Background Early childhood caries (ECC) is the most common chronic childhood disease, with nearly 1.8 billion new cases per year worldwide. ECC afflicts approximately 55% of low-income and minority US preschool children, resulting in harmful short- and long-term effects on health and quality of life. Clinical evidence shows that caries is reversible if detected and addressed in its early stages. However, many low-income US children often have poor access to pediatric dental services. In this underserved group, dental caries is often diagnosed at a late stage when extensive restorative treatment is needed. With more than 85% of lower-income Americans owning a smartphone, mobile health tools such as smartphone apps hold promise in achieving patient-driven early detection and risk control of ECC. Objective This study aims to use a community-based participatory research strategy to refine and test the usability of an artificial intelligence–powered smartphone app, AICaries, to be used by children’s parents/caregivers for dental caries detection in their children. Methods Our previous work has led to the prototype of AICaries, which offers artificial intelligence–powered caries detection using photos of children’s teeth taken by the parents’ smartphones, interactive caries risk assessment, and personalized education on reducing children’s ECC risk. This AICaries study will use a two-step qualitative study design to assess the feedback and usability of the app component and app flow, and whether parents can take photos of children’s teeth on their own. Specifically, in step 1, we will conduct individual usability tests among 10 pairs of end users (parents with young children) to facilitate app module modification and fine-tuning using think aloud and instant data analysis strategies. In step 2, we will conduct unmoderated field testing for app feasibility and acceptability among 32 pairs of parents with their young children to assess the usability and acceptability of AICaries, including assessing the number/quality of teeth images taken by the parents for their children and parents’ satisfaction. Results The study is funded by the National Institute of Dental and Craniofacial Research, United States. This study received institutional review board approval and launched in August 2021. Data collection and analysis are expected to conclude by March 2022 and June 2022, respectively. Conclusions Using AICaries, parents can use their regular smartphones to take photos of their children’s teeth and detect ECC aided by AICaries so that they can actively seek treatment for their children at an early and reversible stage of ECC. Using AICaries, parents can also obtain essential knowledge on reducing their children’s caries risk. Data from this study will support a future clinical trial that evaluates the real-world impact of using this smartphone app on early detection and prevention of ECC among low-income children. International Registered Report Identifier (IRRID) PRR1-10.2196/32921

Despite the cariogenic role of Candida suggested from recent studies, oral Candida acquisition in children at high risk for early childhood caries (ECC) and its association with cariogenic bacteria Streptococcus mutans remain unclear. Although ECC disproportionately afflicts socioeconomically disadvantaged and racial-minority children, microbiological studies focusing on the underserved group are scarce. Our prospective cohort study examined the oral colonization of Candida and S. mutans among 101 infants exclusively from a low-income and racial-minority background in the first year of life. The Cox hazard proportional model was fitted to assess factors associated with the time to event of the emergence of oral Candida and S. mutans. Oral Candida colonization started as early as 1 wk among 13% of infants, increased to 40% by 2 mo, escalated to 48% by 6 mo, and remained the same level until 12 mo. S. mutans in saliva was detected among 20% infants by 12 mo. The emergence of S. mutans by year 1 was 3.5 times higher (hazard ratio [HR], 3.5; confidence interval [CI], 1.1–11.3) in infants who had early colonization of oral Candida compared to those who were free of oral Candida ( P = 0.04) and 3 times higher (HR, 3.0; CI, 1.3–6.9) among infants whose mother had more than 3 decayed teeth ( P = 0.01), even after adjusting demographics, feeding, mother’s education, and employment status. Infants’ salivary S. mutans abundance was positively correlated with infants’ Candida albicans ( P < 0.01) and Candida krusei levels ( P < 0.05). Infants’ oral colonization of C. albicans was positively associated with mother’s oral C. albicans carriage and education ( P < 0.01) but negatively associated with mother’s employment status ( P = 0.01). Future studies are warranted to examine whether oral Candida modulates the oral bacterial community as a whole to become cariogenic during the onset and progression of ECC, which could lead to developing novel ECC predictive and preventive strategies from a fungal perspective.

Background Amid COVID-19, and other possible future infectious disease pandemics, dentistry needs to consider modified dental examination regimens that render quality care, are cost effective, and ensure the safety of patients and dental health care personnel (DHCP). Traditional dental examinations, which number more than 300 million per year in the United States, rely on person-to-person tactile examinations, pose challenges to infection control, and consume large quantities of advanced-level personal protective equipment (PPE). Therefore, our long-term goal is to develop an innovative mobile dentistry (mDent) model that takes these issues into account. This model supplements the traditional dental practice with virtual visits, supported by mobile devices such as mobile telephones, tablets, and wireless infrastructure. The mDent model leverages the advantages of digital mobile health (mHealth) tools such as intraoral cameras to deliver virtual oral examinations, treatment planning, and interactive oral health management, on a broad population basis. Conversion of the traditional dental examinations to mDent virtual examinations builds upon (1) the reliability of teledentistry, which uses intraoral photos and live videos to make diagnostic decisions, and (2) rapid advancement in mHealth tool utilization. Objective In this pilot project, we designed a 2-stage implementation study to assess 2 critical components of the mDent model: virtual hygiene examination (eHygiene) and patient self-taken intraoral photos (SELFIE). Our specific aims are to (1) assess the acceptance and barriers of mDent eHygiene among patients and DHCP, (2) assess the economic impact of mDent eHygiene, and (3) assess the patient’s capability to generate intraoral photos using mHealth tools (exploratory aim, SELFIE). Methods This study will access the rich resources of the National Dental Practice-Based Research Network to recruit 12 dentists, 12 hygienists, and 144 patients from 12 practices. For aims 1 and 2, we will use role-specific questionnaires to collect quantitative data on eHygiene acceptance and economic impact. The questionnaire components include participant characteristics, the System Usability Scale, a dentist-patient communication scale, practice operation cost, and patient opportunity cost. We will further conduct a series of iterative qualitative research activities using individual interviews to further elicit feedback and suggestion for changes to the mDent eHygiene model. For aim 3, we will use mixed methods (quantitative and qualitative) to assess the patient’s capability of taking intraoral photos, by analyzing obtained photos and recorded videos. Results The study is supported by the US National Institute of Dental and Craniofacial Research. This study received “single” institutional review board approval in August 2021. Data collection and analysis are expected to conclude by December 2021 and March 2022, respectively. Conclusions The study results will inform the logistics of conducting virtual dental examinations and empowering patients with mHealth tools, providing better safety and preserving PPE amid the COVID-19 and possible future pandemics. International Registered Report Identifier (IRRID) PRR1-10.2196/32345

Objectives To assess the oral health condition and oral microbial outcomes from receiving an innovative treatment regimen - Prenatal Total Oral Rehabilitation (PTOR). Methods This prospective cohort study included 15 pregnant women in the PTOR group who had a baseline visit before PTOR and three follow-up visits (immediate after, 2 weeks and 2 months) after receiving PTOR. A historical control group of additional 15 pregnant women was matched from a separate study based on a propensity score. Along with demographic and medical background, oral health conditions and perinatal oral health literacy were assessed. Oral samples (saliva and plaque) were analyzed to identify and quantify Streptococcus mutans and Candida species by culturing-dependent and -independent methods. Results Significant reductions of salivary S. mutans were observed following PTOR, the effect remained until 2-month follow-up ( p < 0.05). The carriage of salivary and plaque S. mutans at the 2-month visit of the PTOR group was significantly lower than that of the control group ( p < 0.05). Oral health conditions reflected by BOP and PI were significantly improved upon receiving PTOR ( p < 0.05). Receiving PTOR significantly improved the perinatal oral health literacy score, and the knowledge retained until 2-month follow-up ( p < 0.05). Conclusions PTOR is associated with an improvement in oral health conditions and perinatal oral health literacy, and a reduction in S. mutans carriage, within a 2-month follow-up period. Future clinical trials are warranted to comprehensively assess the impact of PTOR on the maternal oral flora other than S. mutans and Candida , birth outcomes, and their offspring's oral health.

Early Childhood Caries (ECC) is the most common childhood disease worldwide and a health disparity among underserved children. ECC is preventable and reversible if detected early. However, many children from low-income families encounter barriers to dental care. An at-home caries detection technology could potentially improve access to dental care regardless of patients’ economic status and address the overwhelming prevalence of ECC. Our team has developed a smartphone application (app), AICaries, that uses artificial intelligence (AI)-powered technology to detect caries using children’s teeth photos. We used mixed methods to assess the acceptance, usability, and feasibility of the AICaries app among underserved parent-child dyads. We conducted moderated usability testing (Step 1) with ten parent-child dyads using "Think-aloud" methods to assess the flow and functionality of the app and analyze the data to refine the app and procedures. Next, we conducted unmoderated field testing (Step 2) with 32 parent-child dyads to test the app within their natural environment (home) over two weeks. We administered the System Usability Scale (SUS) and conducted semi-structured individual interviews with parents and conducted thematic analyses. AICaries app received a 78.4 SUS score from the participants, indicating an excellent acceptance. Notably, the majority (78.5%) of parent-taken photos of children’s teeth were satisfactory in quality for detection of caries using the AI app. Parents suggested using community health workers to provide training to parents needing assistance in taking high quality photos of their young child’s teeth. Perceived benefits from using the AICaries app include convenient at-home caries screening, informative on caries risk and education, and engaging family members. Data from this study support future clinical trial that evaluates the real-world impact of using this innovative smartphone app on early detection and prevention of ECC among low-income children.

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Early childhood caries (ECC) is the most common, yet preventable chronic disease in children under the age of 6. Treatments on severe ECC are extremely expensive and unaffordable for socioeconomically disadvantaged families. The identification of ECC in an early stage usually requires expertise in the field, and hence is often ignored by parents. Therefore, early prevention strategies and easy-to-adopt diagnosis techniques are desired. In this study, we propose a multistage deep learning-based system for cavity detection. We create a dataset containing RGB oral images labeled manually by dental practitioners. We then investigate the effectiveness of different deep learning models on the dataset. Furthermore, we integrate the deep learning system into an easy-to-use mobile application that can diagnose ECC from an early stage and provide real-time results to untrained users.

BACKGROUND Early childhood caries (ECC) is the most common chronic childhood disease, with nearly 1.8 billion new cases per year globally. ECC afflicts approximately 55% of low-income and minority US preschool children, resulting in harmful short- and long-term effects on health and quality of life. Clinical evidence shows that caries is reversible if detected and addressed in its early stages. However, many low-income US children often have poor access to pediatric dental services. In this underserved group, dental caries is often diagnosed at a late stage when extensive restorative treatment is needed. With more than 85% of lower-income Americans owning a smartphone, mHealth tools, such as smartphone application, hold great promise to achieve patient-driven early detection and risk control of ECC. OBJECTIVE This study aims to employ a community-based participatory research strategy to refine and test the usability of an artificial intelligence (AI) -powered smartphone app, AICaries, to be used by children's parents/caregivers for dental caries detection in their children. METHODS Our previous work has led to the prototype of AICaries, which offers AI-powered caries detection using photos of children's teeth taken by the parents' smartphones, interactive caries risk assessment, and personalized education on reducing children's ECC risk. This AICaries study will utilize a 2-step qualitative study design to assess the feedback and usability of the app component, app flow and whether parents can take photo of children’s teeth on their own. Specifically, in Step 1, we will conduct individual usability tests among 10 pairs of end-users (parents with young children) to facilitate app module modification and fine-tuning using Think-aloud and Instant Data Analysis strategies. In Step 2, we will conduct unmoderated field testing for app feasibility and acceptability among 32 pairs of parents with their young children to assess the usability and acceptability of AICaries, including assessing the number/quality of teeth images taken by the parents for their children and parents’ satisfaction. RESULTS The study is funded by the National Institute of Dental and Craniofacial Research, USA. This study received IRB approval and launched in August, 2021. Data collection and analysis are expected to conclude by March 2021 and June 2022, respectively. CONCLUSIONS Using AICaries, parents can use their regular smartphones to take photo of their children’s teeth and detect ECC aided by AICaries, so that they can actively seek treatment for their children at an early and reversible stage of ECC. Using AICaries, parents can also obtain essential knowledge on reducing their children's caries risk. Data from this study will support future clinical trial that evaluates the real-world impact of using this innovative smartphone app on early detection and prevention of ECC among low-income children.