BigMouth: development and maintenance of a successful dental data repository

Periodontal Disease, Tooth Loss, and Systemic Conditions: An Exploratory Study

BACKGROUND

Although systemic medical conditions are associated with periodontitis and tooth loss, large-scale studies that include less prevalent systemic conditions are needed. The purpose of the study was to investigate the link between periodontal disease and tooth loss with systemic medical conditions in a large and diverse population.

METHODS

Dental charts of adult patients who had attended the dental clinics seeking dental therapy of the universities contributing data to the BigMouth network and accepted the protocol of the study were included. Dental Procedure Codes and Current Procedural Terminology procedures were utilised to identify patients with and without periodontitis. Data were extracted from patients' electronic health records including demographic characteristics, dental procedural codes, and self-reported medical conditions as well as the number of missing teeth.

RESULTS

A total of 108,307 records were ultimately included in the analysis; 42,377 of them included a diagnosis of periodontitis. The median age of the included population was 47.0 years, and 55.2% were female. Older and male individuals were significantly more likely to be in the periodontitis group and have higher number of missing teeth. A number of systemic conditions are associated with periodontitis and a higher number of missing teeth. High blood pressure, smoking, drug use, and diabetes were all found to be significant. Other significant conditions were anaemia, lymphoma, glaucoma, dialysis, bronchitis, sinusitis hepatitis, and asthma.

CONCLUSIONS

Within the limitations of this retrospective study that utilised the BigMouth dental data repository, the association of a number of systemic conditions such as smoking, diabetes, and hypertension with periodontitis and tooth loss has been confirmed. Additional connections have been highlighted for conditions that are not commonly reported in the literature.

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.

Informatics approaches to improve the quality of dental care

Despite technological advances, challenges exist in US dental care, including variations in quality of care, access and untreated dental needs. The implementation of learning health systems (LHSs) in dentistry can help to address these challenges. LHSs use robust informatics infrastructure including data and technology to continuously measure and improve the quality and safety of care and can help to reduce costs and improve patient outcomes. The use of EHRs and standardized diagnostic terminologies are highlighted, as they allow for the storage and sharing of patient data, providing a comprehensive view of a patient's medical and dental history, and can be used to identify patterns and trends to improve the delivery of care. The BigMouth Dental Data Repository is an example of an informatic platform that aggregates patient data from multiple institutions and is being used to for scientific inquiry to improve oral health.

Supplement Consumption and Periodontal Health: An Exploratory Survey Using the BigMouth Repository

BACKGROUND

Dietary supplements have been investigated for their impact on the periodontal apparatus (alveolar bone, mucosa, periodontal ligament, and cementum) and their hypothetical protective role against periodontitis. There remains a gap in the field in this area. Thus, the present study aims to examine the correlation between populations who report taking different dietary supplements and their relative periodontal health.

METHODS

The BigMouth dental data repository derived from the dental Electronic Health Records (EHRs) of the University of Michigan school of dentistry was used to extract data relating to all patients who fulfilled the eligibility criteria. The prevalence of periodontitis compared to periodontal health as related to supplement consumption was assessed.

RESULTS

A total of 118,426 individuals (55,459 males and 62,967 females) with self-reported consumption of the dietary supplements of interest were identified in the University of Michigan database via the BigMouth repository. Associations with the following vitamins were investigated, Vitamin B, Vitamin C, Vitamin D, Vitamin E, Multivitamins, Fish oil, Calcium, Omega 3, Saw palmetto, Zinc, Sildenafil, Flax seed, Folic acid, Garlic pills, Ginger pills, Ginko, Ginseng, Glucosamine, Iron, and Magnesium. Out of these supplements, only multivitamins and iron were found to significantly favor periodontal health, while folic acid and vitamin E significantly favored periodontitis.

CONCLUSIONS

This study found a minimal association between the consumption of dietary supplements with periodontal health.

Caries risk and social determinants of health: A big data report

BACKGROUND

Oral health is influenced by social determinants of health (SDH), predisposing people and communities to greater risk of developing caries. This study evaluated the association between caries risk in adults and SDH such as ZIP Codes, systemic diseases, payment methods, and race or ethnicity.

METHODS

The BigMouth Dental Data Repository (n = 57,211) was used to extract clinical and SDH data from patients' dental electronic health records for 2019. Caries risk categories were used as ZIP Code data was merged with the Social Deprivation Index, a composite measure of area-level deprivation based on 7 demographic characteristics collected in the American Community Survey.

RESULTS

The results showed that the odds of being in the high caries risk group were higher for people in the 49- to 64-year age group (adjusted odds ratio [aOR], 2.24; 95% CI, 2.08 to 2.40; P ≤ .001), men (aOR, 1.19; 95% CI, 1.13 to 1.25; P ≤ .001), people who had comorbidities (diabetes: aOR, 1.16; 95% CI, 1.08 to 1.24; P ≤ .001; cardiovascular disease: aOR, 1.40; 95% CI, 1.32 to 1.50), and people with an Social Deprivation Index score above the 75th percentile (aOR, 2.39; 95% CI, 2.21 to 2.58; P ≤ .001). In addition, Hispanic and Black people had higher odds of being at high caries risk than other races or ethnicities (Hispanic: aOR, 3.05; 95% CI, 2.32 to 4.00; Black: aOR, 2.05; 95% CI, 1.02 to 4.01).

CONCLUSIONS

This study shows the association of caries risk with higher social deprivation, reinforcing the role of structural and upstream factors in oral health. This study is unique in using recorded ZIP Code information and assessing caries risk levels for those regions.

PRACTICAL IMPLICATIONS

The physical and structural environment should be considered contributors to caries risk in people.

Natural language processing for clinical notes in dentistry: A systematic review

OBJECTIVE

To identify and synthesise research on applications of natural language processing (NLP) for information extraction and retrieval from clinical notes in dentistry.

MATERIALS AND METHODS

A predefined search strategy was applied in EMBASE, CINAHL and Medline. Studies eligible for inclusion were those that that described, evaluated, or applied NLP to clinical notes containing either human or simulated patient information. Quality of the study design and reporting was independently assessed based on a set of questions derived from relevant tools including CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). A narrative synthesis was conducted to present the results.

RESULTS

Of the 17 included studies, 10 developed and evaluated NLP methods and 7 described applications of NLP-based information retrieval methods in dental records. Studies were published between 2015 and 2021, most were missing key details needed for reproducibility, and there was no consistency in design or reporting. The 10 studies developing or evaluating NLP methods used document classification or entity extraction, and 4 compared NLP methods to non-NLP methods. The quality of reporting on NLP studies in dentistry has modestly improved over time.

CONCLUSIONS

Study design heterogeneity and incomplete reporting of studies currently limits our ability to synthesise NLP applications in dental records. Standardisation of reporting and improved connections between NLP methods and applied NLP in dentistry may improve how we can make use of clinical notes from dentistry in population health or decision support systems.

PROTOCOL REGISTRATION

PROSPERO CRD42021227823.

Dental Research Data Availability and Quality According to the FAIR Principles

According to the FAIR principles, data produced by scientific research should be findable, accessible, interoperable, and reusable-for instance, to be used in machine learning algorithms. However, to date, there is no estimate of the quantity or quality of dental research data evaluated via the FAIR principles. We aimed to determine the availability of open data in dental research and to assess compliance with the FAIR principles (or FAIRness) of shared dental research data. We downloaded all available articles published in PubMed-indexed dental journals from 2016 to 2021 as open access from Europe PubMed Central. In addition, we took a random sample of 500 dental articles that were not open access through Europe PubMed Central. We assessed data sharing in the articles and compliance of shared data to the FAIR principles programmatically. Results showed that of 7,509 investigated articles, 112 (1.5%) shared data. The average (SD) level of compliance with the FAIR metrics was 32.6% (31.9%). The average for each metric was as follows: findability, 3.4 (2.7) of 7; accessibility, 1.0 (1.0) of 3; interoperability, 1.1 (1.2) of 4; and reusability, 2.4 (2.6) of 10. No considerable changes in data sharing or quality of shared data occurred over the years. Our findings indicated that dental researchers rarely shared data, and when they did share, the FAIR quality was suboptimal. Machine learning algorithms could understand 1% of available dental research data. These undermine the reproducibility of dental research and hinder gaining the knowledge that can be gleaned from machine learning algorithms and applications.