DeepRhythmAI is a cloud-based software that utilizes AI algorithms to assess cardiac arrhythmias using a single- or two-lead ECG data from adult patients. It is intended for use by a healthcare solution integrator to build web, mobile or another types of applications to let qualified healthcare professionals review and confirm the analytic result. The product supports downloading and analyzing data recorded in the compatible formats from ECG devices such as Holter, Event recorder, Outpatient Cardiac Telemetry devices or other similar recorders when the assessment of the rhythm is necessary. The product can be electronically interfaced and perform analysis with data transferred from other computer-based ECG systems, such as an ECG management system. DeepRhythmAI can be integrated into medical devices. In this case, the medical device manufacturer will identify the indication for use depending on the application of their device. DeepRhythmAI is not for use in life-supporting or sustaining systems or ECG Alarm devices. Interpretation results are not intended to be the sole means of diagnosis. It is offered to physicians and clinicians on an advisory basis only in conjunction with the physician's knowledge of ECG patterns, patient background, clinical history, symptoms and other diagnostic information.

Device Description

The DeepRhythmAI is a cloud-based software utilizing CNN and transformer models for automated analysis of ECG data. It uses a scalable Application Programming Interface (API) to enable easy integration with other medical products. The main component of DeepRhythmAI is an automated proprietary deep-learning algorithm, which measures and analyzes ECG data to provide qualified healthcare professional with supportive information for review. DeepRhythmAI can be integrated into medical devices. The product supports downloading and analyzing data recorded in compatible formats from ECG devices such as Holter, Event recorder, Outpatient Cardiac Telemetry devices or other similar recorders used when assessment of the rhythm is necessary. The DRAI can also be electronically interfaced and perform analysis with data transferred from other computer-based ECG systems, such as an ECG management system. DeepRhythmAI doesn't have User Interface therefore it should be integrated with the external visualization software used by the ECG technicians for the ECG visualization and analysis reporting.

AI/ML Overview

The provided FDA 510(k) clearance letter and summary for DeepRhythmAI offer general statements about performance testing but lack the specific details required to fully address all aspects of the request, especially quantifiable acceptance criteria and the results that prove them. The document primarily focuses on the substantial equivalence argument against a predicate device (which is itself DeepRhythmAI).

Based on the provided text, here's an attempt to extract and infer the information:

Acceptance Criteria and Device Performance:

The document mentions that the device was tested "according to the recognized consensus standards, ANSI/AAMI/IEC 60601-2-47:2012/(R)2016 and AAMI/ANSI/EC57:2012." These standards define performance requirements for ECG analysis devices, including aspects like beat detection accuracy, heart rate accuracy, and arrhythmia detection. However, the exact quantifiable acceptance criteria (e.g., "accuracy must be >X%") and the observed numeric device performance (e.g., "accuracy was Y%") are not reported in the provided text.

The closest we get to "reported performance" is the statement: "Overall, the software verification & validation testing was completed successfully and met all requirements. Testing demonstrated that the subject device performance was deemed to be acceptable." This is a qualitative statement, not quantitative performance data.

Table of Acceptance Criteria and Reported Device Performance:

Acceptance Criteria (Inferred from Standards)	Reported Device Performance (Not Quantified in Doc)
QRS detection accuracy (as per ANSI/AAMI standards)	Met all requirements; performance deemed acceptable.
Heart rate determination accuracy for non-paced adult (as per ANSI/AAMI standards)	Met all requirements; performance deemed acceptable.
R-R interval detection accuracy (as per ANSI/AAMI standards)	Met all requirements; performance deemed acceptable.
Non-paced arrhythmias interpretation accuracy (as per ANSI/AAMI standards)	Met all requirements; performance deemed acceptable.
Non-paced ventricular arrhythmias calls accuracy (as per ANSI/AAMI standards)	Met all requirements; performance deemed acceptable.
Atrial fibrillation detection accuracy (as per ANSI/AAMI standards)	Met all requirements; performance deemed acceptable.
Cardiac beats detection accuracy (Ventricular ectopic beats, Supraventricular ectopic beats) (as per ANSI/AAMI standards)	Met all requirements; performance deemed acceptable.
Cyber security requirements met	No vulnerabilities identified.
Software requirements satisfied	All software requirements satisfied.

Study Details:

Sample size used for the test set and the data provenance:
- Test Set Sample Size: The document states the algorithm was "tested against the proprietary database (MDG validation db) that includes a large number of recordings captured among the intended patient population." The exact number of recordings is not specified, only "a large number."
- Data Provenance: The data comes from a "proprietary database (MDG validation db)." The country of origin is not explicitly stated. The document indicates it includes data for both two-lead and single-lead patch recorders, implying diverse ECG device sources. It is implied to be retrospective data collected for validation purposes.
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- This information is not provided in the document. The document states a "proprietary database" was used for validation, but it does not detail how the ground truth within this database was established (e.g., by how many cardiologists or expert technicians, or their qualifications).
Adjudication method (e.g., 2+1, 3+1, none) for the test set:
- This information is not provided in the document.
If a multi-reader multi-case (MRMC) comparative effectiveness study was done, if so, what was the effect size of how much human readers improve with AI vs without AI assistance:
- A MRMC comparative effectiveness study involving human readers and AI assistance is not mentioned in the provided text. The study described focuses on the standalone performance of the device against a ground truth. The device "is offered to physicians and clinicians on an advisory basis only" and results are "not intended to be the sole means of diagnosis," indicating a human-in-the-loop context, but no study is presented to quantify this human-AI interaction's effect on reader performance.
If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Yes, a standalone performance study was done. The document states the algorithm was "tested against the proprietary database (MDG validation db)." The entire summary of performance data refers to evaluation of the "DeepRhythmAI software for arrhythmia detection and automated analysis of ECG data." There is no mention of human interaction during this performance evaluation.
The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- The document implies the use of an "MDG validation db" but does not specify the type of ground truth used to annotate this database. It's common for such ECG databases to rely on expert adjudicated annotations, but this is not explicitly stated.
The sample size for the training set:
- The sample size for the training set is not provided. The document only discusses the "MDG validation db" which is used for testing/validation.
How the ground truth for the training set was established:
- As the training set sample size is not provided, neither is information on how its ground truth was established.

Summary of Missing Information:

The provided document, being a 510(k) clearance letter and summary, serves to establish substantial equivalence. It confirms that specific performance testing was conducted according to recognized standards and deemed acceptable, but it does not provide the detailed scientific study results that would include:

Quantifiable acceptance criteria and the exact numeric performance results for each criterion.
The raw sample size of the test set.
Details on the experts involved in ground truth creation for the test set (number, qualifications, adjudication method).
Information on any MRMC studies or effect sizes of AI assistance on human readers.
Explicit details about the ground truth methodology for the validation database.
Any information regarding the training dataset (size, ground truth methodology).

To fully answer the request, one would typically need access to the full 510(k) submission, which contains the detailed V&V (Verification and Validation) reports.

Summary

FDA 510(k) Clearance Letter - DeepRhythmAI

Page 1

U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov

Doc ID # 04017.07.05

May 27, 2025

Medicalgorithmics S.A.
Agnieszka Romowicz
Product Compliance Manager
Aleje Jerozolimskie 81
Warsaw, 02-001
Poland

Re: K250932
Trade/Device Name: DeepRhythmAI
Regulation Number: 21 CFR 870.1425
Regulation Name: Programmable Diagnostic Computer
Regulatory Class: Class II
Product Code: DQK, DPS, QYX
Dated: March 28, 2025
Received: March 28, 2025

Dear Agnieszka Romowicz:

We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device"

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K250932 - Agnieszka Romowicz Page 2

(https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).

Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.

All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.

For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

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K250932 - Agnieszka Romowicz Page 3

Sincerely,

Jennifer W. Shih -S

Jennifer Kozen
Assistant Director
Division of Cardiac Electrophysiology,
Diagnostics, and Monitoring Devices
Office of Cardiovascular Devices
Office of Product Evaluation and Quality
Center for Devices and Radiological Health

Enclosure

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FORM FDA 3881 (8/23) Page 1 of 1 PSC Publishing Services (301) 443-6740 EF

DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration

Indications for Use

Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.

510(k) Number (if known): K250932
Device Name: DeepRhythmAI

Indications for Use (Describe)

The product can be electronically interfaced and perform analysis with data transferred from other computer-based ECG systems, such as an ECG management system. DeepRhythmAI can be integrated into medical devices. In this case, the medical device manufacturer will identify the indication for use depending on the application of their device.

DeepRhythmAI is not for use in life-supporting or sustaining systems or ECG Alarm devices. Interpretation results are not intended to be the sole means of diagnosis. It is offered to physicians and clinicians on an advisory basis only in conjunction with the physician's knowledge of ECG patterns, patient background, clinical history, symptoms and other diagnostic information.

Type of Use (Select one or both, as applicable)

☒ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)

CONTINUE ON A SEPARATE PAGE IF NEEDED.

This section applies only to requirements of the Paperwork Reduction Act of 1995.

DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.

The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:

Department of Health and Human Services
Food and Drug Administration
Office of Chief Information Officer
Paperwork Reduction Act (PRA) Staff
PRAStaff@fda.hhs.gov

"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."

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Traditional 510(k) Premarket Notification

DeepRhythmAI

510(k) Summary

March 28, 2025

510(k) Summary

I. Submitter's name and address:

Medicalgorithmics S.A.
Aleje Jerozolimskie 81,
02-001 Warsaw, Poland

Contact Person:
Agnieszka Romowicz
Phone: (+48) 733 888 448
Email: a.romowicz@medicalgorithmics.com

Date Prepared: 2025-03-28

II. Device

Trade name: DeepRhythmAI
Common name: ECG Analysis System
Classification name: Programmable Diagnostic Computer/Electrocardiograph/Outpatient Cardiac Telemetry
Regulation number: 870.1425, 870.2340, 870.1025
Regulatory Class: Class II
Classification Product code: DQK, DPS, QYX

III. Substantial Equivalence

The selected predicate device is:

DeepRhythmAI, K241197 (Predicate Device)

No reference devices were used in this submission.

IV. Device description

K250932
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analyzes ECG data to provide qualified healthcare professional with supportive information for review.

DeepRhythmAI can be integrated into medical devices. The product supports downloading and analyzing data recorded in compatible formats from ECG devices such as Holter, Event recorder, Outpatient Cardiac Telemetry devices or other similar recorders used when assessment of the rhythm is necessary. The DRAI can also be electronically interfaced and perform analysis with data transferred from other computer-based ECG systems, such as an ECG management system. DeepRhythmAI doesn't have User Interface therefore it should be integrated with the external visualization software used by the ECG technicians for the ECG visualization and analysis reporting.

DeepRhythmAI is not for use in life supporting or sustaining systems or ECG Alarm devices. Interpretation results are not intended to be the sole means of diagnosis. It is offered to physicians and clinicians on an advisory basis only in conjunction with the physician's knowledge of ECG patterns, patient background, clinical history, symptoms, and other diagnostic information.

V. Indications for use

DeepRhythmAI is a cloud-based software that utilizes AI algorithms to assess cardiac arrhythmias using a single- or two-lead ECG data from adult patients.

It is intended for use by a healthcare solution integrator to build web, mobile or another types of applications to let qualified healthcare professionals review and confirm the analytic result. The product supports downloading and analyzing data recorded in the compatible formats from ECG devices such as Holter, Event recorder, Outpatient Cardiac Telemetry devices or other similar recorders when the assessment of the rhythm is necessary.

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VI. Comparison to predicate device

The following tables provide a comparison of the detection features and device comparison of DeepRhythmAI and the predicate device.

Detection Features comparison:

Device functionality	Subject device (-)	Predicate device (K241197)
	DeepRhythmAI	DeepRhythmAI
QRS detection	YES	YES
Heart rate determination for non-paced adult	YES	YES
R-R interval detection	YES	YES
Non-paced arrhythmias interpretation	YES	YES
Non-paced ventricular arrhythmias calls	YES	YES
Atrial fibrillation detection	YES	YES
Cardiac beats detection (Ventricular ectopic beats, Supraventricular ectopic beats)	YES	YES
Patient populations	Adult	Adult

Device comparison:

Device functionality	Subject device	Predicate device	Similarities/Differences
	DeepRhythmAI (DRAI)	DeepRhythmAI (DRAI)
Manufacturer	Medicalgorithmics S.A.	Medicalgorithmics S.A.	N/A
510(k) Number	---	K241197	N/A
Classification	Class II	Class II	Equivalent
Regulation Number(s)	21 CFR §870.1425, 21 CFR §870.2340, 21 CFR §870.1025	21 CFR §870.1425, 21 CFR §870.2340, 21 CFR §870.1025	Equivalent
Classification name	Programmable Diagnostic Computer,	Programmable Diagnostic Computer,	Equivalent

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Device functionality	Subject device	Predicate device	Similarities/Differences
	DeepRhythmAI (DRAI)	DeepRhythmAI (DRAI)
	Electrocardiograph. Outpatient Cardiac Telemetry	Electrocardiograph, Outpatient Cardiac Telemetry
Product Code	DQK, DPS, QYX	DQK, DPS, QYX	Equivalent
Indications for Use	DeepRhythmAI is a cloud-based software that utilizes AI algorithms to assess cardiac arrhythmias using a single- or two-lead ECG data from adult patients. It is intended for use by a healthcare solution integrator to build web, mobile or another types of applications to let qualified healthcare professionals review and confirm the analytic result. The product supports downloading and analyzing data recorded in the compatible formats from ECG devices such as Holter, Event recorder, Outpatient Cardiac Telemetry devices or other similar recorders when the assessment of the rhythm is necessary. The product can be electronically interfaced and perform analysis with data transferred from other computer-based ECG systems, such as an ECG management system. DeepRhythmAI can be integrated into medical devices. In this case, the medical device manufacturer will identify the indication for use depending on the	DeepRhythmAI is a cloud-based software for the assessment of cardiac arrhythmias using two lead ECG data in adult patients. It is intended for use by a healthcare solution integrator to build web, mobile or another types of applications to let qualified healthcare professionals review and confirm the analytic result. The product supports downloading and analyzing data recorded in the compatible formats from dedicated ambulatory ECG devices such as Holter, event recorder, Outpatient Cardiac Telemetry devices or other similar recorders when the assessment of the rhythm is necessary. The product can be electronically interfaced and perform analysis with data transferred from other computer-based ECG systems, such as an ECG management system. DeepRhythmAI can be integrated into medical devices. In this case, the medical device manufacturer will identify the indication for use depending on the	Similar, DRAI's ECG analysis capabilities were expanded to include single-lead patch recorders placed on the upper mid/left chest.

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Device functionality	Subject device	Predicate device	Similarities/Differences
	DeepRhythmAI (DRAI)	DeepRhythmAI (DRAI)
	application of their device. DeepRhythmAI is not for use in life-supporting or sustaining systems or ECG Alarm devices. Interpretation results are not intended to be the sole means of diagnosis. It is offered to physicians and clinicians on an advisory basis only in conjunction with the physician's knowledge of ECG patterns, patient background, clinical history, symptoms and other diagnostic information.	application of their device. DeepRhythmAI is not for use in life-supporting or sustaining systems or ECG Alarm devices. Interpretation results are not intended to be the sole means of diagnosis. It is offered to physicians and clinicians on an advisory basis only in conjunction with the physician's knowledge of ECG patterns, patient background, clinical history, symptoms and other diagnostic information.
Documentation Level Evaluation	Enhanced Documentation Level	Enhanced Documentation Level	Equivalent
Components	Software only: 1) A web API 2) An automated proprietary algorithm.	Software only: 1) A web API 2) An automated proprietary algorithm.	Equivalent
Interface	Web application programming interface (API)	Web application programming interface (API)	Equivalent
Part responsible for ECG signal analysis	The automated proprietary deep-learning algorithm, which measures and analyzes ECG data to provide qualified healthcare professional with supportive information for review.	The automated proprietary deep-learning algorithm, which measures and analyzes ECG data to provide qualified healthcare professional with supportive information for review.	Equivalent
Display or Graphical User Interface (GUI)	No primary display or GUI	No primary display or GUI	Equivalent

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Device functionality	Subject device	Predicate device	Similarities/Differences
	DeepRhythmAI (DRAI)	DeepRhythmAI (DRAI)
	application of their device. DeepRhythmAI is not for use in life-supporting or sustaining systems or ECG Alarm devices. Interpretation results are not intended to be the sole means of diagnosis. It is offered to physicians and clinicians on an advisory basis only in conjunction with the physician's knowledge of ECG patterns, patient background, clinical history, symptoms and other diagnostic information.	application of their device.
Documentation Level Evaluation	Enhanced Documentation Level	DeepRhythmAI is not for use in life-supporting or sustaining systems or ECG Alarm devices. Interpretation results are not intended to be the sole means of diagnosis. It is offered to physicians and clinicians on an advisory basis only in conjunction with the physician's knowledge of ECG patterns, patient background, clinical history, symptoms and other diagnostic information. Enhanced Documentation Level	Equivalent
Components	Software only: 1) A web API 2) An automated proprietary algorithm.	Software only: 1) A web API 2) An automated proprietary algorithm.	Equivalent
Interface	Web application programming interface (API)	Web application programming interface (API)	Equivalent
Part responsible for ECG signal analysis	The automated proprietary deep-learning algorithm, which measures and analyzes ECG data to provide qualified healthcare professional with supportive information for review.	The automated proprietary deep-learning algorithm, which measures and analyzes ECG data to provide qualified healthcare professional with supportive information for review.	Equivalent
Display or Graphical User Interface (GUI)	No primary display or GUI	No primary display or GUI	Equivalent

Device comparison summary:

The algorithm responsible for arrhythmia detection has been modified, so that it can process two-lead and single-lead ECG recordings Performance data was evaluated on the same requirements according to the international standards ANSI/AAMI EC57:2012 and ANSI/AAMI/IEC 60601-2-47:2012/(R)2016 as the predicate device. Patient population and monitoring environments for devices are equivalent.

The subject device is considered substantially equivalent to the predicate devices.

VII. Summary of performance data

The DeepRhythmAI software for arrhythmia detection and automated analysis of ECG data has been subjected to performance testing according to the recognized consensus standards, ANSI/AAMI/IEC 60601-2-47:2012/(R)2016 and AAMI/ANSI/EC57:2012, Moreover, to enable robust device validation, the algorithm was tested against the proprietary database (MDG validation db) that includes a large number of recordings captured among the intended patient population. MDG validation database allowed for validation of additional compatible hardware configurations including two-leads recorders and single lead patch recorders located on the upper, mid/left chest.

Medicalgorithmics followed ANSI/AAMI/IEC 62304 and the FDA Guidance Document, "General Principles of Software Validation; Final Guidance for Industry and FDA Staff" (January, 2002) with respect to software development and validation. Unit, integration and system level testing conducted identified no residual anomalies during verification software tests. Cybersecurity testing was conducted in which no vulnerabilities were identified and all software requirements were satisfied. Overall, the software verification & validation testing was completed successfully and met all requirements. Testing demonstrated that the subject device performance was deemed to be acceptable.

VIII. Conclusion

In conclusion, DeepRythmAI has extended the intended use compared to the predicate device as it allows for two-lead and single-lead data processing but it can be still concluded that any differences in technological characteristics do not raise different questions of safety or effectiveness. compared to the predicate device. Therefore, DRAI is substantially equivalent to the predicate device.

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Regulation Number and Section

§ 870.1425 Programmable diagnostic computer.

(a)
Identification. A programmable diagnostic computer is a device that can be programmed to compute various physiologic or blood flow parameters based on the output from one or more electrodes, transducers, or measuring devices; this device includes any associated commercially supplied programs.(b)
Classification. Class II (performance standards).