An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population

Bağ, İrem; Bilgir, Elif; Bayrakdar, İbrahim Şevki; Baydar, Oğuzhan; Atak, Fatih Mehmet; Çelik, Özer; Orhan, Kaan

doi:10.1186/s12903-023-03532-8

Cited by 5 publications

(2 citation statements)

References 48 publications

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“…In their deep learning study, Bağ et al, developed YOLO-v5 models to automatically detect nine important anatomical structures in approximately one thousand panoramic radiographs of pediatric patients. The F1 score and sensitivity values for the labelled anatomical regions were 0.98–0.99 for maxillary sinus, 1–1 for orbit, 0.97–0.99 for mandibular canal, 0.88–0.92 for mental foramen, 0.95–0.95 for foramen mandibula, 0.99–0.99 for incisura mandibula, 0.92–0.92 for articular eminence, 0.94–0.99 for condylar, and 0.86–0.97 for coronoid [ 7 ].…”

Section: Discussionmentioning

confidence: 99%

“…AI has become increasingly popular in radiographic interpretation, including in dentomaxillofacial radiology. AI-based methods assist in image interpretation, providing faster data identification and improved diagnostic accuracy [ 7 ]. This is particularly beneficial in recognizing and managing dental and craniofacial conditions, while also eliminating errors associated with human fatigue [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition

Beser,

Reis,

Berber

et al. 2024

BMC Med Imaging

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Objectives In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs. Methods A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed. Results The sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation. Conclusions YOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition

Beser,

Reis,

Berber

et al. 2024

BMC Med Imaging

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DMAF-Net: deformable multi-scale adaptive fusion network for dental structure detection with panoramic radiographs

Li,

Wang,

Liu

2024

Dentomaxillofacial Radiology

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Objectives Panoramic radiography is one of the most commonly used diagnostic modalities in dentistry. Automatic recognition of panoramic radiography helps dentists in decision support. In order to improve the accuracy of the detection of dental structural problems in panoramic radiographs, we have improved the YOLO network and verified the feasibility of this new method in aiding the detection of dental problems. Methods We propose a Deformable Multi-scale Adaptive Fusion Net (DMAF-Net) to detect five types of dental situations (impacted teeth, missing teeth, implants, crown restorations and root canal-treated teeth) in panoramic radiography by improving the You Only Look Once (YOLO) network. In DMAF-Net, we propose different modules to enhance the feature extraction capability of the network as well as to acquire high-level features at different scales, while using adaptive spatial feature fusion to solve the problem of scale mismatches of different feature layers, which effectively improves the detection performance. In order to evaluate the detection performance of the models, we compare the experimental results of different models in the test set, and select the optimal results of the models by calculating the average of different metrics in each category as the evaluation criteria. Results 1474 panoramic radiographs were divided into training, validation and test sets in the ratio of 7:2:1. In the test set, the average precision and recall of DMAF-Net are 92.7% and 87.6%, respectively; the mean Average Precision (mAP0.5 and mAP [0.5:0.95]) are 91.8% and 63.7%, respectively. Conclusions The proposed DMAF-Net model improves existing deep learning models and achieves automatic detection of tooth structure problems in panoramic radiographs. This new method has great potential for new computer-aided diagnostic, teaching and clinical applications in the future.

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Automatic deep learning detection of overhanging restorations in bitewing radiographs

Magat,

Altındag,

Pertek Hatipoglu

et al. 2024

Dentomaxillofacial Radiology

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Objective This study aimed to assess the effectiveness of deep convolutional neural network (CNN) algorithms in detecting and segmentation of overhanging dental restorations in bitewing radiographs. Method and materials A total of 1160 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system) for the detection and segmentation of overhanging restorations. The data was then divided into three groups: 80% for training (930 images, 2399 labels), 10% for validation (115 images, 273 labels), and 10% for testing (115 images, 306 labels). A CNN model known as you only look once (YOLOv5) was trained to detect overhanging restorations in bitewing radiographs. After utilizing the remaining 115 radiographs to evaluate the efficacy of the proposed CNN model, the accuracy, sensitivity, precision, F1 score, and area under the receiver operating characteristic curve (AUC) were computed. Results The model demonstrated a precision of 90.9%, a sensitivity of 85.3%, and an F1 score of 88.0%. Furthermore, the model achieved an AUC of 0.859 on the Receiver Operating Characteristic (ROC) curve. The mean average precision (mAP) at an intersection over a union (IoU) threshold of 0.5 was notably high at 0.87. Conclusion The findings suggest that deep CNN algorithms are highly effective in the detection and diagnosis of overhanging dental restorations in bitewing radiographs. The high levels of precision, sensitivity, and F1 score, along with the significant AUC and mAP values, underscore the potential of these advanced deep learning techniques in revolutionizing dental diagnostic procedures.

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An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population

Cited by 5 publications

References 48 publications

YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition

YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition

DMAF-Net: deformable multi-scale adaptive fusion network for dental structure detection with panoramic radiographs

Automatic deep learning detection of overhanging restorations in bitewing radiographs

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