[Federal Register Volume 88, Number 179 (Monday, September 18, 2023)]
[Notices]
[Pages 63956-63957]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2023-20066]
-----------------------------------------------------------------------
DEPARTMENT OF HEALTH AND HUMAN SERVICES
Centers for Disease Control and Prevention
[30Day-23-23FJ]
Agency Forms Undergoing Paperwork Reduction Act Review
In accordance with the Paperwork Reduction Act of 1995, the Centers
for Disease Control and Prevention (CDC) has submitted the information
collection request titled ``Evaluating Deep Learning Algorithm
Assessment of Digital Photographs for Dental Public Health
Surveillance'' to the Office of Management and Budget (OMB) for review
and approval. CDC previously published a ``Proposed Data Collection
Submitted for Public Comment and Recommendations'' notice on June 5,
2023 to obtain comments from the public and affected agencies. CDC
received two comments. This notice serves to allow an additional 30
days for public and affected agency comments.
CDC will accept all comments for this proposed information
collection project. The Office of Management and Budget is particularly
interested in comments that:
(a) Evaluate whether the proposed collection of information is
necessary for the proper performance of the functions of the agency,
including whether the information will have practical utility;
(b) Evaluate the accuracy of the agencies estimate of the burden of
the proposed collection of information, including the validity of the
methodology and assumptions used;
(c) Enhance the quality, utility, and clarity of the information to
be collected;
(d) Minimize the burden of the collection of information on those
who are to respond, including, through the use of appropriate
automated, electronic, mechanical, or other technological collection
techniques or other forms of information technology, e.g., permitting
electronic submission of responses; and
(e) Assess information collection costs.
To request additional information on the proposed project or to
obtain a copy of the information collection plan and instruments, call
(404) 639-7570. Comments and recommendations for the proposed
information collection should be sent within 30 days of publication of
this notice to www.reginfo.gov/public/do/PRAMain. Find this particular
information collection by selecting ``Currently under 30-day Review--
Open for Public Comments'' or by using the search function. Direct
written comments and/or suggestions regarding the items contained in
this notice to the Attention: CDC Desk Officer, Office of Management
and Budget, 725 17th Street NW, Washington, DC 20503 or by fax to (202)
395-5806. Provide written comments within 30 days of notice
publication.
Proposed Project
Evaluating Deep Learning Algorithm Assessment of Digital
Photographs for Dental Public Health Surveillance--New--National Center
for Chronic Disease Prevention and Health Promotion (NCCDPHP), Centers
for Disease Control and Prevention (CDC).
Background and Brief Description
By age 19, 57% of U.S. adolescents have experienced tooth decay and
17% have at least one decayed tooth needing treatment. Prevalence of
untreated tooth decay among non-Hispanic Black and Mexican American
adolescents is about 30% higher than among non-Hispanic White
adolescents, and among low-income, almost twice the prevalence of
higher-income adolescents. Untreated tooth decay will not resolve and
can cause pain, infection, and difficulties in learning. Poor oral
health in youth is associated with both lower school attendance and
grades. More than 34 million school hours are lost annually due to
unplanned dental visits for acute care needs. Reducing the percentage
of youths who have experienced tooth decay and the percentage with
untreated tooth decay are national health goals (Healthy People 2030).
There are two highly effective interventions to prevent tooth
decay. Dental sealants prevent about 80% of cavities over two years in
the permanent molars where about 90% of tooth decay occurs. Fluoride
can prevent decay in permanent teeth by 15% to 43% per year depending
on mode of delivery. Although the American Dental Association
recommends dentists provide topical fluoride and dental sealants to
youth at risk for caries, uptake of these services is low with about
20% of low-income youth receiving them during an annual dental visit.
Access to these preventive services as measured by dental sealant
prevalence and receipt of preventive dental services among low-income
children are national health goals.
The Centers for Disease Control and Prevention (CDC) has collected
national data on caries, sealant, and fluorosis prevalence in the
National Health and Nutrition Examination Survey (NHANES) for over 30
years and has supported State oral health programs to collect data on
caries and sealant prevalence through cooperative agreements since
2001. Twenty States are currently funded from September 2018 to August
2023 by Actions to Improve Oral Health Outcomes, CDC-RFA-DP18-1810.
Collecting these data can be resource intensive as they are obtained
through visual/tactile examinations conducted by dental professionals.
These data, however, have enabled Federal and State agencies to: (1)
prioritize groups at elevated risk for enhanced prevention efforts; (2)
monitor trends in children's oral health status and disparities; (3)
inform planning, implementation and evaluation of effective oral health
interventions, programs, and policies; (4) measure progress toward
Healthy People objectives; and (5) educate the public and policy makers
regarding cross-cutting public health programs. Having local estimates
of these measures would enable decision-makers to better prioritize
communities for programs that increase access to preventive dental
services.
CDC is examining the feasibility and validity of using digital
photos taken by non-dental professionals, which in turn would be
analyzed by deep learning algorithms to assess youth's oral health
status in lieu of human examination. This deep learning assessment tool
ultimately could be used by public health officials for dental public
health surveillance at the local, State, and national level. It is
anticipated that obtaining information on dental conditions via deep
learning assessment of digital images as opposed to human assessment
will: (1) be more cost effective as it would not require dental
personnel; and (2) improve the accuracy of assessment due to minimal
bias and less confounding factors associated with the examiner (e.g.,
subjective index and thresholding). This tool also would offer
mobility, simplicity, and affordability for rapid and scalable
adaptation in community-based settings.
In order to train and test the deep learning algorithms to identify
caries, sealants, and fluorosis, data on these conditions as assessed
by standardized examiners and corresponding photos are required. The
CDC requests a one-year OMB approval for the one-time
[[Page 63957]]
collection of oral health data from 1,000 middle- and high-school
students in Colorado communities with naturally occurring fluoride in
the tap water at or exceeding one part per million. CDC is funding the
Colorado State Health Department to implement the collection by
recruiting eligible schools and dental examiners, gaining consent,
arranging logistics, and collecting de-identified examination data and
photos taken by the dental examiners. CDC is funding a national expert
in dental public health data collection to train the examiners.
Finally, CDC is funding researchers at Purdue University to develop
photo-taking protocols and deep learning algorithms to identify dental
conditions. Data collected for each student will include: (1) human
assessment of fluorosis severity in the six upper anterior teeth, and
caries/sealant assessment of the occlusal surfaces of the eight
permanent molars; and (2) nine smartphone digital photos of the upper
anterior teeth and 24 intraoral camera digital photos of the occlusal
surfaces of the eight permanent molars. Digital photos of the teeth and
the completed paper screening form will be uploaded to a HIPAA
compliant cloud storage box that can only be accessed by examiners and
designated CDC researchers with administrative rights. CDC is
authorized to collect this information under the Public Health Service
Act, title 42, section 247b-14, Oral health promotion and disease
prevention; and the Public Health Service Act, title 42, section 301.
CDC proposes using data collected from 750 students to train the
deep learning algorithms to assess caries, sealants, and fluorosis and
data from 250 students to evaluate the accuracy of the algorithms in
terms of agreement with standardized examiner assessment. Manuscripts
on: (1) the methodologies used to ensure sufficient photo quality when
taken under field conditions; and (2) the performance of the deep
learning algorithms will be submitted to peer-reviewed journals. The
deep learning tool, if sufficiently accurate, will be piloted in one
data collection cycle of NHANES that is administered by the National
Centers for Health Statistics (NCHS). Ultimately, the tool would be
shared with the State and local oral health programs and other
pertinent partners.
CDC requests OMB clearance for data collection for one year. The
total estimated annualized burden hours are 827. There are no costs to
student respondents other than their time.
Estimated Annualized Burden Hours
----------------------------------------------------------------------------------------------------------------
Average
Number of Number of burden per
Type of respondent Form name respondents responses per response (in
respondent hr)
----------------------------------------------------------------------------------------------------------------
Child................................. Screening/photo/form.... 1,000 1 16/60
Parent or caretaker................... Consent................. 1,000 1 1/60
Screener.............................. Screening/photo form 6 1 90/60
includes training,
travel, screening and
photos, and ongoing
technical assistance.
----------------------------------------------------------------------------------------------------------------
Jeffrey M. Zirger,
Lead, Information Collection Review Office, Office of Public Health
Ethics and Regulations, Office of Science, Centers for Disease Control
and Prevention.
[FR Doc. 2023-20066 Filed 9-15-23; 8:45 am]
BILLING CODE 4163-18-P