The extent is indicated by the percentage of teeth affected by periodontitis at the identified stage. Grading depends on the risk of disease progression associated with the history of disease progression, local and systemic factors. Despite the introduction of new diagnostic terms for periodontal diseases, dental care providers might not be acquainted with them due to the complexity of this new system. This results in clinical documentation lacking accurate and structured diagnosis, or in some cases, no diagnosis being recorded. Inadequate periodontal diagnoses poses a significant threat to patient care quality. An accurate diagnosis is key to the provision of appropriate patient care, outcome assessment and quality improvement efforts. This, in turn, may hinder future care providers from evaluating the patient's condition precisely and providing optimal treatment. Electronic dental records (EDR) have become widely adopted in dental care, providing an opportunity to address the issue of missing diagnoses. EDRs include comprehensive information on a patient's history, clinical examination, diagnosis, treatment, and prognosis

This study aimed to utilize text processing and natural language processing (NLP) models to mine clinical notes for the diagnosis of periodontitis and to evaluate the performance of a named entity recognition (NER) model on different regular expression (RE) methods. Two complexity levels of RE methods were used to extract and generate the training data. The SpaCy package and RoBERTa transformer models were used to build the NER model and evaluate its performance with the manual-labeled gold standards. The comparison of the RE methods with the gold standard showed that as the complexity increased in the RE algorithms, the F1 score increased from 0.3-0.4 to around 0.9. The NER models demonstrated excellent predictions, with the simple RE method showing 0.84-0.92 in the evaluation metrics, and the advanced and combined RE method demonstrating 0.95-0.99 in the evaluation. This study provided an example of the benefit of combining NER methods and NLP models in extracting target information from free-text to structured data and fulfilling the need for missing diagnoses from unstructured notes.

A deep learning algorithm successfully detects periodontal disease from 2D bitewing radiographs, according to research presented at EuroPerio10, the world's leading congress in periodontology and implant dentistry organized by the European Federation of Periodontology (EFP). "Our study shows the potential for artificial intelligence (AI) to automatically identify periodontal pathologies that might otherwise be missed," said study author Dr. Burak Yavuz of Eskisehir Osmangazi University, Turkey. "This could reduce radiation exposure by avoiding repeat assessments, prevent the silent progression of periodontal disease, and enable earlier treatment." Previous studies have examined the use of AI to detect caries, root fractures and apical lesions but there is limited research in the field of periodontology. This study evaluated the ability of deep learning, a type of AI, to determine periodontal status in bitewing radiographs.