[Federal Register Volume 88, Number 107 (Monday, June 5, 2023)]
[Notices]
[Pages 36581-36583]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2023-11859]


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DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

[60Day-23-23FJ; Docket No. CDC-2023-0042]


Proposed Data Collection Submitted for Public Comment and 
Recommendations

AGENCY: Centers for Disease Control and Prevention (CDC), Department of 
Health and Human Services (HHS).

ACTION: Notice with comment period.

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SUMMARY: The Centers for Disease Control and Prevention (CDC), as part 
of its continuing effort to reduce public burden and maximize the 
utility of government information, invites the general public and other 
federal agencies the opportunity to comment on a proposed information 
collection, as required by the Paperwork Reduction Act of 1995. This 
notice invites comment on a proposed information collection project 
titled Evaluating Deep Learning Algorithm Assessment of Digital 
Photographs for Dental Public Health Surveillance. This project entails 
one-time data collection of oral health data from 1,000 school students 
to examine the feasibility and validity of using digital photos taken 
by non-dental professionals, which are analyzed by deep learning 
algorithms to assess youth's oral health status.

DATES: CDC must receive written comments on or before August 4, 2023.

ADDRESSES: You may submit comments, identified by Docket No. CDC-2023-
0042 by any of the following methods:
     Federal eRulemaking Portal: www.regulations.gov. Follow 
the instructions for submitting comments.
     Mail: Jeffrey M. Zirger, Information Collection Review 
Office, Centers for Disease Control and Prevention, 1600 Clifton Road 
NE, MS H21-8, Atlanta, Georgia 30329.
    Instructions: All submissions received must include the agency name 
and Docket Number. CDC will post, without change, all relevant comments 
to www.regulations.gov.
    Please note: Submit all comments through the Federal eRulemaking 
portal (www.regulations.gov) or by U.S. mail to the address listed 
above.

FOR FURTHER INFORMATION CONTACT: To request more information on the 
proposed project or to obtain a copy of the information collection plan 
and instruments, contact Jeffrey M. Zirger, Information Collection 
Review Office, Centers for Disease Control and Prevention, 1600 Clifton 
Road NE, MS H21-8, Atlanta, Georgia 30329; Telephone: 404-639-7118; 
Email: [email protected].

SUPPLEMENTARY INFORMATION: Under the Paperwork Reduction Act of 1995 
(PRA) (44 U.S.C. 3501-3520), federal agencies must obtain approval from 
the Office of Management and Budget (OMB) for each collection of 
information they conduct or sponsor. In addition, the PRA also requires 
federal agencies to provide a 60-day notice in the Federal Register 
concerning each proposed collection of information, including each new 
proposed collection, each proposed extension of existing collection of 
information, and each reinstatement of previously approved information 
collection before submitting the collection to the OMB for approval. To 
comply with this requirement, we are publishing this notice of a 
proposed data collection as described below.
    The OMB is particularly interested in comments that will help:
    1. Evaluate whether the proposed collection of information is 
necessary

[[Page 36582]]

for the proper performance of the functions of the agency, including 
whether the information will have practical utility;
    2. Evaluate the accuracy of the agency's estimate of the burden of 
the proposed collection of information, including the validity of the 
methodology and assumptions used;
    3. Enhance the quality, utility, and clarity of the information to 
be collected;
    4. 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 
submissions of responses; and
    5. Assess information collection costs.

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 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. The Colorado State Health 
Department will implement the collection by recruiting selected schools 
and dental examiners, gaining consent, arranging logistics, and 
collecting data from dental examination and photos taken by the dental 
examiners. CDC will provide dental examination and photo taking 
protocols and train the examiners. 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. Only de-identified 
data will be collected. All de-identified data--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, the Association of State and 
Territorial Dental Directors, and other pertinent partners.
    The 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.

[[Page 36583]]



                                                            Estimated Annualized Burden Hours
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                                                                                                             Number of    Average burden
               Type of respondent                               Form name                    Number of     responses per   per response    Total burden
                                                                                            respondents     respondent        (in hr)         (in hr)
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Child..........................................  Screening/photo/form...................           1,000               1           16/60             270
Parent or caretaker............................  Consent................................           1,000               1            1/60              17
Screener.......................................  Screening/photo form includes training,               6               1              90             540
                                                  travel, screening and photos, and
                                                  ongoing technical assistance.
                                                                                         ---------------------------------------------------------------
    Total......................................  .......................................  ..............  ..............  ..............             827
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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. 2023-11859 Filed 6-2-23; 8:45 am]
BILLING CODE 4163-18-P