Developing Automated Computer Algorithms to Track Periodontal Disease Change from Longitudinal Electronic Dental Records

Classification of periodontitis stage and grade using natural language processing techniques

Periodontitis is a complex and microbiome-related inflammatory condition impacting dental supporting tissues. Emphasizing the potential of Clinical Decision Support Systems (CDSS), this study aims to facilitate early diagnosis of periodontitis by extracting patients' information collected as dental charts and notes. We developed a CDSS to predict the stage and grade of periodontitis using natural language processing (NLP) techniques including bidirectional encoder representation for transformers (BERT). We compared the performance of BERT with that of a baseline feature-engineered model. A secondary data analysis was conducted using 309 anonymized patient periodontal charts and corresponding clinician's notes obtained from the university periodontal clinic. After data preprocessing, we added a classification layer on top of the pre-trained BERT model to classify the clinical notes into their corresponding stage and grades. Then, we fine-tuned the pre-trained BERT model on 70% of our data. The performance of the model was evaluated on 32 unseen new patients' clinical notes. The results were compared with the output of a baseline feature-engineered algorithm coupled with MLP techniques to classify the stage and grade of periodontitis. Our proposed BERT model predicted the patients' stage and grade with 77% and 75% accuracy, respectively. MLP model showed that the accuracy of correct classification of stage and grade of the periodontitis on a set of 32 new unseen data was 59.4% and 62.5%, respectively. The BERT model could predict the periodontitis stage and grade on the same new dataset with higher accuracy (66% and 72%, respectively). The utilization of BERT in this context represents a groundbreaking application in dentistry, particularly in CDSS. Our BERT model outperformed baseline models, even with reduced information, promising efficient review of patient notes. This integration of advanced NLP techniques with CDSS frameworks holds potential for timely interventions, preventing complications and reducing healthcare costs.

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.

Natural Language Processing: Chances and Challenges in Dentistry

INTRODUCTION

Natural language processing (NLP) is an intersection between Computer Science and Linguistic which aims to enable machines to process and understand human language. We here summarized applications and limitations of NLP in dentistry.

DATA AND SOURCES

Narrative review.

FINDINGS

NLP has evolved increasingly fast. For the dental domain, relevant NLP applications are text classification (e.g., symptom classification) and natural language generation and understanding (e.g., clinical chatbots assisting professionals in office work and patient communication). Analyzing large quantities of text will allow understanding diseases and their trajectories and support a more precise and personalized care. Speech recognition systems may serve as virtual assistants and facilitate automated documentation. However, to date, NLP has rarely been applied in dentistry. Existing research focuses mainly on rule-based solutions for narrow tasks. Technologies such as Recurrent Neural Networks and Transformers have been shown to surpass the language processing capabilities of such rule-based solutions in many fields, but are data-hungry (i.e., rely on large amounts of training data), which limits their application in the dental domain at present. Technologies such as federated or transfer learning or data sharing concepts may allow to overcome this limitation, while challenges in terms of explainability, reproducibility, generalizability and evaluation of NLP in dentistry remain to be resolved for enabling approval of such technologies in medical devices and services.

CONCLUSIONS

NLP will become a cornerstone of a number of applications in dentistry. The community is called to action to improve the current limitations and foster reliable, high-quality dental NLP.

CLINICAL SIGNIFICANCE

NLP for text classification (e.g., dental symptom classification) and language generation and understanding (e.g., clinical chatbots, speech recognition) will support administrative tasks in dentistry, provide deeper insights for clinicians and support research and education.

Survival analysis of posterior composite restorations in National Dental PBRN general dentistry practices

OBJECTIVE

Quantify the survival of posterior composite restorations (PCR) placed during the study period in permanent teeth in United States (US) general dental community practices and factors predictive of that survival.

METHODS

A retrospective cohort study was conducted utilizing de-identified electronic dental record (EDR) data of patients who received a PCR in 99 general dentistry practices in the National Dental Practice-Based Research Network (Network). The final analyzed data set included 700,885 PCRs from 200,988 patients. Descriptive statistics and Kaplan Meier (product limit) estimator were performed to estimate the survival rate (defined as the PCR not receiving any subsequent treatment) after the first PCR was observed in the EDR during the study time. The Cox proportional hazards model was done to account for patient- and tooth-specific covariates.

RESULTS

The overall median survival time was 13.3 years. The annual failure rates were 4.5-5.8 % for years 1-5; 5.3-5.7 %, 4.9-5.5 %, and 3.3-5.2 % for years 6-10, 11-15, and 16-20, respectively. The failure descriptions recorded for < 7 % failures were mostly caries (54 %) and broken or fractured tooth/restorations (23 %). The following variables significantly predicted PCR survival: number of surfaces that comprised the PCR; having at least one interproximal surface; tooth type; type of prior treatment received on the tooth; Network region; patient age and sex. Based on the magnitude of the multivariable estimates, no single factor predominated.

CONCLUSIONS

This study of Network practices geographically distributed across the US observed PCR survival rates and predictive factors comparable to studies done in academic settings and outside the US.

CLINICAL SIGNIFICANCE

Specific baseline factors significantly predict the survival of PCRs done in US community dental practices.