Deep learning-based apical lesion segmentation from panoramic radiographs

Song, Il-Seok; Shin, Hak-Kyun; Kang, Ju-Hee; Kim, Jo‐Eun; Huh, Kyung-Hoe; Yi, Won-Jin; Lee, Sam-Sun; Heo, Min-Suk

doi:10.5624/isd.20220078

Cited by 24 publications

(16 citation statements)

References 20 publications

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“…The F1 score values were 82%, 81% and 74%, respectively, with IOU thresholds of 0.3, 0.4 and 0.5. The study demonstrated the ability of the deep learning‐guided method to segment apical lesions [41]. Also, Hamdan et al indicated that DL improved the ability of dental professionals to detect apical radiolucencies on intraoral periapical radiographs [42].…”

Section: Application Of Ai In Periapical Lesions Detection In Endodon...mentioning

confidence: 99%

Applying artificial intelligence to detect and analyse oral and maxillofacial bone loss—A scoping review

et al. 2023

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Radiographic evaluation of bone changes is one of the main tools in the diagnosis of many oral and maxillofacial diseases. However, this approach to assessment has limitations in accuracy, inconsistency and comparatively low diagnostic efficiency. Recently, artificial intelligence (AI)‐based algorithms like deep learning networks have been introduced as a solution to overcome these challenges. Based on recent studies, AI can improve the detection accuracy of an expert clinician for periapical pathology, periodontal diseases and their prognostication, as well as peri‐implant bone loss. Also, AI has been successfully used to detect and diagnose oral and maxillofacial lesions with a high predictive value. This study aims to review the current evidence on artificial intelligence applications in the detection and analysis of bone loss in the oral and maxillofacial regions.

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Section: Application Of Ai In Periapical Lesions Detection In Endodon...mentioning

confidence: 99%

Applying artificial intelligence to detect and analyse oral and maxillofacial bone loss—A scoping review

et al. 2023

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“…Song et al [23] aimed to evaluate the performance of deep learning models based on CNNs (Convolutional Neural Networks) for apical lesion segmentation from panoramic radiographs. They trained a U-Net on a dataset consisting of 1000 radiographs in total.…”

Section: F Machado Et Almentioning

confidence: 99%

Automatic Teeth Segmentation Using Attention U-Net

Mahran¹,

Hussein²,

Saber³

2023

Preprint

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Teeth segmentation plays a pivotal role in dental diagnosis, treatment, planning, and the development of computer-aided dental systems. It enables precise identification and analysis of dental structures, aiding in detecting dental abnormalities, measuring tooth dimensions, and assessing oral health conditions. Accurate teeth segmentation also facilitates the automation of dental workflows, leading to improved efficiency and reduced human error. Artificial Intelligence (AI) has witnessed rapid advancements, with various approaches developed or enhanced. While Convolutional Neural Networks (CNNs) have been widely used in medical image segmentation, the U-Net architecture has emerged as a standout performer due to its exceptional segmentation capabilities. This paper presents a proof of concept for the Attention U-Net architecture, as previously used in [1], applied to teeth segmentation. The study demonstrates the superior performance of this network in accurately segmenting teeth using a newly available benchmark dataset called Tufts Dental X-Ray Dataset. When trained and tested on 10-fold cross-validation, the model achieved an average dice coefficient of 95.01%, intersection over union of 90.6%, and pixel accuracy of 98.82%. These scores surpass those of all other networks implemented on the same dataset. By leveraging the Attention U-Net architecture, our research showcases the potential of advanced AI techniques in dental radiography. The findings contribute to the ongoing efforts to develop automated systems to assist dental professionals in their clinical practice.

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“…For apical lesion segmentation from panoramic radiographs, [20] adopts a deep learning technique. There were a total of 1691 images utilized for segmentation.…”

Section: Related Workmentioning

confidence: 99%

Multi Oral Disease Classification from Panoramic Radiograph using Transfer Learning and XGBoost

Jaiswal¹,

Katkar²,

Bhirud³

2022

IJACSA

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The subject of oral healthcare is a crucial research field with significant technological development. This research examines the field of oral health care known as dentistry, a branch of medicine concerned with the anatomy, development, and disorders of the teeth. Good oral health is essential for speaking, smiling, testing, touching, digesting food, swallowing, and many other aspects, such as expressing a variety of emotions through facial expressions. Comfort in doing all these activities contributes to a person's self-confidence. For diagnosing multiple oral diseases at a time panoramic radiograph is used. Oral healthcare experts are important to appropriately detect and classify disorders. This automated approach was developed to eliminate the overhead of experts and the time required for diagnosis. This research is based on a self-created dataset of 500 images representing six distinct diseases in 46 possible combinations. Tooth wear, periapical, periodontitis, tooth decay, missing tooth, and impacted tooth are all examples of diseases. This system is developed using the concept of transfer learning with the use of a different pre-trained network such as "ResNet50V2", "ResNet101V2", "MobileNetV3Large", "MobileNetV3Small", "MobileNet", "EfficientNetB0", "EfficientNetB1", and "EfficientNetB2" with XGBoost and to get the final prediction The images in the dataset were divided into 80% training and 20% images for testing. To assess the performance of this system, various measuring metrics are used. Experiments revealed that the proposed model detected Tooth wear, periapical, periodontitis, tooth decay, missing tooth, and impacted tooth with an accuracy of 91.8%, 92.2%, 92.4%, 93.2%, 91.6%, and 90.8%, respectively.

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Deep learning-based apical lesion segmentation from panoramic radiographs

Cited by 24 publications

References 20 publications

Applying artificial intelligence to detect and analyse oral and maxillofacial bone loss—A scoping review

Applying artificial intelligence to detect and analyse oral and maxillofacial bone loss—A scoping review

Automatic Teeth Segmentation Using Attention U-Net

Multi Oral Disease Classification from Panoramic Radiograph using Transfer Learning and XGBoost

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