Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography

Fukuda, Motoki; Inamoto, Kyoko; Shibata, Naoki; Ariji, Yoshiko; Yanashita, Yudai; Kutsuna, Shota; Nakata, Kohei; Katsumata, Akitoshi; Fujita, Hiroshi; Ariji, Eiichiro

doi:10.1007/s11282-019-00409-x

Cited by 171 publications

(88 citation statements)

References 41 publications

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“…In this way, images can be used as an input for the neural networks in order to achieve several different outputs [15]. As such, deep learning methods have already shown promising results for detecting caries [13], root fractures [9], periodontal diseases [14], for differentiating cysts and jaw tumors [8], for skeletal classification on lateral cephalograms [31], and even for improving oral cancer outcomes [32]. Regarding teeth and bone segmentation, deep learning is an encouraging approach to segment anatomical structures and later on in clinical decision making [5].…”

Section: Discussionmentioning

confidence: 99%

“…In this way, automated methods may enable faster identification and classification of data and eliminate errors associated with human fatigue. Deep learning algorithms have been investigated in dentomaxillofacial radiology for the detection, classification, or diagnosis of diseases or anatomical structures, such as classification of teeth and mandibular morphology [5][6][7]; differentiation of jaw tumors [8]; and detection of root fractures [9], Sjögren's syndrome [10], maxillary sinusitis [11], calcified carotid atheroma's [12], caries [13], and periodontal diseases [14]. Although the results of previous AI research have been extremely promising, the studies are still preliminary [15].…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs

et al. 2020

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Objective To evaluate the performance of a new artificial intelligence (AI)-driven tool for tooth detection and segmentation on panoramic radiographs. Materials and methods In total, 153 radiographs were collected. A dentomaxillofacial radiologist labeled and segmented each tooth, serving as the ground truth. Class-agnostic crops with one tooth resulted in 3576 training teeth. The AI-driven tool combined two deep convolutional neural networks with expert refinement. Accuracy of the system to detect and segment teeth was the primary outcome, time analysis secondary. The Kruskal-Wallis test was used to evaluate differences of performance metrics among teeth groups and different devices and chi-square test to verify associations among the amount of corrections, presence of false positive and false negative, and crown and root parts of teeth with potential AI misinterpretations. Results The system achieved a sensitivity of 98.9% and a precision of 99.6% for tooth detection. For segmenting teeth, lower canines presented best results with the following values for intersection over union, precision, recall, F1-score, and Hausdorff distances: 95.3%, 96.9%, 98.3%, 97.5%, and 7.9, respectively. Although still above 90%, segmentation results for both upper and lower molars were somewhat lower. The method showed a clinically significant reduction of 67% of the time consumed for the manual. Conclusions The AI tool yielded a highly accurate and fast performance for detecting and segmenting teeth, faster than the ground truth alone. Clinical significance An innovative clinical AI-driven tool showed a faster and more accurate performance to detect and segment teeth on panoramic radiographs compared with manual segmentation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs

et al. 2020

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“…Deep Neural Network for Object Detection (DetectNet) outputs the XY coordinates of a detected object. This kind of neural network has been applied in different medical fields [19,44]. Keras is a library of open source neural networks written in Python.…”

Section: Discussionmentioning

confidence: 99%

“…A convolutional neural network (CNN) system was employed by Fukuda et al for detecting vertical root fractures (VRFs) in panoramic radiographies [19]. Three hundred images were used as an image dataset, of which 240 images were assigned to a training set and 60 images were assigned to a test set.…”

Section: Endodontic Treatment Detectionmentioning

confidence: 99%

Dental Images Recognition Technology and Applications: A Literature Review

Prados-Privado

Villalón²,

Martínez-Martínez³

et al. 2020

Applied Sciences

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Neural networks are increasingly being used in the field of dentistry. The aim of this literature review was to visualize the state of the art of artificial intelligence in dental applications, such as the detection of teeth, caries, filled teeth, crown, prosthesis, dental implants and endodontic treatment. A search was conducted in PubMed, the Institute of Electrical and Electronics Engineers (IEEE) Xplore and arXiv.org. Data extraction was performed independently by two reviewers. Eighteen studies were included. The variable teeth was the most analyzed (n = 9), followed by caries (n = 7). No studies detecting dental implants and filled teeth were found. Only two studies investigated endodontic applications. Panoramic radiographies were the most common image employed (n = 5), followed by periapical images (n = 3). Near-infrared light transillumination images were employed in two studies and bitewing and computed tomography (CT) were employed in one study. The included articles used a wide variety of neuronal networks to detect the described variables. In addition, the database used also had a great heterogeneity in the number of images. A standardized methodology should be used in order to increase the compatibility and robustness between studies because of the heterogeneity in the image database, type, neural architecture and results.

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“…By synergistically applying CNNs in routine care, we create an outstanding opportunity to optimize our diagnostic capacities and clinical accuracies. CNNs have shown excellent results in diagnosis and classification of diseases, such as caries staging [10], root fracture detection [11], cancer screening [12,13], and diagnosis of periodontal disease [14]. Moreover, AI applications are highly time-saving in preoperative treatment planning in implantology, orthodontics, and orthognathic surgery, by automated detection and segmentation of anatomical structures [15,16].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence (AI)-Driven Molar Angulation Measurements to Predict Third Molar Eruption on Panoramic Radiographs

Vranckx

Gerven²,

Willems³

et al. 2020

IJERPH

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The purpose of the presented Artificial Intelligence (AI)-tool was to automatically segment the mandibular molars on panoramic radiographs and extract the molar orientations in order to predict the third molars’ eruption potential. In total, 838 panoramic radiographs were used for training (n = 588) and validation (n = 250) of the network. A fully convolutional neural network with ResNet-101 backbone jointly predicted the molar segmentation maps and an estimate of the orientation lines, which was then iteratively refined by regression on the mesial and distal sides of the segmentation contours. Accuracy was quantified as the fraction of correct angulations (with predefined error intervals) compared to human reference measurements. Performance differences between the network and reference measurements were visually assessed using Bland−Altman plots. The quantitative analysis for automatic molar segmentation resulted in mean IoUs approximating 90%. Mean Hausdorff distances were lowest for first and second molars. The network angulation measurements reached accuracies of 79.7% [−2.5°; 2.5°] and 98.1% [−5°; 5°], combined with a clinically significant reduction in user-time of >53%. In conclusion, this study validated a new and unique AI-driven tool for fast, accurate, and consistent automated measurement of molar angulations on panoramic radiographs. Complementing the dental practitioner with accurate AI-tools will facilitate and optimize dental care and synergistically lead to ever-increasing diagnostic accuracies.

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Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography

Cited by 171 publications

References 41 publications

Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs

Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs

Dental Images Recognition Technology and Applications: A Literature Review

Artificial Intelligence (AI)-Driven Molar Angulation Measurements to Predict Third Molar Eruption on Panoramic Radiographs

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