Document ID: FDA-2023-N-0062-0001
Agency: fda
Document Type: Rule
Title: Medical Devices; Hematology and Pathology Devices; Classification of the Software Algorithm Device To Assist Users in Digital Pathology
Posted Date: 2023-02-02T05:00Z

[Federal Register Volume 88, Number 22 (Thursday, February 2, 2023)]
[Rules and Regulations]
[Pages 7007-7010]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2023-02141]

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DEPARTMENT OF HEALTH AND HUMAN SERVICES

Food and Drug Administration

21 CFR Part 864

[Docket No. FDA-2023-N-0062]

Medical Devices; Hematology and Pathology Devices; Classification 
of the Software Algorithm Device To Assist Users in Digital Pathology

AGENCY: Food and Drug Administration, HHS.

ACTION: Final amendment; final order.

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SUMMARY: The Food and Drug Administration (FDA, Agency, or we) is 
classifying the software algorithm device to assist users in digital 
pathology 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 the software algorithm device to assist 
users in digital pathology's classification. We are taking this action 
because we have determined that classifying the device into class II 
(special controls) will provide a reasonable assurance of safety and 
effectiveness of the device. We believe this action will also enhance 
patients' access to beneficial innovative devices.

DATES: This order is effective February 2, 2023. The classification was 
applicable on September 21, 2021.

FOR FURTHER INFORMATION CONTACT: Arpita Roy, Center for Devices and 
Radiological Health, Food and Drug Administration, 10903 New Hampshire 
Ave., Bldg. 66, Rm. 3319, Silver Spring, MD 20993-0002, 240-402-4807, 
[email protected].

SUPPLEMENTARY INFORMATION: 

I. Background

    Upon request, FDA has classified the software algorithm device to 
assist users in digital pathology as class II (special controls), which 
we have determined will provide a reasonable assurance of safety and 
effectiveness. In addition, we believe this action will enhance 
patients' access to beneficial innovation, in part 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 as, and remains 
within, class III and requires premarket approval unless and until FDA 
takes an action to classify or reclassify the device (see 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 (see 21

[[Page 7008]]

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. 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 
application 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 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.
    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 December 31, 2020, FDA received Paige.AI, Inc.'s request for De 
Novo classification of the Paige Prostate. 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 safety and 
effectiveness, 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 21 U.S.C. 360c(a)(1)(B)). 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 September 21, 2021, 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 
864.3750.\1\ We have named the generic type of device software 
algorithm device to assist users in digital pathology, and it is 
identified as an in vitro diagnostic device intended to evaluate 
acquired scanned pathology whole slide images. The device uses software 
algorithms to provide information to the user about presence, location, 
and characteristics of areas of the image with clinical implications. 
Information from this device is intended to assist the user in 
determining a pathology 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 following risks to health associated 
specifically with this type of device and the measures required to 
mitigate these risks in table 1.

 Table 1--Software Algorithm Device To Assist Users in Digital Pathology
                      Risks and Mitigation Measures
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       Identified risks                   Mitigation measures
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False negative classification  Certain design verification and
 (loss of accuracy).            validation, including certain device
                                descriptions, certain analytical
                                studies, and clinical studies; and
                               Certain labeling information, including
                                certain device descriptions, certain
                                performance information, and certain
                                limitations.
False positive classification  Certain design verification and
 (loss of accuracy).            validation, including certain device
                                descriptions, certain analytical
                                studies, and clinical studies; and
                               Certain labeling information, including
                                certain device descriptions, certain
                                performance information, and certain
                                limitations.
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    FDA has determined that special controls, in combination with the 
general controls, address these risks to health and provide reasonable 
assurance of safety and effectiveness. 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 order. This device is subject to premarket notification 
requirements under section 510(k) of the FD&C Act.

III. Analysis of Environmental Impact

    The Agency has determined under 21 CFR 25.34(b) that this action is 
of a type that does not individually or cumulatively 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

[[Page 7009]]

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 21 CFR 
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 system regulation, have been approved under OMB 
control number 0910-0073; and the collections of information in 21 CFR 
parts 801and 809, regarding labeling, have been approved under OMB 
control number 0910-0485.

List of Subjects in 21 CFR Part 864

    Blood, Medical devices, and 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.  864.3750 to subpart D to read as follows:

Sec.  864.3750  Software algorithm device to assist users in digital 
pathology.

    (a) Identification. A software algorithm device to assist users in 
digital pathology is an in vitro diagnostic device intended to evaluate 
acquired scanned pathology whole slide images. The device uses software 
algorithms to provide information to the user about presence, location, 
and characteristics of areas of the image with clinical implications. 
Information from this device is intended to assist the user in 
determining a pathology diagnosis.
    (b) Classification. Class II (special controls). The special 
controls for this device are:
    (1) The intended use on the device's label and labeling required 
under Sec.  809.10 of this chapter must include:
    (i) Specimen type;
    (ii) Information on the device input(s) (e.g., scanned whole slide 
images (WSI), etc.);
    (iii) Information on the device output(s) (e.g., format of the 
information provided by the device to the user that can be used to 
evaluate the WSI, etc.);
    (iv) Intended users;
    (v) Necessary input/output devices (e.g., WSI scanners, viewing 
software, etc.);
    (vi) A limiting statement that addresses use of the device as an 
adjunct; and
    (vii) A limiting statement that users should use the device in 
conjunction with complete standard of care evaluation of the WSI.
    (2) The labeling required under Sec.  809.10(b) of this chapter 
must include:
    (i) A detailed description of the device, including the following:
    (A) Detailed descriptions of the software device, including the 
detection/analysis algorithm, software design architecture, interaction 
with input/output devices, and necessary third-party software;
    (B) Detailed descriptions of the intended user(s) and recommended 
training for safe use of the device; and
    (C) Clear instructions about how to resolve device-related issues 
(e.g., cybersecurity or device malfunction issues).
    (ii) A detailed summary of the performance testing, including test 
methods, dataset characteristics, results, and a summary of sub-
analyses on case distributions stratified by relevant confounders, such 
as anatomical characteristics, patient demographics, medical history, 
user experience, and scanning equipment, as applicable.
    (iii) Limiting statements that indicate:
    (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 in the 
dataset used to train, test, and tune the algorithm during device 
development;
    (B) The data acquired using the device should only be interpreted 
by the types of users indicated in the intended use statement; and
    (C) Qualified users should employ appropriate procedures and 
safeguards (e.g., quality control measures, etc.) to assure the 
validity of the interpretation of images obtained using this device.
    (3) Design verification and validation must include:
    (i) 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) A detailed description of the technical performance assessment 
study protocols (e.g., regions of interest (ROI) localization study) 
and results used to assess the device output(s) (e.g., image overlays, 
image heatmaps, etc.);
    (B) The training dataset must include cases representing different 
pre-analytical variables representative of the conditions likely to be 
encountered when used as intended (e.g., fixation type and time, 
histology slide processing techniques, challenging diagnostic cases, 
multiple sites, patient demographics, etc.);
    (C) The number of WSI in an independent validation dataset must be 
appropriate to demonstrate device accuracy in detecting and localizing 
ROIs on scanned WSI, and must include subsets clinically relevant to 
the intended use of the device;
    (D) Emergency recovery/backup functions, which must be included in 
the device design;
    (E) 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; 
and
    (F) A risk management plan, including a justification of how the 
cybersecurity vulnerabilities of third-party software and services are 
reduced by the device's risk management mitigations in order to address 
cybersecurity risks associated with key device functionality (such as 
loss of image, altered metadata, corrupted image data, degraded image 
quality, etc.). The risk management plan must also include how the 
device will be maintained on its intended platform (e.g. a general 
purpose computing platform, virtual machine, middleware, cloud-based 
computing services, medical device hardware, etc.), which includes how 
the software integrity will be maintained, how the software will be 
authenticated on the platform, how any reliance on the platform will be 
managed in order to facilitate implementation of cybersecurity controls 
(such as user authentication, communication encryption and 
authentication, etc.), and how the device will be protected when the 
underlying platform is not updated, such that the specific risks of the 
device are addressed (such as loss of image, altered metadata, 
corrupted image data, degraded image quality, etc.).
    (ii) Data demonstrating acceptable, as determined by FDA, 
analytical device

[[Page 7010]]

performance, by conducting analytical studies. 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, 
etc.). The analytical studies must include:
    (A) Bench testing or technical testing to assess device output, 
such as localization of ROIs within a pre-specified threshold. Samples 
must be representative of the entire spectrum of challenging cases 
likely to be encountered when the device is used as intended; and
    (B) Data from a precision study that demonstrates device 
performance when used with multiple input devices (e.g., WSI scanners) 
to assess total variability across operators, within-scanner, between-
scanner and between-site, using clinical specimens with defined, 
clinically relevant, and challenging characteristics likely to be 
encountered when the device is used as intended. Samples must be 
representative of the entire spectrum of challenging cases likely to be 
encountered when the device is used as intended. Precision, including 
performance of the device and reproducibility, must be assessed by 
agreement between replicates.
    (iii) Data demonstrating acceptable, as determined by FDA, clinical 
validation must be demonstrated by conducting studies with clinical 
specimens. For each clinical 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) 
(on-site/remote), challenging diagnoses, etc.). The studies must 
include:
    (A) A study demonstrating the performance by the intended users 
with and without the software device (e.g., unassisted and device-
assisted reading of scanned WSI of pathology slides). The study dataset 
must contain sufficient numbers of 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, and subsets defined 
by image scanning characteristics, etc.) such that the performance 
estimates and confidence intervals for these individual subsets can be 
characterized. The performance assessment must be based on appropriate 
diagnostic accuracy measures (e.g., sensitivity, specificity, 
predictive value, diagnostic likelihood ratio, etc.).
    (B) [Reserved]

    Dated: January 26, 2023.
Lauren K. Roth,
Associate Commissioner for Policy.
[FR Doc. 2023-02141 Filed 2-1-23; 8:45 am]
BILLING CODE 4164-01-P