Assessing a Smartphone App (AICaries) That Uses Artificial Intelligence to Detect Dental Caries in Children and Provides Interactive Oral Health Education: Protocol for a Design and Usability Testing Study.

artificial intelligence dental caries mDentistry mobile dentistry smartphone app underserved population

Journal

JMIR research protocols
ISSN: 1929-0748
Titre abrégé: JMIR Res Protoc
Pays: Canada
ID NLM: 101599504

Informations de publication

Date de publication:
22 Oct 2021
Historique:
received: 17 08 2021
accepted: 14 09 2021
pubmed: 17 9 2021
medline: 17 9 2021
entrez: 16 9 2021
Statut: epublish

Résumé

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. 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. 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. 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. 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. PRR1-10.2196/32921.

Sections du résumé

BACKGROUND 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 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 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 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 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) UNASSIGNED
PRR1-10.2196/32921.

Identifiants

pubmed: 34529582
pii: v10i10e32921
doi: 10.2196/32921
pmc: PMC8571694
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e32921

Subventions

Organisme : NIDCR NIH HHS
ID : K23 DE027412
Pays : United States
Organisme : NIDCR NIH HHS
ID : R21 DE030251
Pays : United States

Informations de copyright

©Jin Xiao, Jiebo Luo, Oriana Ly-Mapes, Tong Tong Wu, Timothy Dye, Nisreen Al Jallad, Peirong Hao, Jinlong Ruan, Sherita Bullock, Kevin Fiscella. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 22.10.2021.

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Auteurs

Jin Xiao (J)

Eastman Institute for Oral Health, University of Rochester, Rochester, NY, United States.

Jiebo Luo (J)

Computer Science, University of Rochester, Rochester, NY, United States.

Oriana Ly-Mapes (O)

Eastman Institute for Oral Health, University of Rochester, Rochester, NY, United States.

Tong Tong Wu (TT)

Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, United States.

Timothy Dye (T)

Department of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, NY, United States.

Nisreen Al Jallad (N)

Eastman Institute for Oral Health, University of Rochester, Rochester, NY, United States.

Peirong Hao (P)

Computer Science, University of Rochester, Rochester, NY, United States.

Jinlong Ruan (J)

Computer Science, University of Rochester, Rochester, NY, United States.

Sherita Bullock (S)

Healthy Baby Network, Rochester, NY, United States.

Kevin Fiscella (K)

Department of Family Medicine, University of Rochester Medical Center, Rochester, NY, United States.

Classifications MeSH