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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.

Ask a specific question about this device

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.

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The DeepRhythm Platform is intended for use by qualified healthcare professionals for the assessment of arthythmias using ECG data in adult patients.

The product supports downloading and analyzing data recorded in compatible formats from 1-channel and 2-channel ambulatory ECG recording devices used for the arthythmia diagnostics such as Holter, event recorder or other similar devices when retrospective assessment of the rhythm is necessary.

The DeepRhythm Platform is intended for the storage, analysis, visualization, review and reporting of arthythmias. The DeepRhythm Platform utilizes DeepRhythmAl (FDA-cleared device) for QRS and arrhythmia detection on the received signal. The DeepRhythm Platform provides further ECG signal annotations processing on a beat-by-beat basis, heart rate measurement and rhythm analysis, for both symptomatic and asymptomatic events.

The DeepRhythm Platform is not for use in life supporting or sustaining systems or ECG monitor and Alarm devices. The product can be integrated with computerized ECG monitoring devices compatible with DeepRhythmAI.

The DeepRhythm Platform 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 DeepRhythm Platform system is a cloud-based software with microservice architecture and browser-based user interface. It provides arrhythmia diagnosis process and monitoring session management capabilities.

The DeepRhythm Platform's functionality consists of:

• enrolling a patient for an ECG monitoring session in a medical facility (performed . by Healthcare Professionals users),
• receiving the signal from a compatible monitoring device worn by the patient, .
• using an external AI service for signal analysis and classification (not a part of the . subject device),
• processing signal and annotations received from the external Al service, including . statistics computation,
• · reviewing the signal and annotations by an ECG Technician user,
• generating and publishing a report for a single ECG episode (Urgent report) and . for the entire monitoring session (End of Study report), performed by an ECG Technician user,
• reviewing a published report back at the medical facility that ordered the monitoring . session in the first place (performed by a Physician user).

The provided text does not contain detailed acceptance criteria or a comprehensive study report proving the device meets specific performance criteria. It primarily focuses on the regulatory submission information for FDA 510(k) clearance, asserting substantial equivalence to a predicate device.

Therefore, I cannot populate all the requested information. However, I can extract what is available and note the missing information.

Here's an analysis based on the provided text:

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

The document does not provide a specific table of acceptance criteria with corresponding performance metrics. It generally states that "All necessary testing was conducted on the DeepRhythm Platform to support a determination of substantial equivalence to the predicate device. Test results confirm that DeepRhythm Platform meets its intended use."

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

This information is not provided in the document.

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)

This information is not provided in the document. The document mentions that the DeepRhythm Platform "utilizes DeepRhythmAI (FDA-cleared device) for QRS and arrhythmia detection on the received signal." This suggests that the AI component's ground truth establishment might have been part of its own prior clearance, but details for the DeepRhythm Platform's specific testing are absent.

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 information is not provided in the document. The DeepRhythm Platform's role is described as "interpretation results are not intended to be the sole means of diagnosis. It is offered to physicians and clinicians on an advisory basis only." This suggests it's an assistive tool, but a comparative effectiveness study with human readers is not detailed.

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

The document states that "DeepRhythm Platform utilizes DeepRhythmAI (FDA-cleared device) for QRS and arrhythmia detection on the received signal." This implies that the core algorithmic performance for detection is handled by the pre-cleared DeepRhythmAI. The DeepRhythm Platform "provides further ECG signal annotations processing on a beat-by-beat basis, heart rate measurement and rhythm analysis," and processes statistics from DeepRhythmAI. However, specific standalone performance metrics for the DeepRhythm Platform's unique contributions (beyond DeepRhythmAI) are not detailed.

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

The type of ground truth used for this specific submission's testing is not explicitly stated. Given that it leverages a previously cleared AI for detection, the ground truth for the DeepRhythmAI algorithm itself would have been established during its clearance, likely through expert consensus with ECG interpretation. For the DeepRhythm Platform's own capabilities (processing, visualization, reporting), it would likely involve verification against established ECG processing standards and internal validation, but the ground truth method is not described.

8. The sample size for the training set

The document states that the DeepRhythm Platform "utilizes DeepRhythmAI (FDA-cleared device) for QRS and arrhythmia detection on the received signal." This means the training of the core AI algorithm (DeepRhythmAI) would have been done prior to this submission as part of its own clearance. The sample size for the training of DeepRhythmAI is not provided in this document.

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

As in point 8, the ground truth for the DeepRhythmAI algorithm's training would have been established during its separate FDA clearance. Details on this ground truth establishment are not provided in this 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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The Medicalgorithmics' Q Patch is intended to be used by patients who have a demonstrated need for extended cardiac monitoring and patients with symptoms that may be due to cardiac arrhythmias such as, dizziness, lightheadedness, shortness of breath, palpitations, dyspnea (shortness of breath), anxiety, syncope of unknown etiology in which arrhythmias are suspected or need to be excluded. It is indicated for use on adult patients. The sensor records single ECG channel for up to 15 days and can be used on patients with implanted pacemakers but is not intended to record pacemaker activity.

The Q Patch is a single channel ECG recorder. The device is intended to be placed on the sternum (in the middle of the chest). The Q Patch ECG recorder snaps onto the two off-the-shelf electrodes (Solid gel, Ag/AgCl) and records patient's ECG for up to 15 days powered from single disposable, non-rechargeable battery. The Q Patch Mobile Application is used to initiate recording session, for checking Q Patch status and to finish/stop the session if needed. When the recording is finished, the ECG data stored in Q Patch's memory can be downloaded through a USB port using the Q Patch Downloader (PC application).

The FDA 510(k) clearance document for the Q Patch (K210758) indicates that a clinical study and other performance tests were conducted to demonstrate substantial equivalence to the predicate device. However, the document does not contain a specific table of acceptance criteria and reported device performance metrics in numerical form (e.g., sensitivity, specificity, accuracy) for an algorithm's performance in detecting arrhythmias. This is largely because the Q Patch is described as an "Electrocardiograph, Ambulatory (Without Analysis)," meaning it primarily records ECG data and does not perform automated arrhythmia analysis.

Here's a breakdown of the requested information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

As noted, the document does not provide a table with quantitative acceptance criteria and specific algorithm performance metrics for arrhythmia detection (e.g., sensitivity, specificity). The Q Patch is explicitly stated not to include ECG analysis and visualization software as part of this specific product's clearance. It functions as a recorder, and its data can be integrated with third-party analysis software.

The acceptance criteria are implicitly defined by compliance with relevant electrical safety, EMC, software, usability, and biocompatibility standards, and demonstrating comparable performance to the predicate device in ECG recording quality.

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

• Clinical Study for ECG Signal Quality:

  • Sample Size: 30 participants.
  • Data Provenance: The document does not specify the country of origin of the data or whether it was retrospective or prospective. It mentions "Clinical study where ECG signals were recorded by the subject device and reference device simultaneously have been performed," which suggests a prospective data collection.

• Electrode Adherence Study:

  • Sample Size: 18 participants.

• Usability Testing:

  • Sample Size: 60 participants.

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

For the clinical study on ECG signal quality:

• The document states: "It has been demonstrated that non-standard ECG lead recorded by the Q Patch device is appropriate for ECG evaluation performed by the trained human interpreter."
• It further clarifies: "Potential patient cardiac abnormalities, must be confirmed by a qualified ECG technician or by a physician with other relevant clinical information."
• The number of experts or their specific qualifications (e.g., "radiologist with 10 years of experience") are not specified in the provided text for establishing ground truth within the clinical study. It generally refers to "trained human interpreter," "qualified ECG technician," or "physician."

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1) for establishing ground truth in the clinical study.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs Without AI Assistance

• No MRMC study involving AI assistance: The document states that "The subject device acquires ECG data and does not perform automatic arrhythmia analysis." Therefore, a comparative effectiveness study involving AI assistance for human readers is not applicable as the cleared device does not have an AI analysis component.
• The clinical study compared the Q Patch's ECG signal quality against a "reference device" for human interpretation, not with or without AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• No standalone algorithm performance study: As the device "does not perform automatic arrhythmia analysis," a standalone algorithm performance study is not applicable. The device's primary function is ECG recording.

7. The Type of Ground Truth Used

• For the ECG signal quality clinical study: The ground truth appears to be based on human interpretation of the ECG signals by "trained human interpreter," "qualified ECG technician," or "physician," potentially against findings from a "reference device." This implies a form of expert consensus or comparison to a gold standard from the reference device, though specific details are lacking.

8. The Sample Size for the Training Set

• The document does not indicate any training set as the Q Patch "does not perform automatic arrhythmia analysis" and is not an AI/algorithm-based diagnostic device. The studies described are for hardware performance, signal quality, usability, and biocompatibility.

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

• Not applicable, as there is no mention of a training set for an algorithm.

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The PocketECG IV is intended to be used by:

1. Patients who have a demonstrated need for cardiac monitoring. These may include but are not limited to patients who require monitoring for: a) non-life threatening arrhythmias such as supraventricular tachycardias (e.g. atrial fibrillation, atrial flutter, PACs, PSVT) and ventricular ectopy; b) evaluation of bradyarhythmias and intermittent bundle branch block, including after cardiovascular surgery and myocardial infarction; and c) arrhythmias associated with co-morbid conditions such as hyperthyroidism or chronic lung disease.

2. Patients with symptoms that may be due to cardiac arrhythmias. These may include but are not limited to symptoms such as: a) dizziness or lightheadedness; b) syncope of unknown etiology in which arrhythmias are suspected or need to be excluded; and c) dyspnea (shortness of breath).

3. Patients with palpitations without known arrhythmias to obtain a correlation of rhythm with symptoms.

4. Patients who require monitoring of the effect of drugs to control ventricular rate in various atrial arrhythmias (e.g. atrial fibrillation).

5. Patients recovering from cardiac surgery who are indicated for outpatient arrhythmia monitoring.

6. Patients with diagnosed sleep disordered breathing including sleep apnea (obstructive, central) to evaluate possible nocturnal arrhythmias.

7. Patients requiring arrhythmia evaluation of etiology of stroke or transient cerebral ischemia, possibly secondary to arrial fibrillation or atrial flutter.

Data from the device may be used by another device to analyze. measure or report OT interval. The device is not intended to sound any alarms for QT interval changes.

Contraindications:

The PocketECG IV is not intended to be used by:

1. Patients who have been diagnosed with life-threatening arrhythmias and require hospitalization.

2. Patients who require inpatient monitoring using a life-saving device.

The Medicalgorithmics Unified Arrhythmia Diagnostic System PocketECG IV, type P4TR-AA-ADS is an ambulatory ECG monitor which analyzes electrographic signal, classifies all detected heart beats and recognizes rhythm abnormalities. All detection results, including annotations for every detected heart beat and the entire ECG signal are transmitted via cellular telephony network to a remote server accessible by a Monitoring Center for reviewing by trained medical staff.

Here's a breakdown of the acceptance criteria and study information for the Unified Arrhythmia Diagnostic System PocketECG IV, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The submission references compliance with the IEC 60601-2-47:2012 standard for arrhythmia detection algorithms. The text explicitly states: "Test results were considered to be in complaint with standard requirements." However, concrete numerical acceptance criteria and specific performance metrics (e.g., sensitivity, specificity, accuracy for various arrhythmia types) from this standard are not detailed in the provided document. The document implies that the device meets the standard but doesn't provide the standard's exact requirements or the device's specific results against those requirements.

Similarly, for wireless transmission, the document states: "Wireless transmission performance has been tested according to Verizon Open Development (based on CTIA) requirements for LTE data transmissions." Again, the specific numerical acceptance criteria and detailed performance results are not provided.

Therefore, I can only create a table that states the areas tested and the general conclusion of compliance, not a detailed comparison of specific numerical acceptance criteria versus reported performance.

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

• Sample Size for Test Set: Not explicitly stated. The document mentions "performance testing according to IEC 60601-2-47:2012" but doesn't provide any details about the dataset used for this testing, including the number of patients, recordings, or specific arrhythmias.
• Data Provenance: Not explicitly stated. There is no mention of the country of origin of the data or whether it was retrospective or prospective.

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

• Number of Experts: Not explicitly stated. The document does not describe how ground truth was established for the performance testing, nor does it mention the involvement or qualifications of experts for this purpose.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated. The document does not describe any adjudication method used for the test set.

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

• MRMC Study: No indication of an MRMC comparative effectiveness study being performed or reported in the provided text. The submission focuses on device performance against standards, not on human-AI collaboration or improvement metrics.

6. Standalone Performance Study (Algorithm Only)

• Standalone Performance: Yes, a standalone performance study was implicitly done for the arrhythmia detection algorithms. The text states: "Arrhythmia detection algorithms implemented in PocketECG IV have been subject for performance testing according to IEC 60601-2-47:2012 (AAMI / ANSI / IEC 60601-2-47:2012)." This refers to the algorithm's performance in detecting arrhythmias against established standards, without human intervention as part of the testing methodology described. While the overall system involves human review, the specific performance testing mentioned here would be for the automated algorithm's accuracy.

7. Type of Ground Truth Used

• Type of Ground Truth: Not explicitly stated. While performance testing against IEC 60601-2-47:2012 implies a comparison to a known, verified "true" state of cardiac rhythms, the method by which this ground truth was established (e.g., expert consensus, pathology, other validated methods) is not detailed in the document.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not explicitly stated. The document makes no mention of a training set or its size, which is common for submissions primarily focused on verification and validation of a developed product rather than describing the entire development process of an AI model from scratch.

9. How Ground Truth for the Training Set Was Established

• Ground Truth for Training Set: Not explicitly stated. Since a training set isn't mentioned, the method for establishing its ground truth is also not provided.

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PocketECG CRS is intended to be used for:

1. Cardiac monitoring of patients undergoing a cardiac rehabilitation program. Its main feature is patient monitoring during previously planned training sessions and training assistance to achieve desired intensity and duration of workout. Training session parameters such as heart rate threshold, session duration (time intervals and number of repetitions) are defined by the physician for each patient individually. Additionally, the transmitter allows continuous ECG monitoring between trainings, during patient daily activities.

2. All patients hospitalized with a primary diagnosis of an acute myocardial infarction (MI) or chronic stable angina (CSA), or who during hospitalization have undergone coronary artery bypass graft (CABG) surgery, a percutaneous coronary intervention (PCI), cardiac valve surgery, or cardiac transplantation, referred to an early outpatient cardiac rehabilitation or secondary prevention (CR) program.

3. All patients evaluated in an outpatient setting who within the past 12 months have experienced an acute myocardial infarction (MI), coronary artery bypass graft (CABG) surgery, a percutaneous coronary intervention (PCI), cardiac valve surgery, or cardiac transplantation, or who have chronic stable angina (CSA) and have not already participated in an early outpatient cardiac rehabilitation or secondary prevention (CR) program for the qualifying event or diagnosis, referred to such a program.

4. Patients who require monitoring for: a) non-life threatening arrhythmias such as supraventricular (e.g. atrial fibrillation, atrial flutter, PACs, PSVT) and ventricular ectopy; b) evaluation of bradyarrhythmias and internittent bundle branch block, including after cardiovascular surgery and myocardial infarction; and c) arrhythmias associated with co-morbid conditions such as hyperthyroidism or chronic lung disease;

5. PocketECG CRS can be used to monitor the training session in hospital, rehabilitation center or physicians office under supervision of qualified staff.

Medicalgorithmics Unified Cardiac Rehabilitation System PocketECG CRS is an ambulatory system which can be used for patient monitoring during previously planned cardiac rehabilitation training sessions and training assistance to achieve desired intensity and duration of workout. The system measures patient's physical activity and ECG signal, classifies all detected heart beats and recognizes rhythm abnormalities. PocketECG CRS can be also used for patient's monitoring between training sessions as an ambulatory ECG monitor which analyzes electrographic signal, classifies all detected heart beats and recognizes rhythm abnormalities. All detection results, including annotations for every detected heart beats and the entire ECG signal are transmitted via cellular network to a remote server accessible by a Monitoring Center for review by trained medical staff.

The provided text is a 510(k) summary for the Medicalgorithmics Unified Cardiac Rehabilitation System PocketECG CRS. It details the device's indications for use, technological comparison to predicate devices, and adherence to various standards and guidance documents. However, it does not include specific tables of acceptance criteria with reported device performance, nor does it provide details on the study design elements such as sample sizes for test/training sets, data provenance, number or qualifications of experts for ground truth, adjudication methods, or MRMC comparative effectiveness study results.

The section titled "IX. Performance data" states:
"Arrhythmia detection algorithms implemented in PocketECG CRS (PECGT-III) and PocketECG III (PECGT-IIIR) have been subject for performance testing according to IEC 60601-2-47:2012 (AAMI / ANSI / IEC 60601-2-47:2012) Test results were considered to be in complaint with standard requirements."

This statement indicates that performance testing was conducted and met the requirements of the specified standard, but it does not provide the qualitative or quantitative results needed to populate the requested table or answer the specific questions about the study methodology.

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

Here's a breakdown of what can and cannot be answered based on the provided document:

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

• Cannot be fully provided. The document states that "Test results were considered to be in complaint with standard requirements" (IEC 60601-2-47:2012), implying acceptance criteria from that standard were met. However, the specific acceptance criteria and the reported performance metrics (e.g., sensitivity, specificity for various arrhythmias) are not explicitly listed in a table.

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

• Cannot be provided. The document does not specify the sample size for the test set or the provenance of the data used for performance testing (e.g., country of origin, retrospective/prospective nature).

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)

• Cannot be provided. The document does not describe how ground truth was established for the performance testing, nor does it mention the number or qualifications of any experts involved.

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

• Cannot be provided. The document does not describe any adjudication method used for the test set.

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

• Cannot be provided. The document makes no mention of an MRMC comparative effectiveness study or human reader improvement with AI assistance. The performance testing described appears to be for the algorithm's standalone performance against a standard.

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

• Likely yes, based on the text. The statement "Arrhythmia detection algorithms implemented in PocketECG CRS (PECGT-III) and PocketECG III (PECGT-IIIR) have been subject for performance testing according to IEC 60601-2-47:2012" implies standalone algorithmic performance was assessed against a standard. There is no mention of human-in-the-loop performance studies described in this summary.

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

• Cannot be provided specifically. While it's implied that there was a ground truth or reference standard against which the arrhythmia detection algorithms were tested, the document does not specify the type of ground truth (e.g., whether it was expert consensus, manually annotated ECGs, etc.).

8. The sample size for the training set

• Cannot be provided. The document does not mention the training set or its size.

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

• Cannot be provided. As the training set is not mentioned, its ground truth establishment is also not described.

In summary, the provided 510(k) summary confirms that performance testing (specifically for arrhythmia detection algorithms) was conducted in accordance with IEC 60601-2-47:2012 and found to be "in complaint with standard requirements." However, it lacks the detailed quantitative and qualitative results, and the specifics of the study methodology (sample sizes, data provenance, ground truth establishment, expert involvement, or any human-in-the-loop studies) that your request entails.

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The indications for use for the PocketECG III monitor are as follows:

1. Patients who have a demonstrated need for cardiac monitoring. These may include but are not limited to patients who require monitoring for: a) non-life threatening arrhythmias such as supraventricular tachycardias (e.g. atrial fibrillation, atrial flutter, PACs. PSVT) and ventricular ectopy: b) evaluation of bradyarrhythmias and intermittent bundle branch block, including after cardiovascular surgery and myocardial infarction; and c) arrhythmias associated with co-morbid conditions such as hyperthyroidism or chronic lung disease;
2. Patients with symptoms that may be due to cardiac arrhythmias. These may include but are not limited to symptoms such as: a) dizziness or lightheadedness; b) syncope of unknown etiology in which arrhythmias are suspected or need to be excluded; and c) dyspnea(shortness of breath);
3. Patients with palpitations with or without known arrhythmias to obtain correlation of rhythm with symptoms;
4. Patients who require monitoring of effect of drugs to control ventricular rate in various atrial arrhythmias (e.g. atrial fibrillation);
5. Patients recovering from cardiac surgery who are indicated for outpatient arrhythmia monitoring;
6. Patients with diagnosed sleep disordered breathing including sleep apnea (obstructive, central) to evaluate possible nocturnal arrhythmias;
7. Patients requiring arrhythmia evaluation of etiology of stroke or transient cerebral ischemia, possibly secondary to atrial fibrillation or atrial flutter;
8. Data from the device may be used by another device to analyze, measure or report QT interval. The device is not intended to sound any alarms for QT interval changes.

PocketECG III - Medicalgorithmics Unified Arrhythmia Diagnostic System, type PECGT-IIIV is an ambulatory ECG monitor which analyzes electrographic signal, classifies all detected heart beats and recognizes rhythm abnormalities. All detection results, including annotations for every detected heart beat and the entire ECG signal are transmitted via cellular telephony network to a remote server accessible by a Monitoring Center for reviewing by trained medical staff.

The provided text does not contain detailed information about specific acceptance criteria and a study proving the device meets them in the format requested. While it outlines the device's indications for use, contraindications, and general technical specifications, it lacks the precise performance metrics, study design, and results typically found in such a document.

Here's an analysis based on the available information and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

This information is not provided in the document. The text mentions that the device "meets the requirements of following performance standards" (IEC 60601-1, IEC 60601-1-2, IEC 60601-1-11, IEC 60601-2-47, and AAMI/ANSI EC57:2012), but it does not specify the numerical acceptance criteria derived from these standards for arrhythmia detection and the actual reported performance of the PocketECG III against those criteria.

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

This information is not provided in the document.

3. Number of experts used to establish the ground truth for the test set and their qualifications:

This information is not provided in the document. The text mentions "reviewing by trained medical staff" at a Monitoring Center, but it doesn't quantify or qualify these individuals regarding ground truth establishment for a test set.

4. Adjudication method 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, and its effect size:

This information is not provided in the document. The filing focuses on the device itself and its equivalence to a predicate, not on a human-in-the-loop study.

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

The document implies standalone performance testing against standards, as it states the device "analyzes electrographic signal, classifies all detected heart beats and recognizes rhythm abnormalities." It also mentions meeting "requirements of following performance standards," which would necessitate standalone testing of the algorithm's accuracy against a known truth. However, the specific details and results of such testing are not provided.

7. The type of ground truth used:

This information is not explicitly stated in the document. Given the nature of arrhythmia detection devices and the reference to standards like AAMI/ANSI EC57:2012, it is highly probable that the ground truth would involve expert cardiac rhythm annotations on ECG recordings. However, the document does not confirm this or describe the process.

8. The sample size for the training set:

This information is not provided in the document.

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

This information is not provided in the document.

Summary of what is present and relevant:

• Device: PocketECG III - Medicalgorithmics Unified Arrhythmia Diagnostic System, type PECGT-IIIV
• Purpose: Ambulatory ECG monitor that analyzes electrographic signals, classifies heartbeats, and recognizes rhythm abnormalities.
• Process: Detects, annotates, and transmits ECG signal and detection results via cellular network to a remote server for review by trained medical staff.
• Standards Mentioned: IEC 60601-1, IEC 60601-1-2, IEC 60601-1-11, IEC 60601-2-47, and AAMI / ANSI EC57:2012. The document states the device "meets the requirements" of these standards. This implies that some form of performance testing against criteria derived from these standards was conducted for substantial equivalence.

In conclusion, the provided text is a 510(k) summary for regulatory clearance, focusing on demonstrating substantial equivalence to a predicate device and adherence to general safety and performance standards. It does not delve into the detailed scientific study design, specific performance metrics, and results that would fully address all parts of your request regarding acceptance criteria and their proof.

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The indications for use for the PocketECG v3 monitor are as follows:

1. Patients who have a demonstrated need for cardiac monitoring. These may include but are not limited to patients who require monitoring for: a) non-life threatening arrhythmias such as supraventricular tachycardias (e.g. atrial fibrillation, atrial flutter, PACs, PSVT) and ventricular ectopy; b) evaluation of bradyarrhythmias and intermittent bundle branch block, including after cardiovascular surgery and myocardial infarction; and c) arrhythmias associated with co-morbid conditions such as hyperthyroidism or chronic lung disease
2. Patients with symptoms that may be due to cardiac arrhythmias. These may include but are not limited to symptoms such as: a) dizziness or lightheadedness; b) syncope of unknown etiology in which arrhythmias are suspected or need to be excluded; and c) dyspnea (shortness of breath)
3. Patients with palpitations with or without known arrhythmias to obtain correlation of rhythm with symptoms.
4. Patients who require monitoring of effect of drugs to control ventricular rate in various atrial arrhythmias (e.g. atrial fibrillation)
5. Patients recovering from cardiac surgery who are indicated for outpatient arrhythmia monitoring
6. Patients with diagnosed sleep disordered breathing including sleep apnea (obstructive, central) to evaluate possible nocturnal arrhythmias
7. Patients requiring arrhythmia evaluation of etiology of stroke or transient cerebral ischemia, possibly secondary to atrial fibrillation or atrial flutter.
8. Data from the device may be used by another device to analyze, measure or report QT interval. The device is not intended to sound any alarms for OT interval changes.

PocketECG v3 - Medicalgorithmics Unified Arrhythmia Diagnostic System is an ambulatory ECG monitor which analyzes electrographic signal, classifies all detected heart beats and recognizes rhythm abnormalities. All detection results, including annotations for every detected heart beat and the entire ECG signal are transmitted via cellular telephony network to a remote server accessible by a Monitoring Center for reviewing by trained medical staff.

This appears to be a 510(k) summary for the PocketECG v3 device. Unfortunately, the provided text does not contain any information about acceptance criteria or specific study results demonstrating the device meets those criteria.

The document primarily focuses on:

• Device identification: Names, classification, product codes.
• Substantial equivalence: Listing predicate devices and comparing similarities and differences.
• Indications for Use and Contraindications.
• Referenced standards: IEC, AAMI/ANSI, ISO.
• FDA correspondence: The final pages show the FDA's decision letter.

To fully answer your request, information regarding performance studies, acceptance criteria, sample sizes, ground truth establishment, and expert qualifications would need to be present in a different section of the 510(k submission, likely a dedicated "Performance Testing" or "Clinical Performance" section, which is not included in the provided text.

Therefore, I cannot generate the table or provide the detailed study information you requested based on the given input.

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