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...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000046 |
| _version_ | 1797725002118725632 |
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| author | Nisreen Al-Jallad Oriana Ly-Mapes Peirong Hao Jinlong Ruan Ashwin Ramesh Jiebo Luo Tong Tong Wu Timothy Dye Noha Rashwan Johana Ren Hoonji Jang Luis Mendez Nora Alomeir Sherita Bullock Kevin Fiscella Jin Xiao |
| author_facet | Nisreen Al-Jallad Oriana Ly-Mapes Peirong Hao Jinlong Ruan Ashwin Ramesh Jiebo Luo Tong Tong Wu Timothy Dye Noha Rashwan Johana Ren Hoonji Jang Luis Mendez Nora Alomeir Sherita Bullock Kevin Fiscella Jin Xiao |
| author_sort | Nisreen Al-Jallad |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-12T10:24:57Z |
| format | Article |
| id | doaj.art-c187384c424a463089fd8bcaa1317bd0 |
| institution | Directory Open Access Journal |
| issn | 2767-3170 |
| language | English |
| last_indexed | 2024-03-12T10:24:57Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj.art-c187384c424a463089fd8bcaa1317bd02023-09-02T09:51:04ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702022-01-0116e000004610.1371/journal.pdig.0000046Artificial intelligence-powered smartphone application, AICaries, improves at-home dental caries screening in children: Moderated and unmoderated usability test.Nisreen Al-JalladOriana Ly-MapesPeirong HaoJinlong RuanAshwin RameshJiebo LuoTong Tong WuTimothy DyeNoha RashwanJohana RenHoonji JangLuis MendezNora AlomeirSherita BullockKevin FiscellaJin XiaoEarly 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.https://doi.org/10.1371/journal.pdig.0000046 |
| spellingShingle | Nisreen Al-Jallad Oriana Ly-Mapes Peirong Hao Jinlong Ruan Ashwin Ramesh Jiebo Luo Tong Tong Wu Timothy Dye Noha Rashwan Johana Ren Hoonji Jang Luis Mendez Nora Alomeir Sherita Bullock Kevin Fiscella Jin Xiao Artificial intelligence-powered smartphone application, AICaries, improves at-home dental caries screening in children: Moderated and unmoderated usability test. PLOS Digital Health |
| 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 |
| url | https://doi.org/10.1371/journal.pdig.0000046 |
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