Bitewing
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OBJECTIVE: The objective of this study was to compare the detection of caries in bitewing radiographs by multiple dentists with an automatic method and to evaluate the detection performance in the absence of a reliable ground truth. MATERIALS AND METHODS: Four experts and three novices marked caries using bounding boxes in 100 bitewing radiographs. The same dataset was processed by an automatic object detection deep learning method. All annotators were compared in terms of the number of errors and intersection over union (IoU) using pairwise comparisons, with respect to the consensus standard, and with respect to the annotator of the training dataset of the automatic method. RESULTS: The number of lesions marked by experts in 100 images varied between 241 and 425. Pairwise comparisons showed that the automatic method outperformed all dentists except the original annotator in the mean number of errors, while being among the best in terms of IoU. With respect to a consensus standard, the performance of the automatic method was best in terms of the number of errors and slightly below average in terms of IoU. Compared with the original annotator, the automatic method had the highest IoU and only one expert made fewer errors. CONCLUSIONS: The automatic method consistently outperformed novices and performed as well as highly experienced dentists. CLINICAL SIGNIFICANCE: The consensus in caries detection between experts is low. An automatic method based on deep learning can improve both the accuracy and repeatability of caries detection, providing a useful second opinion even for very experienced dentists.
- MeSH
- interproximální technika MeSH
- lidé MeSH
- náchylnost k zubnímu kazu * MeSH
- zubní kaz * diagnostické zobrazování MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Práce podává přehled způsobů intraorálního i extraorálního radiografického vyšetření v dětské stomatologii.Uvádí hlavní indikace rtg vyšetřování dětí, typy a rozměry běžně používaných rtg filmů, techniky zhotovení a způsoby interpretace rtg snímků. Je zdůrazněn význam bitewing techniky pro detekci zubního kazu a jsou uvedeny vhodné časové intervaly pro opakování bitewing snímků v dočasné i stálé dentici. Je diskutována oprávněnost systematického rtg vyšetřování pro jiné důvody než zubní kaz a jsou uvedeny i možnosti ochrany dítěte před nadměrným ozářením. Autoři dále diskutují i možnosti využití digitální radiografie u dětí.
The paper presents a survey of the modes of intraoral and extraoral radiographic examination in children stomatology. Main indication of X-ray examination in children, types and dimension of the commonly used X-ray films, techniques of elaboration and the modes of interpretation of X-ray pictures are described. The importance of the bitewing technique for the detection of caries is pointed out and suitable time intervals for repetition of the bitewing pictures in temporary and permanent dentition are presented. The authors discuss the feasibility of systematic X-ray examination for other reasons than caries and describe the possibilities of protecting the child against superfluous irradiation. The authors also discuss the possibilities of using digital radiography in children.
- MeSH
- dospělí MeSH
- fluorescence MeSH
- impedanční spektroskopie přístrojové vybavení MeSH
- interproximální technika přístrojové vybavení MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- optické zobrazování metody přístrojové vybavení MeSH
- senioři MeSH
- statistika jako téma MeSH
- zubní kaz diagnostické zobrazování MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- senioři MeSH
- Publikační typ
- srovnávací studie MeSH
- Geografické názvy
- Dánsko MeSH
OBJECTIVE: The aim of this work was to assemble a large annotated dataset of bitewing radiographs and to use convolutional neural networks to automate the detection of dental caries in bitewing radiographs with human-level performance. MATERIALS AND METHODS: A dataset of 3989 bitewing radiographs was created, and 7257 carious lesions were annotated using minimal bounding boxes. The dataset was then divided into 3 parts for the training (70%), validation (15%), and testing (15%) of multiple object detection convolutional neural networks (CNN). The tested CNN architectures included YOLOv5, Faster R-CNN, RetinaNet, and EfficientDet. To further improve the detection performance, model ensembling was used, and nested predictions were removed during post-processing. The models were compared in terms of the [Formula: see text] score and average precision (AP) with various thresholds of the intersection over union (IoU). RESULTS: The twelve tested architectures had [Formula: see text] scores of 0.72-0.76. Their performance was improved by ensembling which increased the [Formula: see text] score to 0.79-0.80. The best-performing ensemble detected caries with the precision of 0.83, recall of 0.77, [Formula: see text], and AP of 0.86 at IoU=0.5. Small carious lesions were predicted with slightly lower accuracy (AP 0.82) than medium or large lesions (AP 0.88). CONCLUSIONS: The trained ensemble of object detection CNNs detected caries with satisfactory accuracy and performed at least as well as experienced dentists (see companion paper, Part II). The performance on small lesions was likely limited by inconsistencies in the training dataset. CLINICAL SIGNIFICANCE: Caries can be automatically detected using convolutional neural networks. However, detecting incipient carious lesions remains challenging.
- MeSH
- deep learning * MeSH
- lidé MeSH
- náchylnost k zubnímu kazu MeSH
- neuronové sítě MeSH
- zubní kaz * diagnostické zobrazování MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
OBJECTIVES: Class imbalance in datasets is one of the challenges of machine learning (ML) in medical image analysis. We employed synthetic data to overcome class imbalance when segmenting bitewing radiographs as an exemplary task for using ML. METHODS: After segmenting bitewings into classes, i.e. dental structures, restorations, and background, the pixel-level representation of implants in the training set (1543 bitewings) and testing set (177 bitewings) was 0.03 % and 0.07 %, respectively. A diffusion model and a generative adversarial network (pix2pix) were used to generate a dataset synthetically enriched in implants. A U-Net segmentation model was trained on (1) the original dataset, (2) the synthetic dataset, (3) on the synthetic dataset and fine-tuned on the original dataset, or (4) on a dataset which was naïvely oversampled with images containing implants. RESULTS: U-Net trained on the original dataset was unable to segment implants in the testing set. Model performance was significantly improved by naïve over-sampling, achieving the highest precision. The model trained only on synthetic data performed worse than naïve over-sampling in all metrics, but with fine-tuning on original data, it resulted in the highest Dice score, recall, F1 score and ROC AUC, respectively. The performance on other classes than implants was similar for all strategies except training only on synthetic data, which tended to perform worse. CONCLUSIONS: The use of synthetic data alone may deteriorate the performance of segmentation models. However, fine-tuning on original data could significantly enhance model performance, especially for heavily underrepresented classes. CLINICAL SIGNIFICANCE: This study explored the use of synthetic data to enhance segmentation of bitewing radiographs, focusing on underrepresented classes like implants. Pre-training on synthetic data followed by fine-tuning on original data yielded the best results, highlighting the potential of synthetic data to advance AI-driven dental imaging and ultimately support clinical decision-making.
- MeSH
- lidé MeSH
- počítačové zpracování obrazu * metody MeSH
- strojové učení * MeSH
- zubní implantáty MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
