Medical Devices; Hematology and Pathology Devices; Classification of the Digital Cervical Cytology Slide Imaging System With Artificial Intelligence Algorithm

Issuing agencies

Abstract

The Food and Drug Administration (FDA) is classifying the digital cervical cytology slide imaging system with artificial intelligence algorithm into class II (special controls). The special controls that apply to the device type are identified in this order and will be part of the codified language for classification of the digital cervical cytology slide imaging system with artificial intelligence algorithm. We are taking this action because we have determined that classifying the device into class II will provide a reasonable assurance of the safety and effectiveness of the device. We believe this action will also enhance patients' access to beneficial innovative devices, in part by reducing regulatory burdens.

Full Text

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<title>Federal Register, Volume 91 Issue 213 (Thursday, November 5, 2026)</title>
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[Federal Register Volume 91, Number 213 (Thursday, November 5, 2026)]
[Rules and Regulations]
[Pages 69687-69690]
From the Federal Register Online via the Government Publishing Office [<a href="http://www.gpo.gov">www.gpo.gov</a>]
[FR Doc No: 2026-22593]



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DEPARTMENT OF HEALTH AND HUMAN SERVICES

Food and Drug Administration

21 CFR Part 864

[Docket No. FDA-2026-N-12384]


Medical Devices; Hematology and Pathology Devices; Classification 
of the Digital Cervical Cytology Slide Imaging System With Artificial 
Intelligence Algorithm

AGENCY: Food and Drug Administration, HHS.

ACTION: Final amendment; final order.

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SUMMARY: The Food and Drug Administration (FDA) is classifying the 
digital cervical cytology slide imaging system with artificial 
intelligence algorithm into class II (special controls). The special 
controls that apply to the device type are identified in this order and 
will be part of the codified language for classification of the digital 
cervical cytology slide imaging system with artificial intelligence 
algorithm. We are taking this action because we have determined that 
classifying the device into class II will provide a reasonable 
assurance of the safety and effectiveness of the device. We believe 
this action will also enhance patients' access to beneficial innovative 
devices, in part by reducing regulatory burdens.

DATES: This order is effective November 5, 2026. The classification was 
applicable on January 31, 2024.

FOR FURTHER INFORMATION CONTACT: Arpita Roy, Center for Devices and 
Radiological Health, Food and Drug Administration, 10903 New Hampshire 
Ave., Bldg. 66, Rm. 3302, Silver Spring, MD 20993-0002, 240-402-4807, 
<a href="/cdn-cgi/l/email-protection#347546445d40551a665b4d745250551a5c5c471a535b42"><span class="__cf_email__" data-cfemail="bbfac9cbd2cfda95e9d4c2fbdddfda95d3d3c895dcd4cd">[email&#160;protected]</span></a>.

SUPPLEMENTARY INFORMATION:

I. Background

    Upon request, FDA (the Agency or we) has classified the digital 
cervical cytology slide imaging system with artificial intelligence 
algorithm into class II (special controls), which we have determined 
will provide a reasonable assurance of the safety and effectiveness of 
the device. In addition, we believe this action will enhance patients' 
access to beneficial innovation, in part by reducing regulatory burdens 
by placing the device into a lower device class than the automatic 
class III assignment.
    The automatic assignment of class III occurs by operation of law 
and without any action by FDA, regardless of the level of risk posed by 
the new device. Any device that was not in commercial distribution 
before May 28, 1976, is automatically classified into, and remains 
within, class III and requires premarket approval unless and until FDA 
takes an action to classify or reclassify the device (21 U.S.C. 
360c(f)(1)). We refer to these devices as ``postamendments devices'' 
because they were not in commercial distribution prior to the date of 
enactment of the Medical Device Amendments of 1976, which amended the 
Federal Food, Drug, and Cosmetic Act (FD&C Act).
    FDA may take a variety of actions in appropriate circumstances to 
classify or reclassify a device into class I or II. We may issue an 
order finding a new device to be substantially equivalent under section 
513(i) of the FD&C Act (21 U.S.C. 360c(i)) to a predicate device that 
does not require premarket approval. We determine whether a new device 
is substantially equivalent to a predicate device by means of the 
procedures for premarket notification under section 510(k) of the FD&C 
Act (21 U.S.C. 360(k)) and part 807 (21 CFR part 807).
    FDA may also classify a device through ``De Novo'' classification, 
a common name for the process authorized under section 513(f)(2) of the 
FD&C Act (see also part 860, subpart D (21 CFR part 860, subpart D)). 
Section 207 of the Food and Drug Administration Modernization Act of 
1997 (Pub. L. 105-115) established the first procedure for De Novo 
classification. Section 607 of the Food and Drug Administration Safety 
and Innovation Act (Pub. L. 112-144) modified the De Novo 
classification process by adding a second procedure. A device sponsor 
may utilize either procedure for De Novo classification.
    Under the first procedure, the person submits a premarket 
notification (510(k)) for a device that has not previously been 
classified. After receiving an order from FDA classifying the device 
into class III under section 513(f)(1) of the FD&C Act, the person then 
requests a classification under section 513(f)(2).
    Under the second procedure, rather than first submitting a 510(k) 
and then a request for classification, if the person determines that 
there is no legally marketed device upon which to base a determination 
of substantial equivalence, that person requests a classification under 
section 513(f)(2) of the FD&C Act.
    Under either procedure for De Novo classification, FDA is required 
to classify the device by written order within 120 days. The 
classification will be according to the criteria under section 
513(a)(1) of the FD&C Act. Although the device was automatically placed 
within class III, the De Novo classification is considered to be the 
initial classification of the device.
    We believe this De Novo classification will enhance patients' 
access to beneficial innovation, in part by reducing regulatory 
burdens. When FDA classifies a device into class I or II via the De 
Novo process, the device can serve as a predicate for future devices of 
that type, including for 510(k)s (see section 513(f)(2)(B)(i) of the 
FD&C Act). As a result, other device sponsors do not have to submit a 
De Novo request or premarket approval application to market a 
substantially equivalent device (see section 513(i) of the FD&C Act, 
defining ``substantial equivalence''). Instead, sponsors can use the 
less burdensome 510(k) process, when necessary, to market their device.

II. De Novo Classification

    On August 27, 2021, FDA received Hologic Inc.'s request for De Novo 
classification of the Genius Digital Diagnostics System with the Genius 
Cervical AI algorithm. FDA reviewed the request in order to classify 
the device under the criteria for classification set forth in section 
513(a)(1) of the FD&C Act.
    We classify devices into class II if general controls by themselves 
are insufficient to provide reasonable assurance of the safety and 
effectiveness of the device, but there is sufficient information to 
establish special controls that, in combination with the general 
controls, provide reasonable assurance of the safety and effectiveness 
of the device for its intended use (see section 513(a)(1)(B) of the 
FD&C Act). After review of the information submitted in the request, we 
determined that the device can be classified into class II with the 
establishment of special controls. FDA has determined that these 
special controls, in addition to the general controls, will provide 
reasonable assurance of the safety and effectiveness of the device.
    Therefore, on January 31, 2024, FDA issued an order to the 
requester classifying the device into class II. In this final order, 
FDA is codifying the classification of the device by adding 21 CFR 
864.3900.\1\ We have named the

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generic type of device ``digital cervical cytology slide imaging system 
with artificial intelligence algorithm,'' and it is identified as a 
prescription in vitro diagnostic device consisting of an automated 
digital slide creation, viewing, and management system intended to aid 
in the review of digital images of slides prepared from Pap test 
specimens and conventional Pap smears by selecting and presenting areas 
of interest to facilitate interpretation by the reader. The artificial 
intelligence (AI) software algorithm uses machine learning techniques 
to provide information to the user about the presence, location, or 
characteristics of areas of the image with clinical implications. 
Information from this device is intended to assist the user in 
rendering a cytology diagnosis.
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    \1\ FDA notes that the ``ACTION'' caption for this final order 
is styled as ``Final amendment; final order,'' rather than ``Final 
order.'' Beginning in December 2019, this editorial change was made 
to indicate that the document ``amends'' the Code of Federal 
Regulations. The change was made in accordance with the Office of 
Federal Register's (OFR) interpretations of the Federal Register Act 
(44 U.S.C. chapter 15), its implementing regulations (1 CFR 5.9 and 
parts 21 and 22), and the Document Drafting Handbook.
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    FDA has identified the risks to health associated with this type of 
device and the measures required to mitigate these risks in table 1.

  Table 1--Risks to Health and Mitigation Measures for Digital Cervical
  Cytology Slide Imaging Systems With Artificial Intelligence Algorithm
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       Identified risks to health              Mitigation measures
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False negative and false positive        Certain design verification and
 results due to device errors in          validation, including certain
 presenting abnormal cells in the         studies and risk mitigation
 digital image gallery.                   analysis.
                                         Certain labeling information,
                                          including limitations, device
                                          descriptions, methodology and
                                          protocols, and performance
                                          information.
False negative and false positive        Certain design verification and
 results due to incorrect                 validation, including certain
 interpretation of digital images or      studies and risk mitigation
 incorrect operation of the device by     analysis.
 the user.                               Certain labeling information,
                                          including limitations, user
                                          training, device descriptions,
                                          methodology and protocols, and
                                          performance information.
------------------------------------------------------------------------

    FDA has determined that special controls, in combination with the 
general controls, address these risks to health and provide reasonable 
assurance of the safety and effectiveness of the device. For a device 
to fall within this classification, and thus avoid automatic 
classification in class III, it would have to comply with the special 
controls named in this final order. The necessary special controls 
appear in the regulation codified by this final order.
    At the time of classification, digital cervical cytology slide 
imaging systems with AI algorithm are for prescription use only. 
Therefore, these devices are subject to the prescription labeling 
requirements for in vitro diagnostic products (see 21 CFR 809.10(a)(4) 
and (b)(5)(ii)).
    Under the FD&C Act, submission of a premarket notification under 
section 510(k) is required to reasonably assure the safety and 
effectiveness of class II devices unless FDA determines that the device 
type should be exempt under section 510(m) of the FD&C Act. At this 
time, FDA has not made this determination for digital cervical cytology 
slide imaging systems with AI algorithm. This device is therefore 
subject to premarket notification requirements under section 510(k) of 
the FD&C Act.

III. Analysis of Environmental Impact

    We have determined under 21 CFR 25.34(b) that this action is of a 
type that does not normally have a significant effect on the human 
environment. Therefore, neither an environmental assessment nor an 
environmental impact statement is required.

IV. Paperwork Reduction Act of 1995

    This final order establishes special controls that refer to 
previously approved collections of information found in other FDA 
regulations and guidance. These collections of information are subject 
to review by the Office of Management and Budget (OMB) under the 
Paperwork Reduction Act of 1995 (44 U.S.C. 3501-3521). The collections 
of information in part 860, subpart D, regarding De Novo classification 
have been approved under OMB control number 0910-0844; the collections 
of information in 21 CFR part 814, subparts A through E, regarding 
premarket approval have been approved under OMB control number 0910-
0231; the collections of information in part 807, subpart E, regarding 
premarket notification submissions have been approved under OMB control 
number 0910-0120; the collections of information in 21 CFR part 820 
regarding quality management system regulation have been approved under 
OMB control number 0910-0073; and the collections of information in 21 
CFR parts 801 and 809 regarding labeling have been approved under OMB 
control number 0910-0485.

List of Subjects in 21 CFR Part 864

    Blood, Medical devices, Packaging and containers.

    Therefore, under the Federal Food, Drug, and Cosmetic Act and under 
authority delegated to the Commissioner of Food and Drugs, 21 CFR part 
864 is amended as follows:

PART 864--HEMATOLOGY AND PATHOLOGY DEVICES

0
1. The authority citation for part 864 continues to read as follows:

    Authority: 21 U.S.C. 351, 360, 360c, 360e, 360j, 360l, 371.


0
2. Add Sec.  864.3900 to subpart D to read as follows:


Sec.  864.3900  Digital cervical cytology slide imaging system with 
artificial intelligence algorithm.

    (a) Identification. The digital cervical cytology slide imaging 
system with artificial intelligence (AI) algorithm is a prescription in 
vitro diagnostic device consisting of an automated digital slide 
creation, viewing, and management system intended to aid in the review 
of digital images of slides prepared from Pap test specimens and 
conventional Pap smears by selecting and presenting areas of interest 
to facilitate interpretation by the reader. The AI software algorithm 
uses machine learning techniques to provide information to the user 
about the presence, location, or characteristics of areas of the image 
with clinical implications. Information from this device is intended to 
assist the user in rendering a cytology diagnosis.
    (b) Classification. Class II (special controls). The special 
controls for this device are:

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    (1) The labeling required under Sec.  809.10(b) of this chapter 
must include the following:
    (i) The intended use must include the specimen type (e.g., Pap test 
or conventional Pap smear), information on the device input (e.g., Pap 
test glass slides) and output(s) (e.g., whole slide image (WSI) of 
cytology glass slides or cell spot), information on the AI algorithm 
output(s) (i.e., format of the information provided by the device to 
the user, such as objects of interest (OOI) found on the WSI, which is 
presented in a gallery for the user to review), and intended users;
    (ii) A detailed description of the intended patient population;
    (iii) Detailed instructions for use, including the intended image 
review and interpreting protocol for use of the device, and how to 
resolve device-related issues (e.g., cybersecurity or device 
malfunction issues);
    (iv) A detailed description of the device, including the following:
    (A) Necessary input/output devices (e.g., scanner, imager, 
management server, review station, viewing software, software 
algorithm);
    (B) Necessary infrastructure, platform, and/or software application 
services, and configuration(s) (e.g., storage, network architecture, 
cloud services);
    (C) Compatible scanning hardware and scanning protocols;
    (D) Interaction with input/output devices, and necessary third-
party software;
    (E) Recommended training for intended users for safe use of the 
device;
    (v) A detailed summary of the performance testing required by 
paragraphs (b)(2)(i), (b)(2)(ii), (b)(2)(iv), and (b)(2)(v)(A) of this 
section, including test methods, dataset characteristics, results, and 
a summary of sub-analyses on case distributions stratified by relevant 
confounders, i.e., three-dimensional (3D) characteristics of cells and 
cell clusters in cytology samples, patient demographics, medical 
history, user experience, sites, and scanning equipment as applicable;
    (vi) Intended user screening time data to provide information about 
daily slide screening workload limits, as determined by the performance 
testing in paragraph (b)(2)(v)(B); and
    (vii) Limiting statements, including the following:
    (A) A description of situations in which the device may fail or may 
not operate at its expected performance level (e.g., poor image quality 
or for certain subpopulations), including any limitations due to the 
dataset used to train, test, and tune the algorithm during device 
development;
    (B) That review of the entire gallery (i.e., OOIs with clinical 
implications) should be performed by the user prior to rendering a 
diagnosis;
    (C) That users should use the device in conjunction with the 
standard of care, an assessment of patient history and other risk 
factors, and professional screening and management guidelines to guide 
patient care; and
    (D) That users should employ appropriate procedures and safeguards 
(e.g., daily quality control measures, deferral to glass slide review, 
cybersecurity), including when the device is used for remote review of 
images, to assure the validity of the interpretation of images obtained 
using this device.
    (2) Design verification and validation must include:
    (i) A detailed description and the technical performance assessment 
of each component in the device, including the following, as 
appropriate:
    (A) Slide feeder;
    (B) Light source;
    (C) Imaging optics;
    (D) Z-focal plane acquisition, including a detailed description of 
methods used to ensure that the complete depth of the cellular material 
is captured (e.g., Z-plane merging, Z-stacking), when applicable;
    (E) Mechanical scanner movement;
    (F) Digital imaging sensor;
    (G) Image processing software;
    (H) Image composition techniques;
    (I) Image file formats;
    (J) Image review manipulation software;
    (K) Computer environment; and
    (L) Display system.
    (ii) Detailed technical performance assessment at the system level, 
including for the following, as appropriate:
    (A) Coverage of Z-depth to represent the 3D aspects of cells and 
cell clusters in cytology samples;
    (B) Color reproducibility;
    (C) Spatial resolution;
    (D) Focusing accuracy;
    (E) Whole slide cell spot or specimen material coverage;
    (F) Stitching error; and
    (G) Turnaround time.
    (iii) A detailed description of the device software, including its 
algorithm and its development, that includes a description of any 
datasets used to train, tune, or test the software algorithm. This 
detailed description of the device software must include:
    (A) The study protocols (e.g., OOI reproducibility study, imaging 
reproducibility study, cell count validation study) and results used to 
assess the device output(s) (e.g., image overlays, image heatmaps, 
image location, total number of cells);
    (B) The training dataset, including cases representing different 
pre-analytical variables representative of the conditions likely to be 
encountered when used as intended (e.g., fixation type and time, 
cytology slide processors, challenging diagnostic cases, multiple 
sites, patient demographics);
    (C) An appropriate number of digital cytology images in an 
independent validation dataset to demonstrate device accuracy in 
detecting OOI on scanned cytology images, which must include subsets 
clinically relevant to the intended use of the device; and
    (D) A system level architecture diagram with a matrix to depict the 
communication endpoints, communication protocols, and security 
protections for the device and its supportive systems, including any 
products or services that are included in the communication pathway.
    (iv) Detailed documentation of studies demonstrating acceptable, as 
determined by FDA, analytical device performance using samples that are 
representative of the entire spectrum of challenging cases likely to be 
encountered when the device is used as intended. For each analytical 
study, relevant details must be documented (e.g., the origin of the 
study slides and images, reader/annotator qualifications, method of 
annotation, location of the study site(s), challenging diagnoses). The 
analytical studies must include:
    (A) Testing to assess device outputs (e.g., OOI reproducibility 
study, cell count validation study); and
    (B) Precision and reproducibility study(ies) that demonstrate 
device performance when used with multiple input devices (e.g., digital 
cytology scanners) to assess total variability across images, 
operators, within-scanners, between-scanners and between-sites, using a 
comprehensive set of representative clinical specimens with defined, 
clinically relevant features and challenging characteristics likely to 
be encountered when the device is used as intended. Precision and 
reproducibility must be assessed by agreement between replicates.
    (v) Data demonstrating acceptable, as determined by FDA, clinical 
validation by conducting studies with clinical specimens to demonstrate 
that screening and diagnosing digital images of cervical cytology 
slides with the assistance of the device's software algorithms is non-
inferior to using a comparator method that FDA has determined to be 
appropriate (e.g., optical microscope). For each clinical study, 
relevant details must be

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documented (e.g., the origin of the study slides and images, reader/
annotator qualifications, method of annotation, location of the study 
site(s) (on-site/remote), challenging diagnoses). The studies must 
include:
    (A) A study demonstrating the performance by the intended users 
with and without the AI algorithm (i.e., unassisted (manual microscope 
read) and device-assisted reading of scanned digital images of cervical 
cytology slides). The study dataset must contain sufficient numbers of 
cytological slides for each diagnostic category and processed by 
representative processing instruments such that the performance 
estimates and confidence intervals for these individual subsets can be 
appropriately characterized. The performance assessment must be based 
on appropriate diagnostic accuracy measures (e.g., sensitivity, 
specificity, predictive value, diagnostic likelihood ratio); and
    (B) Intended user screening time data obtained from the clinical 
study, to characterize and set recommended daily maximum workload 
screening limits for intended users. The study dataset must contain 
sufficient cases from relevant cohorts that are representative of the 
scope of patients likely to be encountered given the intended use of 
the device (e.g., subsets defined by clinically relevant confounders, 
challenging diagnoses, subsets with potential biopsy appearance 
modifiers, concomitant diseases, subsets defined by image scanning 
characteristics). The reader population must be comprised of the 
intended user population in terms of clinical training, certification, 
and years of experience.
    (vi) Human factors study data to evaluate the usability and user 
experience of the device with representative users.

Grace R. Graham,
Deputy Commissioner for Policy, Legislation, and International Affairs.
[FR Doc. 2026-22593 Filed 11-4-26; 8:45 am]
BILLING CODE 4164-01-P


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