Characterization of dental pathologies using digital panoramic X-ray images based on texture analysis

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.

Tooth-Related Disease Detection System Based on Panoramic Images and Optimization Through Automation: Development Study

Background

Early detection of tooth-related diseases in patients plays a key role in maintaining their dental health and preventing future complications. Since dentists are not overly attentive to tooth-related diseases that may be difficult to judge visually, many patients miss timely treatment. The 5 representative tooth-related diseases, that is, coronal caries or defect, proximal caries, cervical caries or abrasion, periapical radiolucency, and residual root can be detected on panoramic images. In this study, a web service was constructed for the detection of these diseases on panoramic images in real time, which helped shorten the treatment planning time and reduce the probability of misdiagnosis.

Objective

This study designed a model to assess tooth-related diseases in panoramic images by using artificial intelligence in real time. This model can perform an auxiliary role in the diagnosis of tooth-related diseases by dentists and reduce the treatment planning time spent through telemedicine.

Methods

For learning the 5 tooth-related diseases, 10,000 panoramic images were modeled: 4206 coronal caries or defects, 4478 proximal caries, 6920 cervical caries or abrasion, 8290 periapical radiolucencies, and 1446 residual roots. To learn the model, the fast region-based convolutional network (Fast R-CNN), residual neural network (ResNet), and inception models were used. Learning about the 5 tooth-related diseases completely did not provide accurate information on the diseases because of indistinct features present in the panoramic pictures. Therefore, 1 detection model was applied to each tooth-related disease, and the models for each of the diseases were integrated to increase accuracy.

Results

The Fast R-CNN model showed the highest accuracy, with an accuracy of over 90%, in diagnosing the 5 tooth-related diseases. Thus, Fast R-CNN was selected as the final judgment model as it facilitated the real-time diagnosis of dental diseases that are difficult to judge visually from radiographs and images, thereby assisting the dentists in their treatment plans.

Conclusions

The Fast R-CNN model showed the highest accuracy in the real-time diagnosis of dental diseases and can therefore play an auxiliary role in shortening the treatment planning time after the dentists diagnose the tooth-related disease. In addition, by updating the captured panoramic images of patients on the web service developed in this study, we are looking forward to increasing the accuracy of diagnosing these 5 tooth-related diseases. The dental diagnosis system in this study takes 2 minutes for diagnosing 5 diseases in 1 panoramic image. Therefore, this system plays an effective role in setting a dental treatment schedule.

Detecting 17 fine-grained dental anomalies from panoramic dental radiography using artificial intelligence

Panoramic dental radiography is one of the most common examinations performed in dental clinics. Compared with other dental images, it covers a wide area from individual teeth to the maxilla and mandibular area. Dental clinicians can get much information about patients’ health. However, it is time-consuming and laborious to detect all signs of anomalies because these regions are very complicated. So it is needed to filter out healthy images to save clinicians’ time to examine. For this, we applied modern artificial intelligence-based computer vision techniques. In this study, we built a model to detect 17 fine-grained dental anomalies which are critical to patients’ dental health and quality of life. We used about 23,000 anonymized panoramic dental images taken from local dental clinics from July 2020 to July 2021. Our model can detect these abnormal signs and filter out normal images with high sensitivity of about 0.99. The result indicates that our model can be used in real clinical practice to alleviate the burden of clinicians.

Application of Texture and Fractal Dimension Analysis to Evaluate Subgingival Cement Surfaces in Terms of Biocompatibility

Biocompatibility is defined as “the ability of a biomaterial, prosthesis, or medical device to perform with an appropriate host response in a specific application”. Biocompatibility is especially important for restorative dentists as they use materials that remain in close contact with living tissues for a long time. The research material involves six types of cement used frequently in the subgingival region: Ketac Fil Plus (3M ESPE, Germany), Riva Self Cure (SDI, Australia) (Glass Ionomer Cements), Breeze (Pentron Clinical, USA) (Resin-based Cement), Adhesor Carbofine (Pentron, Czech Republic), Harvard Polycarboxylat Cement (Harvard Dental, Great Britain) (Zinc polycarboxylate types of cement) and Agatos S (Chema-Elektromet, Poland) (Zinc Phosphate Cement). Texture and fractal dimension analysis was applied. An evaluation of cytotoxicity and cell adhesion was carried out. The fractal dimension of Breeze (Pentron Clinical, USA) differed in each of the tested types of cement. Adhesor Carbofine (Pentron, Czech Republic) cytotoxicity was rated 4 on a 0–4 scale. The Ketac Fil Plus (3M ESPE, Germany) and Riva Self Cure (SDI, Australia) cements showed the most favorable conditions for the adhesion of fibroblasts, despite statistically significant differences in the fractal dimension of their surfaces.

Radiographic modalities for diagnosis of caries in a historical perspective: from film to machine-intelligence supported systems

Radiographic imaging for the diagnosis of caries lesions has been a supplement to clinical examination for approximately a century. Various methods, and particularly X-ray receptors, have been developed over the years, and computer systems have focused on aiding the dentist in the detection of lesions and in estimating lesion depth. The present historical review has sampled accuracy ex vivo studies and clinical studies on radiographic caries diagnosis that have compared two or more receptors for capturing the image. The epochs of film radiography, xeroradiography, digital intraoral radiography, panoramic radiography and other extraoral methods, TACT analysis, cone-beam CT and artificial intelligence systems aiding in decision-making are reviewed. The author of this review (43 years in academia) has been involved in caries research and contributed to the literature in all the mentioned epochs.

Caries detection enhancement using texture feature maps of intraoral radiographs

Objectives

Dental caries are caused by tooth demineralization due to bacterial plaque formation. However, the resulting lesions are often discrete and thus barely recognizable in intraoral radiography images. Therefore, more advanced detection techniques are in great demand among dentists and radiographers. This study was performed to evaluate the performance of texture feature maps in the recognition of discrete demineralization related to caries plaque formation.

Methods

Digital intraoral radiology image analysis protocols incorporating first-order features (FOF), co-occurrence matrices, gray tone difference matrices, run-length matrices (RLM), local binary patterns (LBP), and k-means clustering (CLU) were used to transform the digital intraoral radiology images of 10 patients with confirmed caries, which were retrospectively reviewed in a dental clinic. The performance of the resulting texture feature maps was compared with that of radiographic images by radiologists and dental specialists.

Results

Significantly improved detection of caries spots was achieved by employing the CLU and FOF texture feature maps. The caries-affected area with sharp margins was well defined using the CLU approach. A pseudo-three-dimensional effect was observed in outlining the demineralization zones inside the cavity with the FOF 5 protocol. In contrast, the LBP and RLM techniques produced less satisfactory results with unsharp edges and less detailed depiction of the lesions.

Conclusions

This study illustrated the applicability of texture feature maps to the recognition of demineralized spots on the tooth surface debilitated by caries and identified the best performing techniques.