Evaluation of an Artificial Intelligence web-based software to detect and classify dental structures and treatments in panoramic radiographs

Automating Dental Condition Detection on Panoramic Radiographs: Challenges, Pitfalls, and Opportunities

Background/Objectives: The integration of AI into dentistry holds promise for improving diagnostic workflows, particularly in the detection of dental pathologies and pre-radiotherapy screening for head and neck cancer patients. This study aimed to develop and validate an AI model for detecting various dental conditions, with a focus on identifying teeth at risk prior to radiotherapy. Methods: A YOLOv8 model was trained on a dataset of 1628 annotated panoramic radiographs and externally validated on 180 radiographs from multiple centers. The model was designed to detect a variety of dental conditions, including periapical lesions, impacted teeth, root fragments, prosthetic restorations, and orthodontic devices. Results: The model showed strong performance in detecting implants, endodontic treatments, and surgical devices, with precision and recall values exceeding 0.8 for several conditions. However, performance declined during external validation, highlighting the need for improvements in generalizability. Conclusions: YOLOv8 demonstrated robust detection capabilities for several dental conditions, especially in training data. However, further refinement is needed to enhance generalizability in external datasets and improve performance for conditions like periapical lesions and bone loss.

Clinical Validation of Deep Learning for Segmentation of Multiple Dental Features in Periapical Radiographs

Periapical radiographs are routinely used in dental practice for diagnosis and treatment planning purposes. However, they often suffer from artifacts, distortions, and superimpositions, which can lead to potential misinterpretations. Thus, an automated detection system is required to overcome these challenges. Artificial intelligence (AI) has been revolutionizing various fields, including medicine and dentistry, by facilitating the development of intelligent systems that can aid in performing complex tasks such as diagnosis and treatment planning. The purpose of the present study was to verify the diagnostic performance of an AI system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on periapical radiographs. A dataset comprising 1000 periapical radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists. A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.7-0.73), implants (0.97-0.98), restored teeth (0.85-0.89), teeth with fixed prosthesis (0.92-0.94), and missing teeth (0.82-0.85). The automatic detection by the AI system was comparable to the oral radiologists and may be useful for automatic identification in periapical radiographs.

Automatic feature segmentation in dental panoramic radiographs

OBJECTIVE

The purpose of the present study was to verify the diagnostic performance of an AI system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on panoramic radiography.

METHODS

This is a cross-sectional study. A dataset comprising 1000 panoramic radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists.

RESULTS

A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.691-0.878), implants (0.770-0.952), restored teeth (0.773-0.834), teeth with fixed prostheses (0.972-0.980), and missing teeth (0.956-0.988).

DISCUSSION

Panoramic radiographs are commonly used for diagnosis and treatment planning. However, they often suffer from artifacts, distortions, and superimpositions, leading to potential misinterpretations. Thus, an automated detection system is required to tackle these challenges. Artificial intelligence (AI) has revolutionized various fields, including dentistry, by enabling the development of intelligent systems that can assist in complex tasks such as diagnosis and treatment planning.

CONCLUSION

The automatic detection by the AI system was comparable to oral radiologists and may be useful for automatic identifications in panoramic radiographs. These findings signify the potential for AI systems to enhance diagnostic accuracy and efficiency in dental practices, potentially reducing the likelihood of diagnostic errors caused by unexperienced professionals.

Deep learning in the overall process of implant prosthodontics: A state-of-the-art review

Artificial intelligence represented by deep learning has attracted attention in the field of dental implant restoration. It is widely used in surgical image analysis, implant plan design, prosthesis shape design, and prognosis judgment. This article mainly describes the research progress of deep learning in the whole process of dental implant prosthodontics. It analyzes the limitations of current research, and looks forward to the future development direction.

Identification of Root Canal Morphology in Fused-rooted Mandibular Second Molars From X-ray Images Based on Deep Learning

INTRODUCTION

Understanding the intricate anatomical morphology of fused-rooted mandibular second molars (MSMs) is essential for root canal treatment. The present study utilized a deep learning approach to identify the three-dimensional root canal morphology of MSMs from two-dimensional X-ray images.

METHODS

A total of 271 fused-rooted MSMs were included in the study. Micro-computed tomography reconstruction images and two-dimensional X-ray projection images were obtained. The ground truth of three-dimensional root canal morphology was determined through micro-computed tomography images, which were classified into merging, symmetrical, and asymmetrical types. To amplify the X-ray image dataset, traditional augmentation techniques from the python package Augmentor and a multiangle projection method were employed. Identification of root canal morphology was conducted using the pretrained VGG19, ResNet18, ResNet50, and EfficientNet-b5 on X-ray images. The classification results from convolutional neural networks (CNNs) were then compared with those performed by endodontic residents.

RESULTS

The multiangle projection augmentation method outperformed the traditional approach in all CNNs except for EfficientNet-b5. ResNet18 combined with the multiangle projection method outperformed all other combinations, with an overall accuracy of 79.25%. In specific classifications, accuracies of 81.13%, 86.79%, and 90.57% were achieved for merging, symmetrical, and asymmetrical types, respectively. Notably, CNNs surpassed endodontic residents in classification performance; the average accuracy for endodontic residents was only 60.38% (P < .05).

CONCLUSIONS

CNNs were more effective than endodontic residents in identifying the three-dimensional root canal morphology of MSMs. The result indicates that CNNs possess the capacity to employ two-dimensional images effectively in aiding three-dimensional diagnoses.

Artificial intelligence for detecting periapical radiolucencies: A systematic review and meta-analysis

OBJECTIVES

Dentists' diagnostic accuracy in detecting periapical radiolucency varies considerably. This systematic review and meta-analysis aimed to investigate the accuracy of artificial intelligence (AI) for detecting periapical radiolucency.

DATA

Studies reporting diagnostic accuracy and utilizing AI for periapical radiolucency detection, published until November 2023, were eligible for inclusion. Meta-analysis was conducted using the online MetaDTA Tool to calculate pooled sensitivity and specificity. Risk of bias was evaluated using QUADAS-2.

SOURCES

A comprehensive search was conducted in PubMed/MEDLINE, ScienceDirect, and Institute of Electrical and Electronics Engineers (IEEE) Xplore databases. Studies reporting diagnostic accuracy and utilizing AI tools for periapical radiolucency detection, published until November 2023, were eligible for inclusion.

STUDY SELECTION

We identified 210 articles, of which 24 met the criteria for inclusion in the review. All but one study used one type of convolutional neural network. The body of evidence comes with an overall unclear to high risk of bias and several applicability concerns. Four of the twenty-four studies were included in a meta-analysis. AI showed a pooled sensitivity and specificity of 0.94 (95 % CI = 0.90-0.96) and 0.96 (95 % CI = 0.91-0.98), respectively.

CONCLUSIONS

AI demonstrated high specificity and sensitivity for detecting periapical radiolucencies. However, the current landscape suggests a need for diverse study designs beyond traditional diagnostic accuracy studies. Prospective real-life randomized controlled trials using heterogeneous data are needed to demonstrate the true value of AI.

CLINICAL SIGNIFICANCE

Artificial intelligence tools seem to have the potential to support detecting periapical radiolucencies on imagery. Notably, nearly all studies did not test fully fledged software systems but measured the mere accuracy of AI models in diagnostic accuracy studies. The true value of currently available AI-based software for lesion detection on both 2D and 3D radiographs remains uncertain.

Oral Health Status and Treatment Needs Based on Artificial Intelligence (AI) Dental Panoramic Radiograph (DPR) Analysis: A Cross-Sectional Study

Background: The application of artificial intelligence (AI) is gaining popularity in modern dentistry. AI has been successfully used to interpret dental panoramic radiographs (DPRs) and quickly screen large groups of patients. This cross-sectional study aimed to perform a population-based assessment of the oral health status and treatment needs of the residents of Kielce, Poland, and the surrounding area based on DPR analysis performed by a high-accuracy AI algorithm trained with over 250,000 radiographs. Methods: This study included adults who had a panoramic radiograph performed, regardless of indications. The following diagnoses were used for analysis: (1) dental caries, (2) missing tooth, (3) dental filling, (4) root canal filling, (5) endodontic lesion, (6) implant, (7) implant abutment crown, (8) pontic crown, (9) dental abutment crown, and (10) sound tooth. The study sample included 980 subjects. Results: The patients had an average of 15 sound teeth, with the domination of the lower dental arch over the upper one. The most commonly identified pathology was dental caries, which affected 99% of participants. A total of 67% of patients underwent root canal treatment. Every fifth endodontically treated tooth presented a periapical lesion. Of study group members, 82% lost at least one tooth. Pontics were identified more often (9%) than implants (2%) in replacing missing teeth. Conclusions: DPR assessment by AI has proven to be an efficient method for population analysis. Despite recent improvements in the oral health status of Polish residents, its level is still unsatisfactory and suggests the need to improve oral health. However, due to some limitations of this study, the results should be interpreted with caution.

Performance evaluation of three versions of a convolutional neural network for object detection and segmentation using a multiclass and reduced panoramic radiograph dataset

OBJECTIVES

To evaluate the diagnostic performance of three versions of a deep-learning convolutional neural network in terms of object detection and segmentation using a multiclass panoramic radiograph dataset.

METHODS

A total of 600 orthopantomographies were randomly selected for this study and manually annotated by a single operator using an image annotation tool (COCO Annotator v.11.0.1) to establish ground truth. The annotation classes included teeth, maxilla, mandible, inferior alveolar nerve, dento- and implant-supported crowns/pontics, endodontic treatment, resin-based restorations, metallic restorations, and implants. The dataset was then divided into training, validation, and testing subsets, which were used to train versions 5, 7, and 8 of You Only Look Once (YOLO) Neural Network. Results were stored, and a posterior performance analysis was carried out by calculating the precision (P), recall (R), F1 Score, Intersection over Union (IoU), and mean average precision (mAP) at 0.5 and 0.5-0.95 thresholds. The confusion matrix and recall precision graphs were also sketched.

RESULTS

YOLOv5s showed an improvement in object detection results with an average R = 0.634, P = 0.781, mAP0.5 = 0.631, and mAP0.5-0.95 = 0.392. YOLOv7m achieved the best object detection results with average R = 0.793, P = 0.779, mAP0.5 = 0.740, and mAP0.5-0.95 = 0,481. For object segmentation, YOLOv8m obtained the best average results (R = 0.589, P = 0.755, mAP0.5 = 0.591, and mAP0.5-0.95 = 0.272).

CONCLUSIONS

YOLOv7m was better suited for object detection, while YOLOv8m demonstrated superior performance in object segmentation. The most frequent error in object detection was related to background classification. Conversely, in object segmentation, there is a tendency to misclassify True Positives across different dental treatment categories.

CLINICAL SIGNIFICANCE

General diagnostic and treatment decisions based on panoramic radiographs can be enhanced using new artificial intelligence-based tools. Nevertheless, the reliability of these neural networks should be subjected to training and validation to ensure their generalizability.

Panoramic imaging errors in machine learning model development: a systematic review

OBJECTIVES

To investigate the management of imaging errors from panoramic radiography (PAN) datasets used in the development of machine learning (ML) models.

METHODS

This systematic literature followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and used three databases. Keywords were selected from relevant literature.

ELIGIBILITY CRITERIA

PAN studies that used ML models and mentioned image quality concerns.

RESULTS

Out of 400 articles, 41 papers satisfied the inclusion criteria. All the studies used ML models, with 35 papers using deep learning (DL) models. PAN quality assessment was approached in 3 ways: acknowledgement and acceptance of imaging errors in the ML model, removal of low-quality radiographs from the dataset before building the model, and application of image enhancement methods prior to model development. The criteria for determining PAN image quality varied widely across studies and were prone to bias.

CONCLUSIONS

This study revealed significant inconsistencies in the management of PAN imaging errors in ML research. However, most studies agree that such errors are detrimental when building ML models. More research is needed to understand the impact of low-quality inputs on model performance. Prospective studies may streamline image quality assessment by leveraging DL models, which excel at pattern recognition tasks.

Unexpected metallic foreign bodies on panoramic scans - a narrative review

BACKGROUND

The digital panoramic radiograph (orthopantomogram, OPG) is the standard radiographic technique for basic diagnostics in dental practice. A correctly taken image provides a good overview of teeth and jaw, whereas radiopaque foreign materials, e. g. metal, can obscure relevant findings.

METHODS

A literature review on unexpected metallic foreign bodies in OPG was performed to determine the spectrum of metallic foreign bodies that may cause radiopaque areas on panoramic radiographs in routine clinical use.

RESULTS AND CONCLUSION

A total of 37 different unexpected metallic foreign bodies were found. They can be categorized as jewelry, clothing, personal protective equipment, medical devices, iatrogenic foreign bodies, and rare incidental findings. Radiopaque foreign materials in the OPG are often relatively easy to recognize as artifacts because of their location, and they are avoidable in most cases. If unclear, a three-dimensional radiograph was helpful for determining the location. Radiopaque areas caused by foreign bodies can lead to misinterpretation or partial or complete non-evaluability and should therefore be avoided.

KEY POINTS

· The OPG is the standard radiograph for dentists, oral surgeons, and oral and maxillofacial surgeons.. · Foreign bodies made of metal can lead to non-evaluability of panoramic radiographs. Based on a review of the literature and exemplary radiographs, this article provides an overview of rare but typical metallic foreign bodies in OPG, thus addressing the problem of the subfield of radiography by making radiologists more familiar with these images.. · The spectrum of unexpected metallic foreign bodies includes unremoved earrings with the typical ghost images on the contralateral side, piercings, hearing aids, acupuncture needles, rare iatrogenic foreign bodies, incidental findings in infants in the nose and external auditory canal, vascular clips after surgical interventions, and ritual subcutaneous foreign materials..

CITATION FORMAT

· Brauer HU, Bartols A, Hellmann D et al. Unexpected metallic foreign bodies on panoramic scans - a narrative review. Fortschr Röntgenstr 2023; 195: 809 - 818.