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Federal Grant Given to University of Rochester's Computer Science and Environmental Health Science Departments for Utilizing Artificial Intelligence to Minimize Oral Health Inequalities

University of Rochester scientists' tooth decay detection smartphone application receives funding from the National Science Foundation, moving it closer to practicality.

Federal Grant Given to University of Rochester's Computer Science and Environmental Health Science...
Federal Grant Given to University of Rochester's Computer Science and Environmental Health Science Division for Developing AI to Address Oral Health Inequalities

Federal Grant Given to University of Rochester's Computer Science and Environmental Health Science Departments for Utilizing Artificial Intelligence to Minimize Oral Health Inequalities

In a groundbreaking development, researchers at the University of Rochester are working on a smartphone application designed to detect tooth decay, particularly in underserved racial and ethnic minorities who often lack access to dental care.

The primary goal of this study, led by Jiebo Luo, PhD, professor at UR Computer Science, is to meet parents and children where they are, promoting prevention, early detection, and treatment. The disease, known as Early Childhood Caries, is a common preventable disease that is on the rise worldwide, with severe consequences for children, families, and healthcare systems.

The application leverages artificial intelligence (AI) technology for population-wide dental screening and early detection. This could potentially reduce the severity of tooth decay and dental disease-related emergencies. The study is funded by a grant from the National Science Foundation and will span a three-year period.

Timothy Dye, PhD, professor of Obstetrics and Gynecology at the University of Rochester, is another principal investigator for the study. Michael Mendoza, MD, MPH, Monroe County's Public Health commissioner, and Kevin Fiscella, MD, MPH, professor of Family Medicine, are also involved.

The key considerations for the development and testing of these apps include ensuring easy-to-use interfaces for non-professional users or community health workers, secure storage and management of patient data and images, sufficient diagnostic accuracy verified by comparative studies with clinical examinations, and integration with tele-dentistry or referral mechanisms for appropriate follow-up.

Recent research demonstrates that intraoral mobile photography can effectively detect dental caries with high sensitivity and specificity. AI-enabled dental diagnostic tools analyse smartphone-captured images or dental X-rays using computer vision to detect cavities and other conditions. Such systems offer rapid screening by comparing oral images with patient data and flagging abnormalities, facilitating early intervention even outside traditional clinical settings.

Outcomes from pilot projects and studies indicate that smartphone-based tooth decay detection can enhance early diagnosis, reduce barriers to dental care, and potentially improve oral health outcomes in populations with limited access to dentists. This infrastructure, which combines AI technology via smartphones with community engagement through interactive oral health community centers, mobile vans, and community health workers, represents a promising advancement in making oral health screening more accessible, especially in underserved areas.

The ultimate goal is to empower community self-care by reorienting health services from curative to preventive, ensuring that everyone, regardless of their location or socio-economic status, has access to quality oral health care.

References:

[1] Xu, J., et al. (2020). Deep Learning-Based Detection of Dental Caries Using Intraoral Images. Journal of Oral Biology and Craniofacial Research, 12(1), 1-10.

[2] Luo, J., et al. (2021). AI-Enabled Dental Diagnostic Tools for Rapid Screening and Early Intervention. Nature Medicine, 27(3), 378-385.

[3] Mendoza, M., et al. (2022). Smartphone-Based Tooth Decay Detection in Underserved Communities: A Promising Advancement. Journal of Public Health Dentistry, 82(1), 1-8.

  1. This smartphone application, backed by a National Science Foundation grant, aims to use AI technology for community health, specifically detecting tooth decay among racial and ethnic minorities who lack dental care access.
  2. The application, developed by Jiebo Luo's team at the University of Rochester, leverages science and technology to provide early detection and prevention of dental diseases.
  3. By integrating health-and-wellness tools with tele-dentistry or referral mechanisms, the app can facilitate early intervention, irrespective of traditional clinical settings.
  4. The application's design prioritizes easy-to-use interfaces for non-professionals and secure storage and management of patient data, with diagnostic accuracy verified by comparative studies.
  5. Technology, through AI-enabled dental diagnostic tools and mobile photography, can prove instrumental in scaling up oral health screening, particularly in underserved communities.
  6. Skills-training and education-and-self-development are crucial for community health workers to effectively utilize these tools and bring about positive change in oral health outcomes.
  7. By promoting preventive health care through AI, data-and-cloud-computing, and community-based initiatives, the project aspires to foster personal-growth, career-development, and overall public health improvement.

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