Evidence map›Paper›PMID 34529582›Full record

ArticleJMIR research protocols2021

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.

Jin Xiao, Jiebo Luo, Oriana Ly-Mapes, Tong Tong Wu, Timothy Dye, Nisreen Al Jallad, Peirong Hao, Jinlong Ruan, Sherita Bullock, Kevin Fiscella

Open access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
4.0field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 41 citations in OpenAlex.

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  12. Tech Bytes-Harnessing Artificial Intelligence for Pediatric Oral Health: A Scoping Review.International journal of clinical pediatric dentistry · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 4 institutions in 1 country.

Jin XiaoEastman Institute for Oral Health, University of Rochester, Rochester, NY, United States.ORCID https://orcid.org/0000-0002-8776-2520
Jiebo LuoComputer Science, University of Rochester, Rochester, NY, United States.ORCID https://orcid.org/0000-0002-4516-9729
Oriana Ly-MapesEastman Institute for Oral Health, University of Rochester, Rochester, NY, United States.ORCID https://orcid.org/0000-0002-0859-921X
Tong Tong WuDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, United States.ORCID https://orcid.org/0000-0002-1175-9923
Timothy DyeDepartment of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, NY, United States.ORCID https://orcid.org/0000-0002-9801-4712
Nisreen Al JalladEastman Institute for Oral Health, University of Rochester, Rochester, NY, United States.ORCID https://orcid.org/0000-0002-5990-8123
Peirong HaoComputer Science, University of Rochester, Rochester, NY, United States.ORCID https://orcid.org/0000-0002-2853-1562
Jinlong RuanComputer Science, University of Rochester, Rochester, NY, United States.ORCID https://orcid.org/0000-0003-0207-5463
Sherita BullockHealthy Baby Network, Rochester, NY, United States.ORCID https://orcid.org/0000-0002-3727-8220
Kevin FiscellaDepartment of Family Medicine, University of Rochester Medical Center, Rochester, NY, United States.ORCID https://orcid.org/0000-0003-3613-8012
Eastman Chemical Company (United States) · USUniversity of Rochester · USUniversity of Rochester Medical Center · USHealthy Start · US

Funding

Association between Oral Candida albicans and the Onset of Severe Early Childhood CariesK23DE027412 · NIDCR · UNIVERSITY OF ROCHESTER · PI XIAO, JIN · 2018 to 2022
$748k
Develop an Artificial Intelligence-powered Smartphone App AICaries for Caries Detection in ChildrenR21DE030251 · NIDCR · UNIVERSITY OF ROCHESTER · PI FISCELLA, KEVIN ANTHONY, LUO, JIEBO · 2021 to 2022
$421k
NIDCR NIH HHS K23 DE027412NIDCR NIH HHS R21 DE030251
6 · The paper itself

Abstract

backgroundEarly 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.

objectiveThis 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.

methodsOur 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.

resultsThe 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.

conclusionsUsing 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.

Indexed as

artificial intelligencedental cariesmDentistrymobile dentistrysmartphone appunderserved population

Identifiers

PMID34529582
PMCPMC8571694
OpenAlexW3199934193

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.