accessibility.

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