Rule2023-02141
Medical Devices; Hematology and Pathology Devices; Classification of the Software Algorithm Device To Assist Users in Digital Pathology
Primary source
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Published
February 2, 2023
Effective
February 2, 2023
Issuing agencies
Health and Human Services DepartmentFood and Drug Administration
Abstract
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.
Full Text
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<title>Federal Register, Volume 88 Issue 22 (Thursday, February 2, 2023)</title>
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[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 [<a href="http://www.gpo.gov">www.gpo.gov</a>]
[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,
<a href="/cdn-cgi/l/email-protection#db9aa9abb2afbaf589b4a29bbdbfbaf5b3b3a8f5bcb4ad"><span class="__cf_email__" data-cfemail="723300021b06135c201d0b321416135c1a1a015c151d04">[email protected]</span></a>.
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
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