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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.
Afiliação
  • Al-Jallad N; Eastman Institute for Oral Health, University of Rochester Medical Center, Rochester, NY, United States of America.
  • Ly-Mapes O; Eastman Institute for Oral Health, University of Rochester Medical Center, Rochester, NY, United States of America.
  • Hao P; Department of Computer Science, University of Rochester, United States of America.
  • Ruan J; Department of Computer Science, University of Rochester, United States of America.
  • Ramesh A; Department of Computer Science, University of Rochester, United States of America.
  • Luo J; Department of Computer Science, University of Rochester, United States of America.
  • Wu TT; Department of Biostatistics and computational biology, University of Rochester Medical Center, Rochester, United States of America.
  • Dye T; Department of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, United States of America.
  • Rashwan N; Eastman Institute for Oral Health, University of Rochester Medical Center, Rochester, NY, United States of America.
  • Ren J; University of Rochester, United States of America.
  • Jang H; Temple University School of Dentistry, Pennsylvania, United States of America.
  • Mendez L; Eastman Institute for Oral Health, University of Rochester Medical Center, Rochester, NY, United States of America.
  • Alomeir N; Eastman Institute for Oral Health, University of Rochester Medical Center, Rochester, NY, United States of America.
  • Bullock S; Healthy Baby Network, Rochester, United States of America.
  • Fiscella K; Department of Family Medicine, University of Rochester Medical Center, Rochester, NY, United States of America.
  • Xiao J; Eastman Institute for Oral Health, University of Rochester Medical Center, Rochester, NY, United States of America.
Article em En | MEDLINE | ID: mdl-36381137
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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Qualitative_research / Risk_factors_studies / Screening_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Qualitative_research / Risk_factors_studies / Screening_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article