Inductively Coupled Intraoral Flexible Coil for Increased Visibility of Dental Root Canals in Magnetic Resonance Imaging

Surveying the landscape of diagnostic imaging in dentistry's future: Four emerging technologies with promise

BACKGROUND

Advances in digital radiography for both intraoral and panoramic imaging and cone-beam computed tomography have led the way to an increase in diagnostic capabilities for the dental care profession. In this article, the authors provide information on 4 emerging technologies with promise.

TYPES OF STUDIES REVIEWED

The authors feature the following: artificial intelligence in the form of deep learning using convolutional neural networks, dental magnetic resonance imaging, stationary intraoral tomosynthesis, and second-generation cone-beam computed tomography sources based on carbon nanotube technology and multispectral imaging. The authors review and summarize articles featuring these technologies.

RESULTS

The history and background of these emerging technologies are previewed along with their development and potential impact on the practice of dental diagnostic imaging. The authors conclude that these emerging technologies have the potential to have a substantial influence on the practice of dentistry as these systems mature. The degree of influence most likely will vary, with artificial intelligence being the most influential of the 4.

CONCLUSIONS AND PRACTICAL IMPLICATIONS

The readers are informed about these emerging technologies and the potential effects on their practice going forward, giving them information on which to base decisions on adopting 1 or more of these technologies. The 4 technologies reviewed in this article have the potential to improve imaging diagnostics in dentistry thereby leading to better patient care and heightened professional satisfaction.

Sensitivity and Specificity of MRI versus CBCT to Detect Vertical Root Fractures Using MicroCT as a Reference Standard

INTRODUCTION

Vertical root fracture (VRF) in root-canal-treated teeth frequently results in tooth loss, partly because VRFs are difficult to diagnose and when detected the fracture is often beyond the point of preservation with surgical intervention. Nonionizing magnetic resonance imaging (MRI) has demonstrated the ability to detect small VRFs, but it is unknown how its diagnostic capabilities compare with the current imaging standard for VRF detection, cone-beam computed tomography (CBCT). This investigation aimed to compare the sensitivity and specificity between MRI and CBCT for detecting VRF, using micro-computed tomography (microCT) as a reference.

METHODS

A total of 120 extracted human tooth roots were root canal treated using common techniques, and VRFs were mechanically induced in a proportion. Samples were imaged using MRI, CBCT, and microCT. Axial MRI and CBCT images were examined by 3 board-certified endodontists, who evaluated VRF status (yes/no) and gave a confidence assessment for that decision, from which a receiver operating characteristic curve was generated. Intra- and inter-rater reliability were calculated, sensitivity and specificity, and area under the curve.

RESULTS

Intra-rater reliability was 0.29-0.48 for MRI and 0.30-0.44 for CBCT. Inter-rater reliability for MRI was 0.37 and for CBCT 0.49. Sensitivity was 0.66 (95% confidence interval [CI], 0.53-0.78) and 0.58 (95% CI, 0.45-0.70), and specificity 0.72 (95% CI, 0.58-0.83) and 0.87 (95% CI, 0.75-0.95) for MRI and CBCT, respectively. Area under the curve was 0.74 (95% CI, 0.65-0.83) for MRI and 0.75 (95% CI, 0.66-0.84) for CBCT.

CONCLUSIONS

There was no significant difference in sensitivity or specificity between MRI and CBCT in detecting VRF, despite the early-stage development of MRI.

Dental MRI-only a future vision or standard of care? A literature review on current indications and applications of MRI in dentistry

MRI is increasingly used as a diagnostic tool for visualising the dentoalveolar complex. A comprehensive review of the current indications and applications of MRI in the dental specialities of orthodontics (I), endodontics (II), prosthodontics (III), periodontics (IV), and oral surgery (V), pediatric dentistry (VI), operative dentistry is still missing and is therefore provided by the present work.The current literature on dental MRI shows that it is used for cephalometry in orthodontics and dentofacial orthopaedics, detection of dental pulp inflammation, characterisation of periapical and marginal periodontal pathologies of teeth, caries detection, and identification of the inferior alveolar nerve, impacted teeth and dentofacial anatomy for dental implant planning, respectively. Specific protocols regarding the miniature anatomy of the dentofacial complex, the presence of hard tissues, and foreign body restorations are used along with dedicated coils for the improved image quality of the facial skull.Dental MRI poses a clinically useful radiation-free imaging tool for visualising the dentoalveolar complex across dental specialities when respecting the indications and limitations.

Characterizing Off-center MRI with ZTE

PURPOSE

To maximize acquisition bandwidth in zero echo time (ZTE) sequences, readout gradients are already switched on during the RF pulse, creating unwanted slice selectivity. The resulting image distortions are amplified especially when the anatomy of interest is not located at the isocenter. We aim to characterize off-center ZTE MRI of extremities such as the shoulder, knee, and hip, adjusting the carrier frequency of the RF pulse excitation for each TR.

METHODS

In ZTE MRI, radial encoding schemes are used, where the distorted slice profile due to the finite RF pulse length rotates with the k-space trajectory. To overcome these modulations for objects far away from the magnet isocenter, the frequency of the RF pulse is shifted for each gradient setting so that artifacts do not occur at a given off-center target position. The sharpness of the edges in the images were calculated and the ZTE acquisition with off-center excitation was compared to an acquisition with isocenter excitation both in phantom and in vivo off-center MRI of the shoulder, knee, and hip at 1.5 and 3T MRI systems.

RESULTS

Distortion and blurriness artifacts on the off-center MRI images of the phantom, in vivo shoulder, knee, and hip images were mitigated with off-center excitation without time or noise penalty, at no additional computational cost.

CONCLUSION

The off-center excitation allows ZTE MRI of the shoulder, knee, and hip for high-bandwidth image acquisitions for clinical settings, where positioning at the isocenter is not possible.

Artificial Intelligence-Based Prediction of Oroantral Communication after Tooth Extraction Utilizing Preoperative Panoramic Radiography

Oroantral communication (OAC) is a common complication after tooth extraction of upper molars. Profound preoperative panoramic radiography analysis might potentially help predict OAC following tooth extraction. In this exploratory study, we evaluated n = 300 consecutive cases (100 OAC and 200 controls) and trained five machine learning algorithms (VGG16, InceptionV3, MobileNetV2, EfficientNet, and ResNet50) to predict OAC versus non-OAC (binary classification task) from the input images. Further, four oral and maxillofacial experts evaluated the respective panoramic radiography and determined performance metrics (accuracy, area under the curve (AUC), precision, recall, F1-score, and receiver operating characteristics curve) of all diagnostic approaches. Cohen’s kappa was used to evaluate the agreement between expert evaluations. The deep learning algorithms reached high specificity (highest specificity 100% for InceptionV3) but low sensitivity (highest sensitivity 42.86% for MobileNetV2). The AUCs from VGG16, InceptionV3, MobileNetV2, EfficientNet, and ResNet50 were 0.53, 0.60, 0.67, 0.51, and 0.56, respectively. Expert 1–4 reached an AUC of 0.550, 0.629, 0.500, and 0.579, respectively. The specificity of the expert evaluations ranged from 51.74% to 95.02%, whereas sensitivity ranged from 14.14% to 59.60%. Cohen’s kappa revealed a poor agreement for the oral and maxillofacial expert evaluations (Cohen’s kappa: 0.1285). Overall, present data indicate that OAC cannot be sufficiently predicted from preoperative panoramic radiography. The false-negative rate, i.e., the rate of positive cases (OAC) missed by the deep learning algorithms, ranged from 57.14% to 95.24%. Surgeons should not solely rely on panoramic radiography when evaluating the probability of OAC occurrence. Clinical testing of OAC is warranted after each upper-molar tooth extraction.