Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs

Fukuda, Motoki; Ariji, Yoshiko; Kise, Yoshitaka; Nozawa, Michihito; Kuwada, Chiaki; Funakoshi, Takuma; Muramatsu, Chisako; Fujita, Hiroshi; Katsumata, Akitoshi; Ariji, Eiichiro

doi:10.1016/j.oooo.2020.04.005

Cited by 55 publications

(54 citation statements)

References 25 publications

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“…Kwak et al recently reported successful results in determining the mandibular canal by the CNN method in CBCT images and stated that this may be an opportunity for future dental planning [ 18 ]. Similarly, Fukuda et al evaluated the relationship between the 3rd mandibular molar tooth and the mandibular canal in their study on 600 panoramic radiographs [ 43 ]. Jaskari et.…”

Section: Discussionmentioning

confidence: 99%

A deep learning approach for dental implant planning in cone-beam computed tomography images

et al. 2021

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Background The aim of this study was to evaluate the success of the artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images. Methods Seventy-five CBCT images were included in this study. In these images, bone height and thickness in 508 regions where implants were required were measured by a human observer with manual assessment method using InvivoDental 6.0 (Anatomage Inc. San Jose, CA, USA). Also, canals/sinuses/fossae associated with alveolar bones and missing tooth regions were detected. Following, all evaluations were repeated using the deep convolutional neural network (Diagnocat, Inc., San Francisco, USA) The jaws were separated as mandible/maxilla and each jaw was grouped as anterior/premolar/molar teeth region. The data obtained from manual assessment and AI methods were compared using Bland–Altman analysis and Wilcoxon signed rank test. Results In the bone height measurements, there were no statistically significant differences between AI and manual measurements in the premolar region of mandible and the premolar and molar regions of the maxilla (p > 0.05). In the bone thickness measurements, there were statistically significant differences between AI and manual measurements in all regions of maxilla and mandible (p < 0.001). Also, the percentage of right detection was 72.2% for canals, 66.4% for sinuses/fossae and 95.3% for missing tooth regions. Conclusions Development of AI systems and their using in future for implant planning will both facilitate the work of physicians and will be a support mechanism in implantology practice to physicians.

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Section: Discussionmentioning

confidence: 99%

A deep learning approach for dental implant planning in cone-beam computed tomography images

et al. 2021

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“…Certain radiographic features such as darkening of the root and narrowing of the mandibular canal have been reported as risk factors for IAN injuries, although its clinical correlation was low 3 . Due to the development of cone-beam computerized tomography (CBCT), determination of positioning between the IAN and teeth has become more accurate, and CBCT is recommended before M3 extraction when the two aforementioned structures are superimposed on panoramic radiography 4 . However, the disadvantages of CBCT include higher radiation doses compared to two-dimensional imaging and the presence of image artifacts mainly produced by metal restorations 5 .…”

Section: Introductionmentioning

confidence: 99%

“…Various studies have applied AI algorithms to read panoramic radiographs for clinical conditions such as age estimation 7 , osteoporosis 8 , 9 , vertical root fracture 10 , automatic teeth detection and numbering 11 , apical lesions 12 , maxillary sinusitis 13 , detecting and segmenting the approximation of the inferior alveolar nerve and mandibular third molar 14 , periodontal bone loss 15 , gender determination 16 , and temporomandibular joint osteoarthritis 17 , 18 . Although some studies evaluate the relationship between M3 and IAN, those studies usually determine whether AI could determine M3 and IAN on panoramic radiograph or CBCT, which was easily discernible in human eyes 4 , 14 , 19 , 20 . A recent study predicting the difficulty of extraction by deep learning was easily distinguishable by humans 21 .…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography

Choi

Lee

Jeong

et al. 2022

Sci Rep

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Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this study were to develop an artificial intelligence (AI) model to determine two positional relationships (true contact and bucco-lingual position) between M3 and IAN when they were overlapped in panoramic radiographs and compare its performance with that of oral and maxillofacial surgery (OMFS) specialists. A total of 571 panoramic images of M3 from 394 patients was used for this study. Among the images, 202 were classified as true contact, 246 as intimate, 61 as IAN buccal position, and 62 as IAN lingual position. A deep convolutional neural network model with ResNet-50 architecture was trained for each task. We randomly split the dataset into 75% for training and validation and 25% for testing. Model performance was superior in bucco-lingual position determination (accuracy 0.76, precision 0.83, recall 0.67, and F1 score 0.73) to true contact position determination (accuracy 0.63, precision 0.62, recall 0.63, and F1 score 0.61). AI exhibited much higher accuracy in both position determinations compared to OMFS specialists. In determining true contact position, OMFS specialists demonstrated an accuracy of 52.68% to 69.64%, while the AI showed an accuracy of 72.32%. In determining bucco-lingual position, OMFS specialists showed an accuracy of 32.26% to 48.39%, and the AI showed an accuracy of 80.65%. Moreover, Cohen’s kappa exhibited a substantial level of agreement for the AI (0.61) and poor agreements for OMFS specialists in bucco-lingual position determination. Determining the position relationship between M3 and IAN is possible using AI, especially in bucco-lingual positioning. The model could be used to support clinicians in the decision-making process for M3 treatment.

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“…Studies have successfully used CNN systems for detecting sinus pathologies 16 , vertical root fractures 10 , mandibular canals 28 , jaw tumors 22 , first molar tooth root morphology 11 , and teeth and tooth numbers 25 from panoramic radiographs. Other studies have used CNN systems for analyzing dental radiography images obtained by techniques such as periapical, bitewing, and CBCT.…”

Section: Discussionmentioning

confidence: 99%

Success of Artificial Intelligence System in Determining Alveolar Bone Loss From Dental Panoramic Radiography Images

Kurt

Çelik

Bayrakdar³

et al. 2020

Cumhuriyet Dental Journal

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This study aims to detect alveolar bone loss from dental panoramic radiography images by using an artificial intelligence (AI) system. Materials and Methods:A total of 2276 panoramic radiography images were evaluated. Of these, 1137 were of bone loss cases and 1139 were of periodontally healthy cases. This dataset is divided into training (n = 1856), validation (n = 210), and testing (n = 210) sets. All images were resized to 1472x718 pixels before training. A random sequence was created using the open-source Python programming language and OpenCV, NumPy, Pandas, and Matplotlib libraries. A pretrained Google Net Inception v3 convolutional neural network (CNN) was used for preprocessing, and the datasets were trained using transfer learning. The diagnostic performance was evaluated using a confusion matrix in terms of the sensitivity, specificity, precision, accuracy, and F1 score.Results: Of 105 cases with bone loss, the CNN system detected 99 with sensitivity, specificity, precision, accuracy, and F1 score of 0.94, 0.88, 0.89, 0.91, and 0.91, respectively. Conclusions:The CNN system successfully determines periodontal bone loss. Therefore, it can be used to facilitate diagnosis and treatment planning by oral physicians in the future.

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Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs

Cited by 55 publications

References 25 publications

A deep learning approach for dental implant planning in cone-beam computed tomography images

A deep learning approach for dental implant planning in cone-beam computed tomography images

Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography

Success of Artificial Intelligence System in Determining Alveolar Bone Loss From Dental Panoramic Radiography Images

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