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

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 professionals 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 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.

DRAI consists of:

An API which allows the client to upload single- or two-lead ECG data and allows to download the results of the ECG analysis.
The automated proprietary deep-learning algorithm, which measures and analyzes ECG data to provide qualified healthcare professional with supportive information for review.

DRAI works in the following sequence:

Accept uploading digital ECG files via secure API;
Analyze the uploaded ECG data using a proprietary algorithm, which detects cardiac beats/arrhythmias and intervals including:
- QRS
- Heart rate determination
- RR Interval measurements
- Non-paced supraventricular rhythm and arrhythmia calls as specified by product's Instruction for Use
- Non-paced ventricular rhythm and arrhythmia calls: as specified by product's Instruction for Use
- Atrioventricular blocks (second or third degree)
Analyze detected individual Ventricular ectopic beats also known as Premature Ventricular Contractions (PVCs) to form groups and subgroups of similar beat morphology if product is configured to do so.
The results of the ECG analysis can be downloaded via secure API by the external visualization software used by healthcare professionals for the ECG visualization and analysis reporting.

AI/ML Overview

This document describes the acceptance criteria and the study proving the device meets these criteria for DeepRhythmAI, a cloud-based software that utilizes AI algorithms to assess cardiac arrhythmias.

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly list quantitative acceptance criteria in terms of specific performance metrics (e.g., sensitivity, specificity, accuracy thresholds). Instead, it states that the device's performance was evaluated against recognized consensus standards and a proprietary database, and that the PVC grouping algorithm meets "predefined requirements for accuracy." Without specific numerical targets, the table below will summarize the types of performance evaluations conducted and the reported outcomes as described.

Feature/Metric Evaluated	Acceptance Criteria (Implicit from standards/statements)	Reported Device Performance
General ECG Analysis	Compliance with ANSI/AAMI/IEC 60601-2-47:2012/(R)2016 and AAMI/ANSI/EC57:2012 standards for ECG analysis.	Subjected to performance testing according to these recognized consensus standards.
QRS detection	Implied high accuracy for QRS detection as per standards.	"YES" - feature is present and presumably performs acceptably.
Heart rate determination for non-paced adult	Implied high accuracy for heart rate determination as per standards.	"YES" - feature is present and presumably performs acceptably.
R-R interval detection	Implied high accuracy for R-R interval detection as per standards.	"YES" - feature is present and presumably performs acceptably.
Non-paced arrhythmias interpretation	Implied high accuracy for non-paced arrhythmias interpretation as per standards.	"YES" - feature is present and presumably performs acceptably.
Non-paced ventricular arrhythmias calls	Implied high accuracy for non-paced ventricular arrhythmias calls as per standards.	"YES" - feature is present and presumably performs acceptably.
Atrial fibrillation detection	Implied high accuracy for AF detection as per standards.	"YES" - feature is present and presumably performs acceptably.
Cardiac beats detection (Ventricular ectopic beats, Supraventricular ectopic beats)	Implied high accuracy for beat detection as per standards.	"YES" - feature is present and presumably performs acceptably.
PVC Morphology grouping	Meets predefined requirements for accuracy when clustering individual PVCs into groups of similar morphology.	PVC grouping algorithm meets predefined requirements for accuracy. Tested via "performance validation testing for a hierarchical Premature Ventricular Contraction (PVC) clustering algorithm."
Software Quality & Cybersecurity	Compliance with ANSI/AAMI/IEC 62304 and FDA Guidance "General Principles of Software Validation"; No residual anomalies; No cybersecurity vulnerabilities.	Unit, integration, and system level testing conducted identified no residual anomalies. Cybersecurity testing conducted found no vulnerabilities. All software requirements satisfied.

2. Sample Size for the Test Set and Data Provenance

The 510(k) summary states that "the algorithm was tested against the proprietary database (MDG validation db) that includes a large number of recordings captured among the intended patient population."

Test Set Sample Size: The exact numerical sample size for the test set is not specified beyond "a large number of recordings."
Data Provenance:
- Country of Origin: Not explicitly stated. It refers to a "proprietary database (MDG validation db)."
- Retrospective or Prospective: Not explicitly stated. Given it's a "validation db," it's likely retrospective data collected over time.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

This information is not provided in the given 510(k) clearance letter. The document mentions "qualified healthcare professionals review and confirm the analytic result" in the context of the device's intended use and that the AI provides "supportive information for review." However, it does not detail how ground truth was established for the validation dataset, nor the number or qualifications of experts involved in that process.

4. Adjudication Method for the Test Set

The adjudication method used for establishing the ground truth for the test set is not provided in the document.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

An MRMC comparative effectiveness study, comparing human readers with AI assistance versus without AI assistance, is not explicitly mentioned or described in the provided 510(k) summary. The device's indication for use states that "Interpretation results are not intended to be the sole means of diagnosis. It is offered to physicians and clinicians on an advisory basis only," suggesting it functions as an assistive tool, but a formal MRMC study demonstrating improvement is not detailed.

6. Standalone (Algorithm Only) Performance Study

Yes, a standalone performance study was done. The document states, "the algorithm was tested against the proprietary database (MDG validation db)" and that DeepRhythmAI "measures and analyzes ECG data to provide qualified healthcare professional with supportive information for review." The performance assessment of the "automated proprietary deep-learning algorithm" and the "hierarchical Premature Ventricular Contraction (PVC) clustering algorithm" implies a standalone evaluation of the algorithm's capabilities.

7. Type of Ground Truth Used for the Test Set

The type of ground truth used is not explicitly stated. However, given the nature of ECG analysis for arrhythmias, it is highly probable that the ground truth was established through expert consensus or manual expert annotation of the ECG recordings in the "proprietary database (MDG validation db)."

8. Sample Size for the Training Set

The sample size for the training set is not provided in the document. The document mentions the use of "CNN and transformer models for automated analysis of ECG data," which implies a machine learning approach requiring a training set, but its size is not disclosed.

9. How the Ground Truth for the Training Set Was Established

The method for establishing ground truth for the training set is not provided in the document. As with the test set, it is likely that expert consensus or manual expert annotation was used to label the data for training the deep learning algorithms.

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 # 04017.08.02

December 11, 2025

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

Re: K253141
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: September 25, 2025
Received: September 25, 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.

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K253141 - Agnieszka Romowicz Page 2

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" (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-

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K253141 - Agnieszka Romowicz Page 3

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).

Sincerely,

JENNIFER W. SHIH -S

Jennifer Kozen
Assistant Director
Division of Cardiac Electrophysiology,
Diagnostics, and Monitoring Devices
OHT2: Office of Cardiovascular Devices
Office of Product Evaluation and Quality
Center for Devices and Radiological Health

Enclosure

Page 4

Indications for Use

Please type in the marketing application/submission number, if it is known. This textbox will be left blank for original applications/submissions.

K253141

Please provide the device trade name(s).

DeepRhythmAI

Please provide your Indications for Use below.

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.

Please select the types of uses (select one or both, as applicable).
☑ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)

Page 5

Traditional 510(k) Premarket Notification

DeepRhythmAI

510(k) Summary

K253141

Page 1 of 8

September 25, 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: +1 (302) 261 5184
Mobile: (+48) 733 888 448
Email: a.romowicz@medicalgorithmics.com

Date Prepared: 2025-09-25

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, K250932 (Predicate Device)

No reference devices were used in this submission.

Page 6

Traditional 510(k) Premarket Notification

DeepRhythmAI

510(k) Summary

K253141

510(k) Summary

I. Submitter's name and address:

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

Contact Person:
Agnieszka Romowicz
Phone: +1 (302) 261 5184
Mobile: (+48) 733 888 448
Email: a.romowicz@medicalgorithmics.com

Date Prepared: 2025-09-25

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, K250932 (Predicate Device)

No reference devices were used in this submission.

Page 1 of 8

Page 7

Traditional 510(k) Premarket Notification

DeepRhythmAI

510(k) Summary

K253141

Page 2 of 8

IV. Device description

DRAI consists of:

An API which allows the client to upload single- or two-lead ECG data and allows to download the results of the ECG analysis.
The automated proprietary deep-learning algorithm, which measures and analyzes ECG data to provide qualified healthcare professional with supportive information for review.

DRAI works in the following sequence:

Accept uploading digital ECG files via secure API;
Analyze the uploaded ECG data using a proprietary algorithm, which detects cardiac beats/arrhythmias and intervals including:
- QRS
- Heart rate determination
- RR Interval measurements
- Non-paced supraventricular rhythm and arrhythmia calls as specified by product's Instruction for Use
- Non-paced ventricular rhythm and arrhythmia calls: as specified by product's Instruction for Use

Page 8

Traditional 510(k) Premarket Notification

DeepRhythmAI

510(k) Summary

K253141

IV. Device description

DRAI consists of:

An API which allows the client to upload single- or two-lead ECG data and allows to download the results of the ECG analysis.
The automated proprietary deep-learning algorithm, which measures and analyzes ECG data to provide qualified healthcare professional with supportive information for review.

DRAI works in the following sequence:

Accept uploading digital ECG files via secure API;
Analyze the uploaded ECG data using a proprietary algorithm, which detects cardiac beats/arrhythmias and intervals including:
- QRS
- Heart rate determination
- RR Interval measurements
- Non-paced supraventricular rhythm and arrhythmia calls as specified by product's Instruction for Use
- Non-paced ventricular rhythm and arrhythmia calls: as specified by product's Instruction for Use

Page 2 of 8

Page 9

Traditional 510(k) Premarket Notification

DeepRhythmAI

510(k) Summary

K253141

Page 3 of 8

Atrioventricular blocks (second or third degree)

Analyze detected individual Ventricular ectopic beats also known as Premature Ventricular Contractions (PVCs) to form groups and subgroups of similar beat morphology if product is configured to do so.
The results of the ECG analysis can be downloaded via secure API by the external visualization software used by healthcare professionals for the ECG visualization and analysis reporting.

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.

Page 3 of 8

Page 10

Traditional 510(k) Premarket Notification

DeepRhythmAI

510(k) Summary

K253141

Page 4 of 8

VI. Comparison to predicate device

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

Detection Features comparison:

Device functionality	Subject device (K253141)	Predicate device (K250932)
	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
PVC Morphology grouping	YES	NO
Patient populations	Adult	Adult

Page 4 of 8

Page 11

Traditional 510(k) Premarket Notification

DeepRhythmAI

510(k) Summary

K253141

The following table provides a comparison of the intended use and device characteristics of the proposed DeepRhythmAI device and predicate device:

Device functionality	Subject device K253141	Predicate device (K250932)	Similarities/Differences
	DeepRhythmAI (DRAI)	DeepRhythmAI (DRAI)
Manufacturer	Medicalgorithmics S.A.	Medicalgorithmics S.A.	N/A
510(k) Number	---	K250932	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, Electrocardiograph, Outpatient Cardiac Telemetry	Programmable Diagnostic Computer, Electrocardiograph, Outpatient Cardiac Telemetry	Equivalent
Product Code	DQK, DPS, QYX	DQK, DPS, QYX	Equivalent
Indications for Use	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	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	Equivalent

Page 5 of 8

Page 12

Traditional 510(k) Premarket Notification

DeepRhythmAI

510(k) Summary

K253141

Page 6 of 8

Device functionality	Subject device K253141	Predicate device (K250932)	Similarities/Differences
	DeepRhythmAI (DRAI)	DeepRhythmAI (DRAI)

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.

| 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 | The automated proprietary deep-learning algorithm, which measures and analyzes ECG data to provide qualified healthcare professional with | Equivalent |

Page 6 of 8

Page 13

Traditional 510(k) Premarket Notification

DeepRhythmAI

510(k) Summary

K253141

Device functionality	Subject device K253141	Predicate device (K250932)	Similarities/Differences
	DeepRhythmAI (DRAI)	DeepRhythmAI (DRAI)

Page 6 of 8

Page 14

Traditional 510(k) Premarket Notification

DeepRhythmAI

510(k) Summary

K253141

Device functionality	Subject device K253141	Predicate device (K250932)	Similarities/Differences
	DeepRhythmAI (DRAI)	DeepRhythmAI (DRAI)

supportive information for review. | supportive information for review. |

Device comparison summary:

Indications for Use in both predicate and subject device are the same. The automated proprietary deep-learning algorithm which is responsible for ECG signal analysis remains unchanged. The only difference is an additional feature added as the ability to cluster individual PVCs into groups of similar morphology.

The DRAI device's technological characteristics are like those of the cleared predicate device.

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

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. Moreover, the DeepRhythmAI has been subjected to performance validation testing for a hierarchical Premature Ventricular Contraction (PVC) clustering algorithm. It shows that PVC grouping algorithm meets predefined requirements for accuracy when clustering individual PVCs into groups of similar morphology from electrocardiogram (ECG) data.

Page 7 of 8

Page 15

Traditional 510(k) Premarket Notification

DeepRhythmAI

510(k) Summary

K253141

Page 8 of 8

Furthermore, 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 based on the Intended Use, Indications for Use, product technical information, performance evaluation, and standards compliance provided in this premarket notification, the DeepRythmAI has been shown to be substantially equivalent to the cited predicate.

Page 8 of 8

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).