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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.

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:

1. An API which allows the client to upload single- or two-lead ECG data and allows to download the results of the ECG analysis.
2. 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:

1. Accept uploading digital ECG files via secure API;

2. 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)

3. 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.

4. 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.

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.

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.

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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.

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.

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:

Study Details:

1. 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.

2. 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).

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

   • This information is not provided in the document.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

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DeepRhythmAl 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. DeepRhythmAl 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.

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.

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 compatible formats from dedicated ambulatory 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. DeepRhythmAl 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.

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.

The provided document is a 510(k) Substantial Equivalence Determination letter from the FDA regarding the DeepRhythmAI device. It outlines the FDA's decision but does not contain detailed performance study data such as specific acceptance criteria and reported numeric device performance, sample sizes used for test and training sets, the number and qualifications of experts for ground truth, adjudication methods, MRMC study details, or the specific type of ground truth used.

The document states that "DeepRhythmAI 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." It also mentions "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." However, the specific results of these tests are not provided in this letter.

Therefore, many of the requested details cannot be extracted from the provided text.

Based on the information available:

1. Table of Acceptance Criteria and Reported Device Performance:

The document generaly states that the device was tested against mentioned standards and that "Test results confirm that DeepRhythmAl meets its intended use." However, specific numerical acceptance criteria and the corresponding reported performance values (e.g., sensitivity, specificity, accuracy for specific arrhythmias) are not provided in this document.

2. Sample size used for the test set and the data provenance:

• Test Set Sample Size: "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 (sample size) is not specified.
• Data Provenance: The data is from a "proprietary database (MDG validation db)." The country of origin and whether it's retrospective or prospective data are not specified.

3. 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.

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

This information is not provided in the document.

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:

This type of study is not mentioned in the document. The device is described as "cloud-based software for the assessment of cardiac arrhythmias... 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." This suggests an AI-assisted workflow, but no MRMC study details are given.

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

The document states that "DeepRhythmAl is a cloud-based software for automated analysis of ECG data. 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." And the performance testing was done for "arrhythmia detection and automated analysis of ECG data," which implies standalone performance was evaluated against the mentioned standards. Specific standalone performance metrics are not provided.

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

While it's implied that ground truth was established to validate the algorithm against standards, the specific type of ground truth (e.g., expert consensus of specific cardiologists, adjudicated clinical events) is not explicitly stated.

8. The sample size for the training set:

This information is not provided in the document. The document only mentions "proprietary deep-learning algorithm" implying a training process, but no details of the training set.

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

This information is not provided in the document.

Ask a specific question about this device

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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