Comparison of diagnosis of cracked tooth using contrast-enhanced CBCT and micro-CT

The exploration of a compound cone-beam CT contrast agent for diagnosis of human extracted cracked tooth

Objectives

This study aims to investigate the use of sodium iodide (NaI), dimethyl sulfoxide (DMSO), ethyl alcohol, and ethyl acetate as cone-beam CT (CBCT) contrast agents for diagnosing cracked teeth. The optimal delay time for detecting the number of crack lines beyond the dentino-enamel junction (Nd), the number of cracks extending from the occlusal surface to the pulp cavity (Np), and the depth of the crack lines was explored.

Methods

14 human extracted cracked teeth were collected, 12 were used for enhanced scanning, and 2 were used for exploring the characteristic of crack lines. The teeth were scanned in 3 CBCT enhanced scanning (ES) modes: ES1 using meglumine diatrizoate (MD); ES2 using NaI and DMSO, ES3 using NaI, DMSO, ethyl alcohol and ethyl acetate. Three delay times (15mins, 30mins, and 60mins) were set for scanning. Nd, Np, and depth of crack lines were evaluated.

Results

There were totally 24 crack lines on 12 cracked teeth. Nd was 10 in ES1 at 60mins, 24 in ES2 at 60mins and 24 in ES3 at 15mins. Np was 1 in ES1 at 60mins, 10 in ES2 at 60mins and 21 in ES3 at 60mins, and there were significantly different among them (p < 0.01). The average depth presented on ES3 was significantly deeper than ES1 and ES2 (p < 0.01).

Conclusion

NaI, DMSO, ethyl alcohol and ethyl acetate show potential as contrast agents for enhanced CBCT scanning in diagnosis of cracked teeth and their depth in vivo. A delay time of 15 min is necessary to confirm the existence of crack lines, while a longer delay time is required to ascertain if these crack lines extend to the pulp cavity.

Automatic segmentation of ameloblastoma on ct images using deep learning with limited data

BACKGROUND

Ameloblastoma, a common benign tumor found in the jaw bone, necessitates accurate localization and segmentation for effective diagnosis and treatment. However, the traditional manual segmentation method is plagued with inefficiencies and drawbacks. Hence, the implementation of an AI-based automatic segmentation approach is crucial to enhance clinical diagnosis and treatment procedures.

METHODS

We collected CT images from 79 patients diagnosed with ameloblastoma and employed a deep learning neural network model for training and testing purposes. Specifically, we utilized the Mask R-CNN neural network structure and implemented image preprocessing and enhancement techniques. During the testing phase, cross-validation methods were employed for evaluation, and the experimental results were verified using an external validation set. Finally, we obtained an additional dataset comprising 200 CT images of ameloblastoma from a different dental center to evaluate the model's generalization performance.

RESULTS

During extensive testing and evaluation, our model successfully demonstrated the capability to automatically segment ameloblastoma. The DICE index achieved an impressive value of 0.874. Moreover, when the IoU threshold ranged from 0.5 to 0.95, the model's AP was 0.741. For a specific IoU threshold of 0.5, the model achieved an AP of 0.914, and for another IoU threshold of 0.75, the AP was 0.826. Our validation using external data confirms the model's strong generalization performance.

CONCLUSION

In this study, we successfully applied a neural network model based on deep learning that effectively performs automatic segmentation of ameloblastoma. The proposed method offers notable advantages in terms of efficiency, accuracy, and speed, rendering it a promising tool for clinical diagnosis and treatment.

Diagnosis of cracked tooth: Clinical status and research progress

Cracked tooth is a common dental hard tissue disease.The involvement of cracks directly affects the selection of treatment and restoration of the affected teeth.It is helpful to choose more appropriate treatment options and evaluate the prognosis of the affected tooth accurately to determine the actual involvement of the crack.However, it is often difficult to accurately and quantitatively assess the scope of cracks at present.So it is necessary to find a real method of early quantitative and non-destructive crack detection.This article reviews the current clinical detection methods and research progress of cracked tooth in order to provide a reference for finding a clinical detection method for cracked tooth.