Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs

2021 ◽  
pp. 20200172
Author(s):  
Münevver Coruh Kılıc ◽  
Ibrahim Sevki Bayrakdar ◽  
Özer Çelik ◽  
Elif Bilgir ◽  
Kaan Orhan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Confusion Matrix ◽  
Forensic Identification ◽  
Deciduous Teeth ◽  
Time Saving ◽  
Panoramic Radiographs ◽  
Intelligence System ◽  
Promising Tool ◽  
Panoramic Images

Objective: This study evaluated the use of a deep-learning approach for automated detection and numbering of deciduous teeth in children as depicted on panoramic radiographs. Methods and materials: An artificial intelligence (AI) algorithm (CranioCatch, Eskisehir-Turkey) using Faster R-CNN Inception v2 (COCO) models were developed to automatically detect and number deciduous teeth as seen on pediatric panoramic radiographs. The algorithm was trained and tested on a total of 421 panoramic images. System performance was assessed using a confusion matrix. Results: The AI system was successful in detecting and numbering the deciduous teeth of children as depicted on panoramic radiographs. The sensitivity and precision rates were high. The estimated sensitivity, precision, and F1 score were 0.9804, 0.9571, and 0.9686, respectively. Conclusion: Deep-learning-based AI models are a promising tool for the automated charting of panoramic dental radiographs from children. In addition to serving as a time-saving measure and an aid to clinicians, AI plays a valuable role in forensic identification.

scholarly journals DIAGNOSTIC CHARTING ON PANORAMIC RADIOGRAPHY USING DEEP- LEARNING ARTIFICIAL INTELLIGENCE SYSTEM

2021 ◽  
Author(s):  
Melike Başaran ◽  
Ozer Celik ◽  
İbrahim Bayrakdar ◽  
Elif Bilgir ◽  
Kaan Orhan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Panoramic Radiography ◽  
Model Development ◽  
Radiographic Examination ◽  
Dental Calculus ◽  
Impacted Tooth ◽  
Validation Data ◽  
Panoramic Radiographs ◽  
Intelligence System

Aims of the Study: A radiographic examination is a significant part of the clinical routine for the diagnosis, management, and follow-up of the disease. Artificial intelligence in dentistry shows that the deep learning technique high enough quality and effective to diagnose and interpret the images in the dental practice. For this purpose, it is aimed to evaluate diagnostic charting on panoramic radiography using a deep-learning AI system in this study. Methods: 1084 anonymized dental panoramic radiographs were labeled for 10 different dental situations including crown, pontic, root-canal treated tooth, implant, implant-supported crown, impacted tooth, residual root, filling, caries, and dental calculus. AI Model (Craniocatch, Eskişehir, Turkey) based on a deep CNN method was proposed. A Faster R-CNN Inception v2 (COCO) model implemented with Tensorflow library was used for model development. The training and validation data sets were used to predict and generate optimal CNN algorithm weight factors. Results: The proposed artificial intelligence model has promising results for detecting dental conditions in panoramic radiographs except for caries and dental calculus. The most successful F1 Scores were obtained from the implant, crown, and implant-supported crown as 0,9433, 0,9122, 0,8947, respectively. Conclusion: Thanks to the improvement of the success rate of AI models in all areas of dentistry radiology, it is predicted that they will help physicians especially in panoramic diagnosis and treatment planning, as well as digital-based student education, especially in this pandemic period when online training is on our agenda.

Artificial intelligence system for detecting superficial laryngopharyngeal cancer with high efficiency of deep learning

Head & Neck ◽  
2020 ◽  
Vol 42 (9) ◽  
pp. 2581-2592
Author(s):  
Atsushi Inaba ◽  
Keisuke Hori ◽  
Yusuke Yoda ◽  
Hiroaki Ikematsu ◽  
Hiroaki Takano ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
High Efficiency ◽  
Artificial Intelligence System ◽  
Intelligence System

scholarly journals Artificial Intelligence Model to Detect Real Contact Relationship between Mandibular Third Molars and Inferior Alveolar Nerve Based on Panoramic Radiographs

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1664
Author(s):  
Tianer Zhu ◽  
Daqian Chen ◽  
Fuli Wu ◽  
Fudong Zhu ◽  
Haihua Zhu
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Inferior Alveolar Nerve ◽  
Third Molars ◽  
Panoramic Radiographs ◽  
The Real ◽  
Detection Model ◽  
Cooperative Approach ◽  
Real Contact ◽  
Mandibular Third Molars

This study aimed to develop a novel detection model for automatically assessing the real contact relationship between mandibular third molars (MM3s) and the inferior alveolar nerve (IAN) based on panoramic radiographs processed with deep learning networks, minimizing pseudo-contact interference and reducing the frequency of cone beam computed tomography (CBCT) use. A deep-learning network approach based on YOLOv4, named as MM3-IANnet, was applied to oral panoramic radiographs for the first time. The relationship between MM3s and the IAN in CBCT was considered the real contact relationship. Accuracy metrics were calculated to evaluate and compare the performance of the MM3–IANnet, dentists and a cooperative approach with dentists and the MM3–IANnet. Our results showed that in comparison with detection by dentists (AP = 76.45%) or the MM3–IANnet (AP = 83.02%), the cooperative dentist–MM3–IANnet approach yielded the highest average precision (AP = 88.06%). In conclusion, the MM3-IANnet detection model is an encouraging artificial intelligence approach that might assist dentists in detecting the real contact relationship between MM3s and IANs based on panoramic radiographs.

scholarly journals Classification of caries in third molars on panoramic radiographs using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shankeeth Vinayahalingam ◽  
Steven Kempers ◽  
Lorenzo Limon ◽  
Dionne Deibel ◽  
Thomas Maal ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Third Molar ◽  
Area Under The Curve ◽  
Third Molars ◽  
Data Set ◽  
Panoramic Radiographs ◽  
Carious Lesions ◽  
Panoramic Images

AbstractThe objective of this study is to assess the classification accuracy of dental caries on panoramic radiographs using deep-learning algorithms. A convolutional neural network (CNN) was trained on a reference data set consisted of 400 cropped panoramic images in the classification of carious lesions in mandibular and maxillary third molars, based on the CNN MobileNet V2. For this pilot study, the trained MobileNet V2 was applied on a test set consisting of 100 cropped PR(s). The classification accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved an accuracy of 0.87, a sensitivity of 0.86, a specificity of 0.88 and an AUC of 0.90 for the classification of carious lesions of third molars on PR(s). A high accuracy was achieved in caries classification in third molars based on the MobileNet V2 algorithm as presented. This is beneficial for the further development of a deep-learning based automated third molar removal assessment in future.

scholarly journals Application of a Deep Learning Artificial Intelligence System for Individual Tooth Identification

2021 ◽  
Vol 20 (2) ◽  
pp. 98-108
Author(s):  
Yuriko Igarashi ◽  
Shintaro Kondo ◽  
Minami Kaneko ◽  
Megumi Aibara ◽  
Fumio Uchikoba
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Artificial Intelligence System ◽  
Intelligence System

scholarly journals Diagnostic charting of panoramic radiography using deep-learning artificial intelligence system

2021 ◽  
Author(s):  
Melike Başaran ◽  
Özer Çelik ◽  
Ibrahim Sevki Bayrakdar ◽  
Elif Bilgir ◽  
Kaan Orhan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Panoramic Radiography ◽  
Artificial Intelligence System ◽  
Intelligence System

scholarly journals Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tao Wang ◽  
Na Song ◽  
Lingling Liu ◽  
Zichao Zhu ◽  
Bing Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Intracerebral Hemorrhage ◽  
Diagnostic System ◽  
Absolute Error ◽  
Volume Measurement ◽  
Spontaneous Intracerebral Hemorrhage ◽  
Promising Tool ◽  
Hemorrhage Volume ◽  
Prediction Of Prognosis

Abstract Background Accurate measurement of hemorrhage volume is critical for both the prediction of prognosis and the selection of appropriate clinical treatment after spontaneous intracerebral hemorrhage (ICH). This study aimed to evaluate the performance and accuracy of a deep learning-based automated segmentation algorithm in segmenting spontaneous intracerebral hemorrhage (ICH) volume either with or without intraventricular hemorrhage (IVH) extension. We compared this automated pipeline with two manual segmentation techniques. Methods We retrospectively reviewed 105 patients with acute spontaneous ICH. Depending on the presence of IVH extension, patients were divided into two groups: ICH without (n = 56) and with IVH (n = 49). ICH volume of the two groups were segmented and measured using a deep learning-based artificial intelligence (AI) diagnostic system and computed tomography-based planimetry (CTP), and the ABC/2 score were used to measure hemorrhage volume in the ICH without IVH group. Correlations and agreement analyses were used to analyze the differences in volume and length of processing time among the three segmentation approaches. Results In the ICH without IVH group, the ICH volumes measured using AI and the ABC/2 score were comparable to CTP segmentation. Strong correlations were observed among the three segmentation methods (r = 0.994, 0.976, 0.974; P < 0.001; concordance correlation coefficient [CCC] = 0.993, 0.968, 0.967). But the absolute error of the ICH volume measured by the ABC/2 score was greater than that of the algorithm (P < 0.05). In the ICH with IVH group, there is no significant differences were found between algorithm and CTP(P = 0.614). The correlation and agreement between CTP and AI were strong (r = 0.996, P < 0.001; CCC = 0.996). The AI segmentation took a significantly shorter amount of time than CTP (P < 0.001), but was slightly longer than ABC/2 score technique (P = 0.002). Conclusions The deep learning-based AI diagnostic system accurately quantified volumes of acute spontaneous ICH with high fidelity and greater efficiency compared to the CTP measurement and more accurately than the ABC/2 scores. We believe this is a promising tool to help physicians achieve precise ICH quantification in practice.

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