Efficacy of tooth brushing via a three-dimensional motion tracking system for dental plaque control in school children: a randomized controlled clinical trial

BACKGROUND

School children are in a developmental period in which permanent teeth replace primary dentition. It is also a period with a high incidence of gingivitis and caries, which can be improved with adequate tooth brushing. Advances in information technology have led to the development of smart health devices that assist in tooth brushing. We compared the effectiveness of computer-assisted toothbrushing using a toothbrushing instruction (TBI) method called the smart toothbrush and smart mirror (STM) system with that of conventional TBI (verbal instructions) for plaque control in school children.

METHODS

This randomized controlled clinical trial analyzed and compared the reduction of the modified Quigley-Hein plaque index between the two methods in 42 school children. The participants were randomly assigned to the STM system group (n = 21) or conventional-TBI group (n = 21). The plaque indices were evaluated at baseline, immediately after TBI (day 0), and 1 week and 1 month after TBI.

RESULTS

The STM system and conventional TBI led to an average reduction of 40.50% and 40.57%, respectively, in whole mouth plaque. Reductions in the plaque indices within each tested time period were observed in both groups (P < 0.001), and the mean plaque reduction did not differ between the two groups (P = 0.44).

CONCLUSIONS

The present study tested a computer assisted system for TBI, more studies are needed to confirm its usefulness in different objectives. Clinical relevance The computer-assisted STM system may be an alternative of TBI for children. Trial registration ClinicalTrials.gov (NCT04627324) Registered 13/11/2020-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT04627324 .

Data Dentistry: How Data Are Changing Clinical Care and Research

Data are a key resource for modern societies and expected to improve quality, accessibility, affordability, safety, and equity of health care. Dental care and research are currently transforming into what we term data dentistry, with 3 main applications: 1) medical data analysis uses deep learning, allowing one to master unprecedented amounts of data (language, speech, imagery) and put them to productive use. 2) Data-enriched clinical care integrates data from individual (e.g., demographic, social, clinical and omics data, consumer data), setting (e.g., geospatial, environmental, provider-related data), and systems level (payer or regulatory data to characterize input, throughput, output, and outcomes of health care) to provide a comprehensive and continuous real-time assessment of biologic perturbations, individual behaviors, and context. Such care may contribute to a deeper understanding of health and disease and a more precise, personalized, predictive, and preventive care. 3) Data for research include open research data and data sharing, allowing one to appraise, benchmark, pool, replicate, and reuse data. Concerns and confidence into data-driven applications, stakeholders’ and system’s capabilities, and lack of data standardization and harmonization currently limit the development and implementation of data dentistry. Aspects of bias and data-user interaction require attention. Action items for the dental community circle around increasing data availability, refinement, and usage; demonstrating safety, value, and usefulness of applications; educating the dental workforce and consumers; providing performant and standardized infrastructure and processes; and incentivizing and adopting open data and data sharing.

mTeeth: Identifying Brushing Teeth Surfaces Using Wrist-Worn Inertial Sensors

Ensuring that all the teeth surfaces are adequately covered during daily brushing can reduce the risk of several oral diseases. In this paper, we propose the mTeeth model to detect teeth surfaces being brushed with a manual toothbrush in the natural free-living environment using wrist-worn inertial sensors. To unambiguously label sensor data corresponding to different surfaces and capture all transitions that last only milliseconds, we present a lightweight method to detect the micro-event of brushing strokes that cleanly demarcates transitions among brushing surfaces. Using features extracted from brushing strokes, we propose a Bayesian Ensemble method that leverages the natural hierarchy among teeth surfaces and patterns of transition among them. For training and testing, we enrich a publicly-available wrist-worn inertial sensor dataset collected from the natural environment with time-synchronized precise labels of brushing surface timings and moments of transition. We annotate 10,230 instances of brushing on different surfaces from 114 episodes and evaluate the impact of wide between-person and within-person between-episode variability on machine learning model's performance for brushing surface detection.

Intelligent Brushing Monitoring Using a Smart Toothbrush with Recurrent Probabilistic Neural Network

Smart toothbrushes equipped with inertial sensors are emerging as high-tech oral health products in personalized health care. The real-time signal processing of nine-axis inertial sensing and toothbrush posture recognition requires high computational resources. This paper proposes a recurrent probabilistic neural network (RPNN) for toothbrush posture recognition that demonstrates the advantages of low computational resources as a requirement, along with high recognition accuracy and efficiency. The RPNN model is trained for toothbrush posture recognition and brushing position and then monitors the correctness and integrity of the Bass Brushing Technique. Compared to conventional deep learning models, the recognition accuracy of RPNN is 99.08% in our experiments, which is 16.2% higher than that of the Convolutional Neural Network (CNN) and 21.21% higher than the Long Short-Term Memory (LSTM) model. The model we used can greatly reduce the computing power of hardware devices, and thus, our system can be used directly on smartphones.

mORAL: An mHealth Model for Inferring Oral Hygiene Behaviors in-the-wild Using Wrist-worn Inertial Sensors

We address the open problem of reliably detecting oral health behaviors passively from wrist-worn inertial sensors. We present our model named mORAL (pronounced em oral) for detecting brushing and flossing behaviors, without the use of instrumented toothbrushes so that the model is applicable to brushing with still prevalent manual toothbrushes. We show that for detecting rare daily events such as toothbrushing, adopting a model that is based on identifying candidate windows based on events, rather than fixed-length timeblocks, leads to significantly higher performance. Trained and tested on 2,797 hours of sensor data collected over 192 days on 25 participants (using video annotations for ground truth labels), our brushing model achieves 100% median recall with a false positive rate of one event in every nine days of sensor wearing. The average error in estimating the start/end times of the detected event is 4.1% of the interval of the actual toothbrushing event.

Efficacy of computer-assisted, 3D motion-capture toothbrushing instruction

OBJECTIVE

The objective of this study was to compare the efficacy of computer-assisted TBI using a smart toothbrush (ST) and smart mirror (SM) in plaque control to that of conventional TBI.

MATERIALS AND METHODS

We evaluated the plaque removal efficacy of a ST comprising a computer-assisted, wirelessly linked, three-dimensional (3D) motion-capture, data-logging, and SM system in TBI. We also evaluated the efficacy of TBI with a ST and SM system by analyzing the reductions of the modified Quigley-Hein plaque index in 60 volunteers. These volunteers were separated randomly into two groups: conventional TBI (control group) and computer-assisted TBI (experimental group). The changes in the plaque indexes were recorded immediately, 1 week, 1 month, and 10 months after TBI.

RESULTS

The patterns of decreases in the modified Quigley-Hein plaque indexes were similar in the two groups. Reductions of the plaque indexes of both groups in each time period were observed (P < 0.0001), and the effects of TBI did not differ between the two groups (P = 0.3803). All volunteers were sufficiently motivated in using this new system.

CONCLUSION

The reported new, computer-assisted TBI system might be an alternative option in controlling dental plaque and maintaining oral hygiene.

CLINICAL RELEVANCE

Individuals can be motivated by the new system; meanwhile, comparable effects of controlling dental plaque can be achieved.