Proof-of-Concept Study on an Automatic Computational System in Detecting and Classifying Occlusal Caries Lesions from Smartphone Color Images of Unrestored Extracted Teeth

Detecting dental caries on oral photographs using artificial intelligence: A systematic review

OBJECTIVES

This systematic review aimed at evaluating the performance of artificial intelligence (AI) models in detecting dental caries on oral photographs.

METHODS

Methodological characteristics and performance metrics of clinical studies reporting on deep learning and other machine learning algorithms were assessed. The risk of bias was evaluated using the quality assessment of diagnostic accuracy studies 2 (QUADAS-2) tool. A systematic search was conducted in EMBASE, Medline, and Scopus.

RESULTS

Out of 3410 identified records, 19 studies were included with six and seven studies having low risk of biases and applicability concerns for all the domains, respectively. Metrics varied widely and were assessed on multiple levels. F1-scores for classification and detection tasks were 68.3%-94.3% and 42.8%-95.4%, respectively. Irrespective of the task, F1-scores were 68.3%-95.4% for professional cameras, 78.8%-87.6%, for intraoral cameras, and 42.8%-80% for smartphone cameras. Limited studies allowed assessing AI performance for lesions of different severity.

CONCLUSION

Automatic detection of dental caries using AI may provide objective verification of clinicians' diagnoses and facilitate patient-clinician communication and teledentistry. Future studies should consider more robust study designs, employ comparable and standardized metrics, and focus on the severity of caries lesions.

Visual Diagnostics of Dental Caries through Deep Learning of Non-Standardised Photographs Using a Hybrid YOLO Ensemble and Transfer Learning Model

BACKGROUND

Access to oral healthcare is not uniform globally, particularly in rural areas with limited resources, which limits the potential of automated diagnostics and advanced tele-dentistry applications. The use of digital caries detection and progression monitoring through photographic communication, is influenced by multiple variables that are difficult to standardize in such settings. The objective of this study was to develop a novel and cost-effective virtual computer vision AI system to predict dental cavitations from non-standardised photographs with reasonable clinical accuracy.

METHODS

A set of 1703 augmented images was obtained from 233 de-identified teeth specimens. Images were acquired using a consumer smartphone, without any standardised apparatus applied. The study utilised state-of-the-art ensemble modeling, test-time augmentation, and transfer learning processes. The "you only look once" algorithm (YOLO) derivatives, v5s, v5m, v5l, and v5x, were independently evaluated, and an ensemble of the best results was augmented, and transfer learned with ResNet50, ResNet101, VGG16, AlexNet, and DenseNet. The outcomes were evaluated using precision, recall, and mean average precision (mAP).

RESULTS

The YOLO model ensemble achieved a mean average precision (mAP) of 0.732, an accuracy of 0.789, and a recall of 0.701. When transferred to VGG16, the final model demonstrated a diagnostic accuracy of 86.96%, precision of 0.89, and recall of 0.88. This surpassed all other base methods of object detection from free-hand non-standardised smartphone photographs.

CONCLUSION

A virtual computer vision AI system, blending a model ensemble, test-time augmentation, and transferred deep learning processes, was developed to predict dental cavitations from non-standardised photographs with reasonable clinical accuracy. This model can improve access to oral healthcare in rural areas with limited resources, and has the potential to aid in automated diagnostics and advanced tele-dentistry applications.

Artificial intelligence-assisted smartphone-based sensing for bioanalytical applications: A review

Artificial intelligence (AI) has received great attention since the concept was proposed, and it has developed rapidly in recent years with applications in many fields. Meanwhile, newer iterations of smartphone hardware technologies which have excellent data processing capabilities have leveraged on AI capabilities. Based on the desirability for portable detection, researchers have been investigating intelligent analysis by combining smartphones with AI algorithms. Various examples of the application of AI algorithm-based smartphone detection and analysis have been developed. In this review, we give an overview of this field, with a particular focus on bioanalytical detection applications. The applications are presented in terms of hardware design, software algorithms, and specific application areas. We also discuss the existing limitations of AI-based smartphone detection and analytical approaches, and their future prospects. The take-home message of our review is that the application of AI in the field of detection analysis is restricted by the limitations of the smartphone's hardware as well as the model building of AI for detection targets with insufficient data. Nevertheless, at this juncture, while bioanalytical diagnostics and health monitoring have set the pace for AI-based smartphone applicability, the future should see the technology making greater inroads into other fields. In relation to the latter, it is likely that the ordinary or average person will play a greater participatory role.

Evaluation of the marginal adaptation and gingival status of full-crown restorations using an intraoral camera

Objective

The purpose of this study was to compare the usefulness of intraoral photographs, acquired with a household intraoral camera operating in conventional, calibrated, and polarized modes, with clinical examinations for assessing the marginal adaptation and gingival status of full-crown restorations.

Methods

Clinical examinations were performed by a prosthodontist who classified the marginal adaptation of full-crown restorations according to FDI World Dental Federation criteria, and a periodontal expert who classified gingival status according to the Modified Gingival Index (MGI). The margins and gingival status of the conventional, calibration, and polarization groups of full-crown restorations were independently assessed by three evaluators who obtained photographs using an intraoral camera. Cases where at least two of three assessors were in agreement were analyzed using Cohen’s kappa coefficient and the chi-square test, and the sensitivity and specificity were calculated.

Results

The conventional, calibration, and polarization groups differed significantly in marginal and gingival status of full-crown restorations. In the calibration group, there was good agreement between the camera-based and oral clinical examinations in terms of the gingival status of full-crown restorations (kappa = 0.945), with 100% sensitivity and 91.67% specificity; this was also the case in the polarization group with respect to the margins of full-crown restorations (kappa = 0.917, sensitivity = 97.22%, specificity = 94.44%).

Conclusions

An intraoral camera with black and white calibrated images is useful to assess the gingival status of full-crown restorations. Polarization mode can be used to assess the marginal adaptation of full-crown restorations. The camera is a feasible and valid diagnostic aid.

Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)—A Systematic Review

Evolution in the fields of science and technology has led to the development of newer applications based on Artificial Intelligence (AI) technology that have been widely used in medical sciences. AI-technology has been employed in a wide range of applications related to the diagnosis of oral diseases that have demonstrated phenomenal precision and accuracy in their performance. The aim of this systematic review is to report on the diagnostic accuracy and performance of AI-based models designed for detection, diagnosis, and prediction of dental caries (DC). Eminent electronic databases (PubMed, Google scholar, Scopus, Web of science, Embase, Cochrane, Saudi Digital Library) were searched for relevant articles that were published from January 2000 until February 2022. A total of 34 articles that met the selection criteria were critically analyzed based on QUADAS-2 guidelines. The certainty of the evidence of the included studies was assessed using the GRADE approach. AI has been widely applied for prediction of DC, for detection and diagnosis of DC and for classification of DC. These models have demonstrated excellent performance and can be used in clinical practice for enhancing the diagnostic performance, treatment quality and patient outcome and can also be applied to identify patients with a higher risk of developing DC.