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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
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 lif...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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JMIR Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8571694/ https://www.ncbi.nlm.nih.gov/pubmed/34529582 http://dx.doi.org/10.2196/32921 |
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author | Xiao, Jin Luo, Jiebo Ly-Mapes, Oriana Wu, Tong Tong Dye, Timothy Al Jallad, Nisreen Hao, Peirong Ruan, Jinlong Bullock, Sherita Fiscella, Kevin |
author_facet | Xiao, Jin Luo, Jiebo Ly-Mapes, Oriana Wu, Tong Tong Dye, Timothy Al Jallad, Nisreen Hao, Peirong Ruan, Jinlong Bullock, Sherita Fiscella, Kevin |
author_sort | Xiao, Jin |
collection | PubMed |
description | 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 |
format | Online Article Text |
id | pubmed-8571694 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-85716942021-11-17 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 Xiao, Jin Luo, Jiebo Ly-Mapes, Oriana Wu, Tong Tong Dye, Timothy Al Jallad, Nisreen Hao, Peirong Ruan, Jinlong Bullock, Sherita Fiscella, Kevin JMIR Res Protoc Protocol 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 JMIR Publications 2021-10-22 /pmc/articles/PMC8571694/ /pubmed/34529582 http://dx.doi.org/10.2196/32921 Text en ©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. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included. |
spellingShingle | Protocol Xiao, Jin Luo, Jiebo Ly-Mapes, Oriana Wu, Tong Tong Dye, Timothy Al Jallad, Nisreen Hao, Peirong Ruan, Jinlong Bullock, Sherita Fiscella, Kevin 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8571694/ https://www.ncbi.nlm.nih.gov/pubmed/34529582 http://dx.doi.org/10.2196/32921 |
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