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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, Mobile Cardiac Telemetry or other similar devices when tof 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 on an advisory basis only in conjunction with the physician's knowledge of ECG patterns, patient background, clinical history, symptoms and other diagnostic information.

The DeepRhythmAl is a cloud-based software 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 DeepRhythmAl is an automated proprietary deep-learning algorithm, which measures and analyzes ECG data to provide qualified healthcare professional with supportive information for review.

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

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. .

DeepRhythmAI algorithm detects cardiac beats/arrythmias and intervals including:

• . QRS
• Heart rate determination
• RR Interval measurements
• Non-paced arrhythmias
• Non-paced ventricular arrhythmia calls
• Ventricular ectopic beats
• Supraventricular ectopic beats

DeepRhythmAl returns the interpretation result to be reviewed by a qualified healthcare professional. DeepRhythmAl when integrated with the other computer-based ECG systems, creates a semi-autonomous system for analysis of ECG recordings. All algorithm annotations must be analyzed and confirmed by a qualified healthcare professional. The subject device can only be integrated with the display product used by the monitoring center that allows for verification of the algorithm output, its correction and confirmation.

DeepRhythmAl 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.

Here's an analysis of the provided text, focusing on the acceptance criteria and study information for the DeepRhythmAI device:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding device performance for specific metrics (e.g., sensitivity, specificity for arrhythmia detection) as one might find in a detailed clinical performance study report. Instead, it states that the device was subjected to performance testing according to recognized consensus standards.

Acceptance Criteria (Implicit from Standards and General Statements):

Reported Device Performance:

The document states that "All necessary testing was conducted on the DeepRhythmAl to support a determination of substantial equivalence to the predicate and reference devices. Test results confirm that DeepRhythmAl meets its intended use." However, specific quantitative performance metrics (e.g., sensitivity, specificity, accuracy, positive predictive value, negative predictive value for different arrhythmias) are not provided in this summary.

2. Sample Size Used for the Test Set and Data Provenance

The document does not provide details on the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective nature of the data). It only mentions that performance testing was conducted according to specific standards.

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

The document does not specify the number of experts used or their qualifications for establishing ground truth for any test set.

4. Adjudication Method for the Test Set

The document does not mention any adjudication method (e.g., 2+1, 3+1, none) used for the test set.

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

The document does not indicate that a multi-reader multi-case (MRMC) comparative effectiveness study was performed or any effect size of human readers improving with AI vs. without AI assistance. The device is explicitly stated to not be for standalone diagnosis and requires review by a qualified healthcare professional.

6. Standalone (Algorithm Only) Performance Study

The document states: "All algorithm annotations must be analyzed and confirmed by a qualified healthcare professional." and "Interpretation results are not intended to be the sole means of diagnosis. It is offered to physicians and clinicians on an advisory basis only..." These statements strongly suggest that the device is not intended or validated for standalone performance. Its integration with human review is a fundamental aspect of its intended use.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used (e.g., expert consensus, pathology, outcomes data, etc.) for its performance testing. Given its function, it is highly probable that expert-annotated ECG data would be used, but this is not confirmed in the provided text.

8. Sample Size for the Training Set

The document does not provide details on the sample size used for the training set for the DeepRhythmAI's deep-learning algorithm.

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

The document does not provide details on how the ground truth for the training set was established.

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The Zio Monitor is a prescription-only, single-patient-use, ECG monitor that continuously records data for up to 14 days. It is indicated for use on patients who may be asymptomatic or who may suffer from transient such as palpitations, shortness of breath, dizziness, lightheadedness, pre-syncope, syncope, fatigue or anxiety.

The Zio monitor is a non-sterile, single-patient-use, long-term ambulatory ECG monitor that is adhered to a patient's left pectoral region in a modified Lead II orientation. The goal of the Zio monitor is to help physicians initiate long-term, patient-compliant ECG monitoring utilizing proprietary technology.

The Zio monitor is applied and activated by the patient at home or at a clinic. Once activated, the device provides continuous, uninterrupted ECG recording into memory with minimal patient interaction. There is a button on the surface of the Zio monitor, which serves to activate the device and may be pressed by the patient during wear to indicate when he or she is experiencing a symptom. Additionally, there is a surface LED light that blinks green to confirm proper activation or that the device is working, and orange to indicate loss of connection with the skin or the presence of error conditions.

Although the provided document is a 510(k) summary for the Zio Monitor, it explicitly states that no clinical testing was performed in support of this premarket notification. Therefore, the document does not contain information about acceptance criteria based on clinical performance, a study proving device performance against such criteria, sample sizes for test sets, expert qualifications, adjudication methods, MRMC studies, standalone performance, or grand truth details for either test or training sets.

The document focuses on demonstrating substantial equivalence to a predicate device (Zio XT Patch) through nonclinical testing, changes in technological characteristics (reduced weight and power), and adherence to recognized consensus standards and guidance documents.

Here's an analysis of the provided information, noting the absence of the requested clinical performance details:

1. A table of acceptance criteria and the reported device performance

   • The document states: "There are no required FDA performance standards for the Zio monitor. All necessary performance testing was conducted on the Zio monitor to ensure performance as intended per specifications and to support a determination of substantial equivalence to the predicate device."
   • It lists several nonclinical tests (Mechanical verification, Biocompatibility, Firmware verification, Electrical safety and EMC, and packaging tests conforming to ASTM standards) and mentions conformance to various ISO/AAMI/IEC standards (Table 2).
   • However, specific quantitative acceptance criteria and their corresponding reported device performance values from these nonclinical tests are not detailed in this summary. The summary only asserts that the device meets these standards and specifications.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

   • Not applicable / Not provided. No clinical test set was used as no clinical testing was performed.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

   • Not applicable / Not provided. No clinical test set requiring expert ground truth was used.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

   • Not applicable / Not provided. No clinical test set requiring adjudication was used.

5. 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

   • Not applicable / Not provided. No clinical testing, including MRMC studies, was performed. The device itself is an ECG monitor, not explicitly described as having AI assistance for human readers in this document.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

   • Not applicable / Not provided. No clinical or algorithm-only performance study was conducted or reported in this summary.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

   • Not applicable / Not provided. No clinical ground truth was established as no clinical testing was performed. For the engineering/nonclinical tests, the "ground truth" would be the specifications and standards themselves.

8. The sample size for the training set

   • Not applicable / Not provided. No machine learning or AI algorithm requiring a training set is discussed or implied to have been evaluated clinically in this document.

9. How the ground truth for the training set was established

   • Not applicable / Not provided. As no training set is mentioned, its ground truth establishment is also not applicable.

In summary, the provided 510(k) summary for the Zio Monitor focuses on demonstrating substantial equivalence through nonclinical conformity and engineering specifications rather than clinical performance data. Therefore, the requested information regarding acceptance criteria derived from clinical studies and their supporting evidence is not present in this document.

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