Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks

Research and Application of Deep Learning Models with Multi-Scale Feature Fusion for Lesion Segmentation in Oral Mucosal Diseases

Given the complexity of oral mucosal disease diagnosis and the limitations in the precision of traditional object detection methods, this study aims to develop a high-accuracy artificial intelligence-assisted diagnostic approach based on the SegFormer semantic segmentation model. This method is designed to automatically segment lesion areas in white-light images of oral mucosal diseases, providing objective and quantifiable evidence for clinical diagnosis. This study utilized a dataset of oral mucosal diseases provided by the Affiliated Stomatological Hospital of Zhejiang University School of Medicine, comprising 838 high-resolution images of three diseases: oral lichen planus, oral leukoplakia, and oral submucous fibrosis. These images were annotated at the pixel level by oral specialists using Labelme software (v5.5.0) to construct a semantic segmentation dataset. This study designed a SegFormer model based on the Transformer architecture, employed cross-validation to divide training and testing sets, and compared SegFormer models of different capacities with classical segmentation models such as UNet and DeepLabV3. Quantitative metrics including the Dice coefficient and mIoU were evaluated, and a qualitative visual analysis of the segmentation results was performed to comprehensively assess model performance. The SegFormer-B2 model achieved optimal performance on the test set, with a Dice coefficient of 0.710 and mIoU of 0.786, significantly outperforming other comparative algorithms. The visual results demonstrate that this model could accurately segment the lesion areas of three common oral mucosal diseases. The SegFormer model proposed in this study effectively achieves the precise automatic segmentation of three common oral mucosal diseases, providing a reliable auxiliary tool for clinical diagnosis. It shows promising prospects in improving the efficiency and accuracy of oral mucosal disease diagnosis and has potential clinical application value.

AI in oral medicine: is the future already here? A literature review

Objective Artificial intelligence (AI) is reshaping many healthcare disciplines, mainly with newly developed computer systems or machines that have the ability to mimic human intelligence. This paper aims to review the available evidence on the applications of AI in oral medicine. The review critically assesses current evidence, shedding light on AI's growing role in this field.Methods Around 20 applicable studies were included in this review from different databases like PubMed and Google Scholar. Studies included involved original research articles, mini-reviews, systematic reviews and meta-analyses.Results Existing papers on AI uses in oral medicine included fundamental areas such as oral cancer, lichen planus, bisphosphonate-related osteonecrosis of the jaw, odontogenic keratocysts and oral lesions classification. AI has proved remarkable potential in terms of accuracy, sensitivity and specificity.Conclusion The outcomes of the papers suggest that AI holds major potential to help dental practitioners diagnose and manage oral diseases with superior precision. While acknowledging the encouraging results, this paper also underscores the necessity for further research and improvement to fully harness the abilities of AI in oral medicine. It calls notice to the fact that AI, although a valued tool, should supplement rather than replace healthcare professionals.

Photobiomodulation Therapy in the Management of Oral Lichen Planus: A Systematic Review and Meta-Analysis

Photobiomodulation therapy (PBMT) is a non-invasive and the latest form of therapy used in the treatment of non oncological diseases as well as cancers of various types and locations. The aim of this study was to systematically review and assess the efficacy of PBMT in managing oral lichen planus (OLP) compared to the interventions. A systematic review and meta-analysis were implemented according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. An electronic search using PubMed, Scopus, and Cochrane was conducted to retrieve relevant studies published until June 2023. The outcomes evaluated included the reduction in pain score and clinical severity scores (Prospero No CRD42023428626). A total of eight studies were identified for qualitative synthesis. The pooled analysis incorporating six studies revealed that there are no significant differences for both mean pain score (mean difference [MD] = 0.21, 95% confidence interval [CI] = -0.51, 0.93) as well as clinical score (MD = -0.08, 95% CI = -0.4, 0.25) between PBMT and comparison groups. Subgroup analysis based on corticosteroids as controls showed that there was no significant difference in mean reduction in pain score between PBMT and topical steroids (MD = 0.38, 95% CI = -0.54, 1.31). PBMT is as effective as other interventions in the treatment of OLP, though not superior, and can be a promising alternative treatment for cases resistant to steroids or when steroids are contraindicated. Further studies are recommended to standardize the optimal settings for the treatment of OLP.

Classification-Predictive Model Based on Artificial Neural Network Validated by Histopathology and Direct Immunofluorescence for the Diagnosis of Oral Lichen Planus

The diagnosis of oral lichen planus (OLP) poses many challenges due to its nonspecific clinical symptoms and histopathological features. Therefore, the diagnostic process should include a thorough clinical history, immunological tests, and histopathology. Our study aimed to enhance the diagnostic accuracy of OLP by integrating direct immunofluorescence (DIF) results with clinical data to develop a multivariate predictive model based on the Artificial Neural Network. Eighty patients were assessed using DIF for various markers (immunoglobulins of classes G, A, and M; complement 3; fibrinogen type 1 and 2) and clinical characteristics such as age, gender, and lesion location. Statistical analysis was performed using machine learning techniques in Statistica 13. The following variables were assessed: gender, age on the day of lesion onset, results of direct immunofluorescence, location of white patches, locations of erosions, treatment history, medications and dietary supplement intake, dental status, smoking status, flossing, and using mouthwash. Four statistically significant variables were selected for machine learning after the initial assessment. The final predictive model, based on neural networks, achieved 85% in the testing sample and 71% accuracy in the validation sample. Significant predictors included stress at onset, white patches under the tongue, and erosions on the mandibular gingiva. In conclusion, while the model shows promise, larger datasets and more comprehensive variables are needed to improve diagnostic accuracy for OLP, highlighting the need for further research and collaborative data collection efforts.

Advancements in diagnosing oral potentially malignant disorders: leveraging Vision transformers for multi-class detection

OBJECTIVES

Diagnosing oral potentially malignant disorders (OPMD) is critical to prevent oral cancer. This study aims to automatically detect and classify the most common pre-malignant oral lesions, such as leukoplakia and oral lichen planus (OLP), and distinguish them from oral squamous cell carcinomas (OSCC) and healthy oral mucosa on clinical photographs using vision transformers.

METHODS

4,161 photographs of healthy mucosa, leukoplakia, OLP, and OSCC were included. Findings were annotated pixel-wise and reviewed by three clinicians. The photographs were divided into 3,337 for training and validation and 824 for testing. The training and validation images were further divided into five folds with stratification. A Mask R-CNN with a Swin Transformer was trained five times with cross-validation, and the held-out test split was used to evaluate the model performance. The precision, F1-score, sensitivity, specificity, and accuracy were calculated. The area under the receiver operating characteristics curve (AUC) and the confusion matrix of the most effective model were presented.

RESULTS

The detection of OSCC with the employed model yielded an F1 of 0.852 and AUC of 0.974. The detection of OLP had an F1 of 0.825 and AUC of 0.948. For leukoplakia the F1 was 0.796 and the AUC was 0.938.

CONCLUSIONS

OSCC were effectively detected with the employed model, whereas the detection of OLP and leukoplakia was moderately effective.

CLINICAL RELEVANCE

Oral cancer is often detected in advanced stages. The demonstrated technology may support the detection and observation of OPMD to lower the disease burden and identify malignant oral cavity lesions earlier.

Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases

Immuno-correlated dermatological pathologies refer to skin disorders that are closely associated with immune system dysfunction or abnormal immune responses. Advancements in the field of artificial intelligence (AI) have shown promise in enhancing the diagnosis, management, and assessment of immuno-correlated dermatological pathologies. This intersection of dermatology and immunology plays a pivotal role in comprehending and addressing complex skin disorders with immune system involvement. The paper explores the knowledge known so far and the evolution and achievements of AI in diagnosis; discusses segmentation and the classification of medical images; and reviews existing challenges, in immunological-related skin diseases. From our review, the role of AI has emerged, especially in the analysis of images for both diagnostic and severity assessment purposes. Furthermore, the possibility of predicting patients' response to therapies is emerging, in order to create tailored therapies.

Artificial intelligence for classification and detection of oral mucosa lesions on photographs: a systematic review and meta-analysis

OBJECTIVE

This study aimed to review and synthesize studies using artificial intelligence (AI) for classifying, detecting, or segmenting oral mucosal lesions on photographs.

MATERIALS AND METHOD

Inclusion criteria were (1) studies employing AI to (2) classify, detect, or segment oral mucosa lesions, (3) on oral photographs of human subjects. Included studies were assessed for risk of bias using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A PubMed, Scopus, Embase, Web of Science, IEEE, arXiv, medRxiv, and grey literature (Google Scholar) search was conducted until June 2023, without language limitation.

RESULTS

After initial searching, 36 eligible studies (from 8734 identified records) were included. Based on QUADAS-2, only 7% of studies were at low risk of bias for all domains. Studies employed different AI models and reported a wide range of outcomes and metrics. The accuracy of AI for detecting oral mucosal lesions ranged from 74 to 100%, while that for clinicians un-aided by AI ranged from 61 to 98%. Pooled diagnostic odds ratio for studies which evaluated AI for diagnosing or discriminating potentially malignant lesions was 155 (95% confidence interval 23-1019), while that for cancerous lesions was 114 (59-221).

CONCLUSIONS

AI may assist in oral mucosa lesion screening while the expected accuracy gains or further health benefits remain unclear so far.

CLINICAL RELEVANCE

Artificial intelligence assists oral mucosa lesion screening and may foster more targeted testing and referral in the hands of non-specialist providers, for example. So far, it remains unclear if accuracy gains compared with specialized can be realized.

Predicting Chronic Hyperplastic Candidiasis Retro-Angular Mucosa Using Machine Learning

Chronic hyperplastic candidiasis (CHC) presents a distinctive and relatively rare form of oral candidal infection characterized by the presence of white or white-red patches on the oral mucosa. Often mistaken for leukoplakia or erythroleukoplakia due to their appearance, these lesions display nonhomogeneous textures featuring combinations of white and red hyperplastic or nodular surfaces. Predominant locations for such lesions include the tongue, retro-angular mucosa, and buccal mucosa. This paper aims to investigate the potential influence of specific anatomical locations, retro-angular mucosa, on the development and occurrence of CHC. By examining the relationship between risk factors, we present an approach based on machine learning (ML) to predict the location of CHC occurrence. In this way, we employ Gradient Boosting Regression (GBR) to classify CHC lesion locations based on important risk factors. This estimator can serve both research and diagnostic purposes effectively. The findings underscore that the proposed ML technique can be used to predict the occurrence of CHC in retro-angular mucosa compared to other locations. The results also show a high rate of accuracy in predicting lesion locations. Performance assessment relies on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R2), and Mean Absolute Error (MAE), consistently revealing favorable results that underscore the robustness and dependability of our classification method. Our research contributes valuable insights to the field, enhancing diagnostic accuracy and informing treatment strategies.

Contemporary Role and Applications of Artificial Intelligence in Dentistry

Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.

Artificial Intelligence's Use in the Diagnosis of Mouth Ulcers: A Systematic Review

Artificial intelligence (AI) has been cited as being helpful in the diagnosis of diseases, the prediction of prognoses, and the development of patient-specific therapeutic strategies. AI can help dentists, in particular, when they need to make important judgments quickly. It can eliminate human mistakes in making decisions, resulting in superior and consistent medical treatment while lowering the workload on dentists. The existing studies relevant to the study and application of AI in the diagnosis of various forms of mouth ulcers are reviewed in this work. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards were followed in the preparation of the review. There were no rule violations, with the significant exception of the use of a better search method that led to more accurate findings. Using search terms mainly such as AI, oral health, oral ulcers, oral herpes simplex, oral lichen planus, pemphigus vulgaris, recurrent aphthous ulcer (RAU), oral cancer, premalignant and malignant disorders, etc., a comprehensive search was carried out in the reliable sources of literature, namely PubMed, Scopus, Embase, Web of Science, Ovid, Global Health, and PsycINFO. For all papers, exhaustive searches were done using inclusion criteria as well as exclusion criteria between June 28, 2018, and June 28, 2023. An AI framework for the automatic categorization of oral ulcers from oral clinical photographs was developed by the authors, and it performed satisfactorily. The newly designed AI model works better than the current convolutional neural network image categorization techniques and shows a fair level of precision in the classification of oral ulcers. However, despite being useful for identifying oral ulcers, the suggested technique needs a broader set of data for validation and training purposes before being used in clinical settings. Automated OCSCC identification using a deep learning-based technique is a quick, harmless, affordable, and practical approach to evaluating the effectiveness of cancer treatment. The categorization and identification of RAU lesions through the use of non-intrusive oral pictures using the previously developed ResNet50 and YOLOV algorithms demonstrated better accuracy as well as adequate potential for the future, which could be helpful in clinical practice. Moreover, the most reliable projections for the likelihood of the presence or absence of RAU were made by the optimized neural network. The authors also discovered variables associated with RAU that might be used as input information to build artificial neural networks that anticipate RAU.