Predicting recurrent aphthous ulceration using genetic algorithms-optimized neural networks

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.

Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls

Background: Machine learning (ML) is a key component of artificial intelligence (AI). The terms machine learning, artificial intelligence, and deep learning are erroneously used interchangeably as they appear as monolithic nebulous entities. This technology offers immense possibilities and opportunities to advance diagnostics in the field of medicine and dentistry. This necessitates a deep understanding of AI and its essential components, such as machine learning (ML), artificial neural networks (ANN), and deep learning (DP). Aim: This review aims to enlighten clinicians regarding AI and its applications in the diagnosis of oral diseases, along with the prospects and challenges involved. Review results: AI has been used in the diagnosis of various oral diseases, such as dental caries, maxillary sinus diseases, periodontal diseases, salivary gland diseases, TMJ disorders, and oral cancer through clinical data and diagnostic images. Larger data sets would enable AI to predict the occurrence of precancerous conditions. They can aid in population-wide surveillance and decide on referrals to specialists. AI can efficiently detect microfeatures beyond the human eye and augment its predictive power in critical diagnosis. Conclusion: Although studies have recognized the benefit of AI, the use of artificial intelligence and machine learning has not been integrated into routine dentistry. AI is still in the research phase. The coming decade will see immense changes in diagnosis and healthcare built on the back of this research. Clinical significance: This paper reviews the various applications of AI in dentistry and illuminates the shortcomings faced while dealing with AI research and suggests ways to tackle them. Overcoming these pitfalls will aid in integrating AI seamlessly into dentistry.

The association between serum zinc levels and subjective symptoms in zinc deficiency patients with chronic liver disease

This study aimed to analyze the association between serum zinc levels and major subjective symptoms in zinc deficiency patients with chronic liver disease. 578 patients with chronic liver disease were enrolled. The patients, whose serum zinc level of <80 µg/dl, completed a questionnaire to determine whether they had subjective symptoms of the five conditions (taste disorder, aphthous stomatitis, dermatitis, alopecia, and anorexia). Then, the association between these subjective symptoms and serum zinc levels was analyzed. In total, 193 patients (33.4%) experienced any subjective symptoms. The prevalence of each symptom was as follows: 36 patients with taste disorder (6.2%), 46 with aphthous stomatitis (8.0%), 77 with dermatitis (13.3%), 46 with alopecia (8.0%), and 53 with anorexia (9.2%). In total, 70.8%, 34.1%, and 26.1% patients with serum zinc levels of <40, ≥40 to <60, and ≥60 to <80 µg/dl, respectively, had these symptoms. When zinc deficiency was defined as a serum zinc level of <80 µg/dl, approximately one-third of patients displayed symptoms presumably originating from zinc deficiency. As serum zinc levels decreased, the prevalence of these symptoms increased. Dermatitis, especially, was relevant to zinc.

Predicting tooth surface loss using genetic algorithms-optimized artificial neural networks

Our aim was to predict tooth surface loss in individuals without the need to conduct clinical examinations. Artificial neural networks (ANNs) were used to construct a mathematical model. Input data consisted of age, smoker status, type of tooth brush, brushing, and consumption of pickled food, fizzy drinks, orange, apple, lemon, and dried seeds. Output data were the sum of tooth surface loss scores for selected teeth. The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer. The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model. Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%. In conclusion, this study shows that modeling tooth surface loss using ANNs is possible and can be achieved with a high degree of accuracy.

Recurrent aphthous stomatitis

Recurrent aphthous stomatitis (RAS) is the most common ulcerative disease affecting the oral mucosa. RAS occurs mostly in healthy individuals and has an atypical clinical presentation in immunocompromised individuals. The etiology of RAS is still unknown, but several local, systemic, immunologic, genetic, allergic, nutritional, and microbial factors, as well as immunosuppressive drugs, have been proposed as causative agents. Clinical management of RAS using topical and systemic therapies is based on severity of symptoms and the frequency, size, and number of lesions. The goals of therapy are to decrease pain and ulcer size, promote healing, and decrease the frequency of recurrence.