Emulating Clinical Diagnostic Reasoning for Jaw Cysts with Machine Learning

Artificial Intelligence Methods in the Detection of Oral Diseases on Pantomographic Images-A Systematic Narrative Review

Background: Artificial intelligence (AI) is playing an increasingly important role in everyday dental practice and diagnosis, especially in the area of analysing digital pantomographic images. Through the use of innovative and modern algorithms, clinicians can more quickly and accurately identify pathological changes contained in digital pantomographic images, such as caries, periapical lesions, cysts, and tumours. It should be noted that pantomographic images are one of the most commonly used imaging modalities in dentistry, and their digital analysis enables the construction of AI models to support diagnosis. Objectives: This paper presents a systematic narrative review of studies included in scientific articles from 2020 to 2025, focusing on three main diagnostic areas: detection of caries, periapical lesions, and cysts and tumours. The results show that neural network models, such as U-Net, Swin Transformer, and CNN, are most commonly used in caries diagnosis and have achieved high performance in lesion identification. In the case of periapical lesions, AI models such as U-Net and Decision Tree also showed high performance, surpassing the performance of young dentists in assessing radiographs in some cases. Results: The studies cited in this review show that the diagnosis of cysts and tumours, on the other hand, relies on more advanced models such as YOLO v8, DCNN, and EfficientDet, which in many cases achieved more than 95% accuracy in the detection of this pathology. The cited studies were conducted at various universities and institutions around the world, and the databases (case databases) analysed in this work ranged from tens to thousands of images. Conclusions: The main conclusion of the literature analysis is that, thanks to its accessibility, speed, and accuracy, AI can significantly assist the work of physicians by reducing the time needed to analyse images. However, despite the promising results, AI should only be considered as an enabling tool and not as a replacement for the knowledge of doctors and their long experience. There is still a need for further improvement of algorithms and further training of the network, especially in identifying more complex clinical cases.

Forensic sex classification by convolutional neural network approach by VGG16 model: accuracy, precision and sensitivity

INTRODUCTION

In the reconstructive phase of medico-legal human identification, the sex estimation is crucial in the reconstruction of the biological profile and can be applied both in identifying victims of mass disasters and in the autopsy room. Due to the inherent subjectivity associated with traditional methods, artificial intelligence, specifically, convolutional neural networks (CNN) may present a competitive option.

OBJECTIVES

This study evaluates the reliability of VGG16 model as an accurate forensic sex prediction algorithm and its performance using orthopantomography (OPGs).

MATERIALS AND METHODS

This study included 1050 OPGs from patients at the Santa Maria Local Health Unit Stomatology Department. Using Python, the OPGs were pre-processed, resized and similar copies were created using data augmentation methods. The model was evaluated for precision, sensitivity, F1-score and accuracy, and heatmaps were created.

RESULTS AND DISCUSSION

The training revealed a discrepancy between the validation and training loss values. In the general test, the model showed a general balance between sexes, with F1-scores of 0.89. In the test by age group, contrary to expectations, the model was most accurate in the 16-20 age group (90%). Apart from the mandibular symphysis, analysis of the heatmaps showed that the model did not focus on anatomically relevant areas, possibly due to the lack of application of image extraction techniques.

CONCLUSIONS

The results indicate that CNNs are accurate in classifying human remains based on the generic factor sex for medico-legal identification, achieving an overall accuracy of 89%. However, further research is necessary to enhance the models' performance.

Artificial Intelligence Models Accuracy for Odontogenic Keratocyst Detection From Panoramic View Radiographs: A Systematic Review and Meta-Analysis

Background and Aims

Odontogenic keratocyst (OKC) is a radiolucent jaw lesion often mistaken for similar conditions like ameloblastomas on panoramic radiographs. Accurate diagnosis is vital for effective management, but manual image interpretation can be inconsistent. While deep learning algorithms in AI have shown promise in improving diagnostic accuracy for OKCs, their performance across studies is still unclear. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of AI models in detecting OKC from panoramic radiographs.

Methods

A systematic search was performed across 5 databases. Studies were included if they examined the PICO question of whether AI models (I) could improve the diagnostic accuracy (O) of OKC in panoramic radiographs (P) compared to reference standards (C). Key performance metrics including sensitivity, specificity, accuracy, and area under the curve (AUC) were extracted and pooled using random-effects models. Meta-regression and subgroup analyses were conducted to identify sources of heterogeneity. Publication bias was evaluated through funnel plots and Egger's test.

Results

Eight studies were included in the meta-analysis. The pooled sensitivity across all studies was 83.66% (95% CI:73.75%-93.57%) and specificity was 82.89% (95% CI:70.31%-95.47%). YOLO-based models demonstrated superior diagnostic performance with a sensitivity of 96.4% and specificity of 96.0%, compared to other architectures. Meta-regression analysis indicated that model architecture was a significant predictor of diagnostic performance, accounting for a significant portion of the observed heterogeneity. However, the analysis also revealed publication bias and high variability across studies (Egger's test, p = 0.042).

Conclusion

AI models, particularly YOLO-based architectures, can improve the diagnostic accuracy of OKCs in panoramic radiographs. While AI shows strong capabilities in simple cases, it should complement, not replace, human expertise, especially in complex situations.

The current landscape of artificial intelligence in oral and maxillofacial surgery- a narrative review

OBJECTIVE

This narrative review aims to explore the current applications and future prospects of AI within the subfields of oral and maxillofacial surgery (OMS), emphasizing its potential benefits and anticipated challenges.

METHODS

A detailed review of the literature was conducted to evaluate the role of AI in oral and maxillofacial surgery. All domains within OMS were reviewed with a focus on diagnostic, therapeutic and prognostic interventions.

RESULTS

AI has been successfully integrated into surgical specialties to enhance clinical outcomes. In OMS, AI demonstrates potential to improve clinical and administrative workflows in both ambulatory and hospital-based settings. Notable applications include more accurate risk prediction, minimally invasive surgical techniques, and optimized postoperative management.

CONCLUSION

OMS stands to benefit enormously from the integration of AI. However, significant roadblocks, such as ethical concerns, data security, and integration challenges, must be addressed to ensure effective adoption. Further research and innovation are needed to fully realize the potential of AI in this specialty.

Prevalence and root canal morphology of taurodontism analyzed by cone-beam computed tomography in Northern China

BACKGROUND

To evaluate the prevalence and characteristics of taurodontism in northern China by using cone-beam computed tomography (CBCT) and assisting the treatment.

METHODS

The study involved CBCT scans of 8112 teeth from 507 participants of northern China, comprising 217 males and 290 females aged 18 to 60. Analysis was conducted using Shifman and Chanannel's criteria to assess the prevalence and attributes of taurodontism, examining differences based on tooth position (maxilla and mandible) as well as gender (P < 0.05). Specific morphology including C-shaped canal was recorded. The curvature of the canals was measured using a modified Schneider method. Moreover, we reported two failure cases with taurodontism referred to a retreatment.

RESULTS

Taurodontism was observed in 113 participants, affecting 23.50% of the males and 21.38% of the females in at least one tooth (P > 0.05). The prevalence was significant higher in maxilla (P < 0.05). The highest incidence of taurodontism was detected in premolars, 9.86% in the maxillary first premolars, and in molars, with 3.94% in the maxillary first molars. Regarding canal curvature, a higher incidence of curved canal could be found in maxillary and mandibular molars of taurodontism (47.50-66.67%), alongside an elevated prevalence of C-shaped taurodontism in mandibular second molars (71.43%). After follow-up, the healing response was satisfactory in both cases.

CONCLUSION

The study highlighted a higher incidence of taurodontism in maxilla, indicating a significant association between taurodontism, C-shaped characteristics, and canal curvature. Dentists are advised to carefully consider the presence of special morphologies during taurodontism treatment.

Physician-dentist dual referral model concept for coordinated bone anabolic therapy

BACKGROUND

Bone anabolic drugs used for the pharmacologic treatment of osteoporosis have the potential to enhance alveolar bone regeneration to improve implant success. There are no US Food and Drug Administration-approved drugs indicated to improve oral bone density around teeth or implants.

METHODS

The authors summarized expert opinions on a novel coordinated treatment approach leveraging the effects of systemic bone anabolic drugs to enhance dental implant therapy in patients with osteoporosis and a dual referral model for physicians and dentists to address the clinical needs of patients with osteoporosis from a comprehensive perspective of oral-systemic health. Interviews of key opinion leaders were conducted with a bone health specialist group consisting of specialists in orthopedic surgery, internal medicine, geriatrics, endocrinology, and clinical densitometry and a surgical dental specialist group consisting of periodontists and oral surgeons.

RESULTS

Overall, both groups shared positive feedback on the idea of strategically timing administration of anabolic osteoporosis drugs with dental treatment. Both groups expressed interest in the dual referral model.

CONCLUSIONS

The feedback of key opinion leaders supported the coordinated bone anabolic therapy concept and identified a need for improved interdisciplinary collaboration, education, and communication to realize the synergies of physician-dentist clinical cooperation.

PRACTICAL IMPLICATIONS

Strategic timing of osteoporosis therapy could improve skeletal bone health and reduce fracture risk while offering adjunctive dental benefits.

Artificial intelligence and radiomics in the diagnosis of intraosseous lesions of the gnathic bones: A systematic review

BACKGROUND

The purpose of this systematic review (SR) is to gather evidence on the use of machine learning (ML) models in the diagnosis of intraosseous lesions in gnathic bones and to analyze the reliability, impact, and usefulness of such models. This SR was performed in accordance with the PRISMA 2022 guidelines and was registered in the PROSPERO database (CRD42022379298).

METHODS

The acronym PICOS was used to structure the inquiry-focused review question "Is Artificial Intelligence reliable for the diagnosis of intraosseous lesions in gnathic bones?" The literature search was conducted in various electronic databases, including PubMed, Embase, Scopus, Cochrane Library, Web of Science, Lilacs, IEEE Xplore, and Gray Literature (Google Scholar and ProQuest). Risk of bias assessment was performed using PROBAST, and the results were synthesized by considering the task and sampling strategy of the dataset.

RESULTS

Twenty-six studies were included (21 146 radiographic images). Ameloblastomas, odontogenic keratocysts, dentigerous cysts, and periapical cysts were the most frequently investigated lesions. According to TRIPOD, most studies were classified as type 2 (randomly divided). The F1 score was presented in only 13 studies, which provided the metrics for 20 trials, with a mean of 0.71 (±0.25).

CONCLUSION

There is no conclusive evidence to support the usefulness of ML-based models in the detection, segmentation, and classification of intraosseous lesions in gnathic bones for routine clinical application. The lack of detail about data sampling, the lack of a comprehensive set of metrics for training and validation, and the absence of external testing limit experiments and hinder proper evaluation of model performance.

Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches

Artificial intelligence (AI) is increasingly applied across all disciplines of medicine, including dentistry. Oral health research is experiencing a rapidly increasing use of machine learning (ML), the branch of AI that identifies inherent patterns in data similarly to how humans learn. In contemporary clinical dentistry, ML supports computer-aided diagnostics, risk stratification, individual risk prediction, and decision support to ultimately improve clinical oral health care efficiency, outcomes, and reduce disparities. Further, ML is progressively used in dental and oral health research, from basic and translational science to clinical investigations. With an ML perspective, this review provides a comprehensive overview of how dental medicine leverages AI for diagnostic, prognostic, and generative tasks. The spectrum of available data modalities in dentistry and their compatibility with various methods of applied AI are presented. Finally, current challenges and limitations as well as future possibilities and considerations for AI application in dental medicine are summarized.

Accuracy of machine learning in the diagnosis of odontogenic cysts and tumors: a systematic review and meta-analysis

BACKGROUND

The recent impact of artificial intelligence in diagnostic services has been enormous. Machine learning tools offer an innovative alternative to diagnose cysts and tumors radiographically that pose certain challenges due to the near similar presentation, anatomical variations, and superimposition. It is crucial that the performance of these models is evaluated for their clinical applicability in diagnosing cysts and tumors.

METHODS

A comprehensive literature search was carried out on eminent databases for published studies between January 2015 and December 2022. Studies utilizing machine learning models in the diagnosis of odontogenic cysts or tumors using Orthopantomograms (OPG) or Cone Beam Computed Tomographic images (CBCT) were included. QUADAS-2 tool was used for the assessment of the risk of bias and applicability concerns. Meta-analysis was performed for studies reporting sufficient performance metrics, separately for OPG and CBCT.

RESULTS

16 studies were included for qualitative synthesis including a total of 10,872 odontogenic cysts and tumors. The sensitivity and specificity of machine learning in diagnosing cysts and tumors through OPG were 0.83 (95% CI 0.81-0.85) and 0.82 (95% CI 0.81-0.83) respectively. Studies utilizing CBCT noted a sensitivity of 0.88 (95% CI 0.87-0.88) and specificity of 0.88 (95% CI 0.87-0.89). Highest classification accuracy was 100%, noted for Support Vector Machine classifier.

CONCLUSION

The results from the present review favoured machine learning models to be used as a clinical adjunct in the radiographic diagnosis of odontogenic cysts and tumors, provided they undergo robust training with a huge dataset. However, the arduous process, investment, and certain ethical concerns associated with the total dependence on technology must be taken into account. Standardized reporting of outcomes for diagnostic studies utilizing machine learning methods is recommended to ensure homogeneity in assessment criteria, facilitate comparison between different studies, and promote transparency in research findings.

Deep learning in the diagnosis for cystic lesions of the jaws: a review of recent progress

Cystic lesions of the gnathic bones present challenges in differential diagnosis. In recent years, artificial intelligence (AI) represented by deep learning (DL) has rapidly developed and emerged in the field of dental and maxillofacial radiology (DMFR). Dental radiography provides a rich resource for the study of diagnostic analysis methods for cystic lesions of the jaws and has attracted many researchers. The aim of the current study was to investigate the diagnostic performance of DL for cystic lesions of the jaws. Online searches were done on Google Scholar, PubMed, and IEEE Xplore databases, up to September 2023, with subsequent manual screening for confirmation. The initial search yielded 1862 titles, and 44 studies were ultimately included. All studies used DL methods or tools for the identification of a variable number of maxillofacial cysts. The performance of algorithms with different models varies. Although most of the reviewed studies demonstrated that DL methods have better discriminative performance than clinicians, further development is still needed before routine clinical implementation due to several challenges and limitations such as lack of model interpretability, multicentre data validation, etc. Considering the current limitations and challenges, future studies for the differential diagnosis of cystic lesions of the jaws should follow actual clinical diagnostic scenarios to coordinate study design and enhance the impact of AI in the diagnosis of oral and maxillofacial diseases.

Odontogenic cystic lesion segmentation on cone-beam CT using an auto-adapting multi-scaled UNet

Objectives

Precise segmentation of Odontogenic Cystic Lesions (OCLs) from dental Cone-Beam Computed Tomography (CBCT) is critical for effective dental diagnosis. Although supervised learning methods have shown practical diagnostic results in segmenting various diseases, their ability to segment OCLs covering different sub-class varieties has not been extensively investigated.

Methods

In this study, we propose a new supervised learning method termed OCL-Net that combines a Multi-Scaled U-Net model, along with an Auto-Adapting mechanism trained with a combined supervised loss. Anonymous CBCT images were collected retrospectively from one hospital. To assess the ability of our model to improve the diagnostic efficiency of maxillofacial surgeons, we conducted a diagnostic assessment where 7 clinicians were included to perform the diagnostic process with and without the assistance of auto-segmentation masks.

Results

We collected 300 anonymous CBCT images which were manually annotated for segmentation masks. Extensive experiments demonstrate the effectiveness of our OCL-Net for CBCT OCLs segmentation, achieving an overall Dice score of 88.84%, an IoU score of 81.23%, and an AUC score of 92.37%. Through our diagnostic assessment, we found that when clinicians were assisted with segmentation labels from OCL-Net, their average diagnostic accuracy increased from 53.21% to 55.71%, while the average time spent significantly decreased from 101s to 47s (P<0.05).

Conclusion

The findings demonstrate the potential of our approach as a robust auto-segmentation system on OCLs in CBCT images, while the segmented masks can be used to further improve OCLs dental diagnostic efficiency.