Rule2026-18612
Medical Devices; Cardiovascular Devices; Classification of the Cardiovascular Machine Learning-Based Notification Software
Primary source
Metadata and text below are from the Federal Register, a public-domain U.S. government work. Always verify the official published version before relying on it for any legal matter.
Published
September 11, 2026
Effective
September 11, 2026
Issuing agencies
Health and Human Services DepartmentFood and Drug Administration
Abstract
The Food and Drug Administration (FDA) is classifying the cardiovascular machine learning-based notification software 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 cardiovascular machine learning- based notification software. 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 175 (Friday, September 11, 2026)</title>
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[Federal Register Volume 91, Number 175 (Friday, September 11, 2026)]
[Rules and Regulations]
[Pages 57785-57787]
From the Federal Register Online via the Government Publishing Office [<a href="http://www.gpo.gov">www.gpo.gov</a>]
[FR Doc No: 2026-18612]
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DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration
21 CFR Part 870
[Docket No. FDA-2026-N-9907]
Medical Devices; Cardiovascular Devices; Classification of the
Cardiovascular Machine Learning-Based Notification Software
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
cardiovascular machine learning-based notification software 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 cardiovascular machine learning-
based notification software. 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 September 11, 2026. The classification
was applicable on August 3, 2023.
FOR FURTHER INFORMATION CONTACT: Hetal Odobasic, Center for Devices and
Radiological Health, Food and Drug Administration, 10903 New Hampshire
Ave., Bldg. 66, Rm. 2108, Silver Spring, MD 20993-0002, 240-402-6540,
<a href="/cdn-cgi/l/email-protection#b6fed3c2d7da98f9d2d9d4d7c5dfd5f6d0d2d798dedec598d1d9c0"><span class="__cf_email__" data-cfemail="50183524313c7e1f343f3231233933103634317e3838237e373f26">[email protected]</span></a>.
SUPPLEMENTARY INFORMATION:
I. Background
Upon request, FDA (the Agency or we) has classified the
cardiovascular machine learning-based notification software 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
[[Page 57786]]
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 January 10, 2023, FDA received Viz.ai, Inc.'s request for De
Novo classification of the Viz HCM device. 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 August 3, 2023, 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
870.2380.\1\ We have named the generic type of device ``cardiovascular
machine learning-based notification software,'' and it is identified as
software that employs machine learning techniques to suggest the
likelihood of a cardiovascular disease or condition for further
referral or diagnostic follow-up. The software identifies a single
condition based on one or more non-invasive physiological inputs as
part of routine medical care. It is intended as the basis for further
testing and is not intended to provide diagnostic quality output. It is
not intended to identify or detect arrhythmias.
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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 Cardiovascular
Machine Learning-Based Notification Software
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Identified risks to health Mitigation measures
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False positive or false negative Clinical performance testing;
leading to incorrect treatment or Non-clinical performance
diagnosis. testing; and Labeling.
Incorrect treatment or diagnosis due to Clinical performance testing;
model bias or failure to adequately and Labeling.
generalize to the intended use
population.
Device used in unsupported patient Labeling; Human factors
population or with unsupported input/ assessment; and Software
hardware. verification, validation, and
hazard analysis.
Overreliance on device output for Human factors assessment; and
follow-up. Labeling.
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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 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.
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 cardiovascular machine
learning-based notification software. This device is therefore 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 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;
[[Page 57787]]
and the collections of information in 21 CFR part 801 regarding
labeling have been approved under OMB control number 0910-0485.
List of Subjects in 21 CFR Part 870
Medical devices.
Therefore, under the Federal Food, Drug, and Cosmetic Act and under
authority delegated to the Commissioner of Food and Drugs, 21 CFR part
870 is amended as follows:
PART 870--CARDIOVASCULAR DEVICES
0
1. The authority citation for part 870 continues to read as follows:
Authority: 21 U.S.C. 351, 360, 360c, 360e, 360j, 360l, 371.
0
2. Add Sec. 870.2380 to subpart C to read as follows:
Sec. 870.2380 Cardiovascular machine learning-based notification
software.
(a) Identification. Cardiovascular machine learning-based
notification software employs machine learning techniques to suggest
the likelihood of a cardiovascular disease or condition for further
referral or diagnostic follow-up. The software identifies a single
condition based on one or more non-invasive physiological inputs as
part of routine medical care. It is intended as the basis for further
testing and is not intended to provide diagnostic quality output. It is
not intended to identify or detect arrhythmias.
(b) Classification. Class II (special controls). The special
controls for this device are:
(1) Clinical performance testing must demonstrate that the device
performs as intended under anticipated conditions of use. The following
must be met:
(i) Clinical validation must use a test dataset of real-world data
acquired from a representative patient population. Data must be
representative of the range of data sources and data quality likely to
be encountered in the intended use population and relevant use
conditions in the intended use environment. The test dataset must be
independent from data used in training/development and contain
sufficient numbers of cases from important cohorts (e.g., demographic
populations, subsets defined by clinically relevant confounders,
comorbidities, and subsets defined by hardware and acquisition
characteristics) such that the performance estimates and confidence
intervals of the device for these individual subsets can be
characterized for the intended use population and acquisition systems
(e.g., acquisition hardware or preprocessing software). Study protocols
must include a description of the adjudication process(es) for
determining ground truth of training and test datasets;
(ii) Data must be provided within the clinical validation study or
using equivalent datasets to demonstrate the consistency of the output
over the full range of inputs;
(iii) Performance goals used to determine success of clinical
validation must be justified in the context of risks associated with
follow-up testing;
(iv) Objective performance measures (e.g., sensitivity,
specificity, positive predictive value or negative predictive value)
must be reported with relevant descriptive or developmental performance
measures. Summary level demographic information and sub-group analyses
must be provided for each study site, relevant demographic sub-groups,
and acquisition systems; and
(v) The test dataset must include a minimum of three geographically
diverse sites, separate from sites used in training of the model.
(2) Software verification, validation, and hazard analysis must be
performed. Software documentation must include:
(i) A description of the model/algorithm, algorithm inputs/outputs,
and supported patient population;
(ii) Integration testing in the intended software system or
software environment; and
(iii) A description of the expected impact of all applicable sensor
acquisition hardware characteristics on performance and any associated
hardware specifications, including:
(A) A description of input signal/data quality control measures;
and
(B) A description of all mitigations for user error or failure of
any subsystem components (including signal detection, signal analysis,
data display, and storage) on output accuracy.
(3) Human factors assessment of the intended users in the intended
use environment must evaluate the risk of misinterpretation of device
output.
(4) Labeling must include:
(i) A summary of the performance testing methods, tested hardware,
tested/supported patient population, results of the performance testing
for tested performance measures/metrics, summary-level descriptions of
patient demographics and associated subgroup analyses for training and
test datasets, and the expected minimum performance of the device;
(ii) Device limitations or subpopulations for which the device may
not perform as expected;
(iii) Warning that the user should not rely on the lack of a
suspected finding to rule out follow-up;
(iv) A statement that the device output should not replace a full
clinical evaluation of the patient and that the output may not be
sufficient as the sole basis for further testing;
(v) Warnings identifying sensor acquisition factors that may impact
measurement results;
(vi) Guidance for interpretation of the measurements and typical
follow-up testing; and
(vii) The type(s) of hardware sensor data used, including
specification of compatible sensors for data acquisition.
Grace R. Graham,
Deputy Commissioner for Policy, Legislation, and International Affairs.
[FR Doc. 2026-18612 Filed 9-10-26; 8:45 am]
BILLING CODE 4164-01-P
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