A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph

Chuo, Yueh; Lin, Wenming; Chen, Tsung‐Yi; Chan, Mei-Ling; Chang, Yu-Sung; Lin, Yan‐Ru; Lin, Yuan-Jin; Shao, Yuhan; Chen, Chiung-An; Chen, Shih-Lun; Abu, Patricia Angela R.

doi:10.3390/bioengineering9120777

Cited by 9 publications

(7 citation statements)

References 25 publications

(35 reference statements)

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“…This research tested the segmentation method proposed in previous research [27]. The result showed that it worked well for front teeth but had difficulty with back teeth due to lighting or imaging conditions.…”

Section: Image Segmentationmentioning

confidence: 96%

“…The accuracy of this step affected the determination of the target object in the later steps. This research tested the fixed threshold using the Otsu algorithm, as mentioned in [25]; the iterative algorithm, as mentioned in [26]; and the adaptive threshold, as mentioned in [27]. However, for the molars' PA, the pixel brightness was a significant factor, which is different from the PA of a single tooth.…”

Section: Adaptive Thresholdmentioning

confidence: 99%

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Deep Learning for Dental Diagnosis: A Novel Approach to Furcation Involvement Detection on Periapical Radiographs

et al. 2023

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Furcation defects pose a significant challenge in the diagnosis and treatment planning of periodontal diseases. The accurate detection of furcation involvements (FI) on periapical radiographs (PAs) is crucial for the success of periodontal therapy. This research proposes a deep learning-based approach to furcation defect detection using convolutional neural networks (CNN) with an accuracy rate of 95%. This research has undergone a rigorous review by the Institutional Review Board (IRB) and has received accreditation under number 202002030B0C505. A dataset of 300 periapical radiographs of teeth with and without FI were collected and preprocessed to enhance the quality of the images. The efficient and innovative image masking technique used in this research better enhances the contrast between FI symptoms and other areas. Moreover, this technology highlights the region of interest (ROI) for the subsequent CNN models training with a combination of transfer learning and fine-tuning techniques. The proposed segmentation algorithm demonstrates exceptional performance with an overall accuracy up to 94.97%, surpassing other conventional methods. Moreover, in comparison with existing CNN technology for identifying dental problems, this research proposes an improved adaptive threshold preprocessing technique that produces clearer distinctions between teeth and interdental molars. The proposed model achieves impressive results in detecting FI with identification rates ranging from 92.96% to a remarkable 94.97%. These findings suggest that our deep learning approach holds significant potential for improving the accuracy and efficiency of dental diagnosis. Such AI-assisted dental diagnosis has the potential to improve periodontal diagnosis, treatment planning, and patient outcomes. This research demonstrates the feasibility and effectiveness of using deep learning algorithms for furcation defect detection on periapical radiographs and highlights the potential for AI-assisted dental diagnosis. With the improvement of dental abnormality detection, earlier intervention could be enabled and could ultimately lead to improved patient outcomes.

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Section: Image Segmentationmentioning

confidence: 96%

Section: Adaptive Thresholdmentioning

confidence: 99%

Deep Learning for Dental Diagnosis: A Novel Approach to Furcation Involvement Detection on Periapical Radiographs

et al. 2023

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“…The improvement in efficiency and quality provides clinicians more time for patient care. Furthermore, periapical (PA) image is utilized as an adjunct diagnostic tool, particularly in the assessment of conditions such as apical lesions [10], implants [11], and furcation involvement [12]. DPR images refer to extraoral radiographs [13], typically encompassing all teeth alongside adjacent skeletal structures and nerves, providing two-dimensional information about dental and maxillofacial osseous anatomy.…”

Section: Introductionmentioning

confidence: 99%

Classification of the Relative Position between the Third Molar and the Inferior Alveolar Nerve Using a Convolutional Neural Network Based on Transfer Learning

Chen,

Chou,

Chuo

et al. 2024

Electronics

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In recent years, there has been a significant increase in collaboration between medical imaging and artificial intelligence technology. The use of automated techniques for detecting medical symptoms has become increasingly prevalent. However, there has been a lack of research on the relationship between impacted teeth and the inferior alveolar nerve (IAN) in DPR images. The severe compression of teeth against the IAN may necessitate the requirement for nerve canal treatment. To reduce the occurrence of such events, this study aims to develop an auxiliary detection system capable of precisely locating the relative positions of the IAN and impacted teeth through object detection and image enhancement. This system is designed to shorten the duration of examinations for dentists while concurrently mitigating the chances of diagnostic errors. The innovations in this research are as follows: (1) using YOLO_v4 to identify impacted teeth and the IAN in DPR images achieves an accuracy of 88%. However, the developed algorithm in this study achieves an accuracy of 93%. (2) Image enhancement is utilized in this study to expand the dataset, with an accuracy of up to 2~3% enhancement in detecting diseases. (3) The segmentation technique proposed in this study surpasses previous methods by achieving 6% higher accuracy in dental diagnosis.

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“…Filters have been helpful in reducing the impact of point creation on cutting technology and recognition in PA [ 17 ]. Furthermore, adaptive thresholds have been suggested to improve the application of cropping technology in dental radiology research [ 18 ]. Regarding the identification of disease symptoms, the backpropagation neural network has been used to diagnose dental caries with an accuracy rate of 94.1% [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film

Chen

Chan

et al. 2023

Bioengineering

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As the popularity of dental implants continues to grow at a rate of about 14% per year, so do the risks associated with the procedure. Complications such as sinusitis and nerve damage are not uncommon, and inadequate cleaning can lead to peri-implantitis around the implant, jeopardizing its stability and potentially necessitating retreatment. To address this issue, this research proposes a new system for evaluating the degree of periodontal damage around implants using Periapical film (PA). The system utilizes two Convolutional Neural Networks (CNN) models to accurately detect the location of the implant and assess the extent of damage caused by peri-implantitis. One of the CNN models is designed to determine the location of the implant in the PA with an accuracy of up to 89.31%, while the other model is responsible for assessing the degree of Peri-implantitis damage around the implant, achieving an accuracy of 90.45%. The system combines image cropping based on position information obtained from the first CNN with image enhancement techniques such as Histogram Equalization and Adaptive Histogram Equalization (AHE) to improve the visibility of the implant and gums. The result is a more accurate assessment of whether peri-implantitis has eroded to the first thread, a critical indicator of implant stability. To ensure the ethical and regulatory standards of our research, this proposal has been certified by the Institutional Review Board (IRB) under number 202102023B0C503. With no existing technology to evaluate Peri-implantitis damage around dental implants, this CNN-based system has the potential to revolutionize implant dentistry and improve patient outcomes.

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A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph

Cited by 9 publications

References 25 publications

Deep Learning for Dental Diagnosis: A Novel Approach to Furcation Involvement Detection on Periapical Radiographs

Deep Learning for Dental Diagnosis: A Novel Approach to Furcation Involvement Detection on Periapical Radiographs

Classification of the Relative Position between the Third Molar and the Inferior Alveolar Nerve Using a Convolutional Neural Network Based on Transfer Learning

Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film

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