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Artificial intelligence-powered smartphone application, AICaries, improves at-home dental caries screening in children: Moderated and unmoderated usability test
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 technolog...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9645586/ https://www.ncbi.nlm.nih.gov/pubmed/36381137 http://dx.doi.org/10.1371/journal.pdig.0000046 |
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author | Al-Jallad, Nisreen Ly-Mapes, Oriana Hao, Peirong Ruan, Jinlong Ramesh, Ashwin Luo, Jiebo Wu, Tong Tong Dye, Timothy Rashwan, Noha Ren, Johana Jang, Hoonji Mendez, Luis Alomeir, Nora Bullock, Sherita Fiscella, Kevin Xiao, Jin |
author_facet | Al-Jallad, Nisreen Ly-Mapes, Oriana Hao, Peirong Ruan, Jinlong Ramesh, Ashwin Luo, Jiebo Wu, Tong Tong Dye, Timothy Rashwan, Noha Ren, Johana Jang, Hoonji Mendez, Luis Alomeir, Nora Bullock, Sherita Fiscella, Kevin Xiao, Jin |
author_sort | Al-Jallad, Nisreen |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9645586 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-96455862022-11-14 Artificial intelligence-powered smartphone application, AICaries, improves at-home dental caries screening in children: Moderated and unmoderated usability test Al-Jallad, Nisreen Ly-Mapes, Oriana Hao, Peirong Ruan, Jinlong Ramesh, Ashwin Luo, Jiebo Wu, Tong Tong Dye, Timothy Rashwan, Noha Ren, Johana Jang, Hoonji Mendez, Luis Alomeir, Nora Bullock, Sherita Fiscella, Kevin Xiao, Jin PLOS Digit Health Research Article 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. Public Library of Science 2022-06-02 /pmc/articles/PMC9645586/ /pubmed/36381137 http://dx.doi.org/10.1371/journal.pdig.0000046 Text en © 2022 Al-Jallad et al 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 author and source are credited. |
spellingShingle | Research Article Al-Jallad, Nisreen Ly-Mapes, Oriana Hao, Peirong Ruan, Jinlong Ramesh, Ashwin Luo, Jiebo Wu, Tong Tong Dye, Timothy Rashwan, Noha Ren, Johana Jang, Hoonji Mendez, Luis Alomeir, Nora Bullock, Sherita Fiscella, Kevin Xiao, Jin Artificial intelligence-powered smartphone application, AICaries, improves at-home dental caries screening in children: Moderated and unmoderated usability test |
title | Artificial intelligence-powered smartphone application, AICaries, improves at-home dental caries screening in children: Moderated and unmoderated usability test |
title_full | Artificial intelligence-powered smartphone application, AICaries, improves at-home dental caries screening in children: Moderated and unmoderated usability test |
title_fullStr | Artificial intelligence-powered smartphone application, AICaries, improves at-home dental caries screening in children: Moderated and unmoderated usability test |
title_full_unstemmed | Artificial intelligence-powered smartphone application, AICaries, improves at-home dental caries screening in children: Moderated and unmoderated usability test |
title_short | Artificial intelligence-powered smartphone application, AICaries, improves at-home dental caries screening in children: Moderated and unmoderated usability test |
title_sort | artificial intelligence-powered smartphone application, aicaries, improves at-home dental caries screening in children: moderated and unmoderated usability test |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9645586/ https://www.ncbi.nlm.nih.gov/pubmed/36381137 http://dx.doi.org/10.1371/journal.pdig.0000046 |
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