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The EMMa system is intended for diagnostic evaluation of patients who experience transient symptomatic events that may suggest non-lethal cardiac arthythmia to support and possibly improve an ongoing treatment by the patient's physician. Data received from battery powered ambulatory monitoring devices, triggered by an arrhythmia detection algorithm or manually by the patient, are stored and forwarded to licensed physician for review.

EMMa (Electronic Monitoring Management) is the server part of a telemedical system to receive data from the ambulatory ECG monitors Kate Loop (Event Monitor) and Kate MCT (Mobile Cardiac Telemetry; similar to Loop but with additional functions like trend data and streaming). Physiological data recorded by the ECG monitors are transmitted via their GSM module (cellular telephon network) to the server EMMa. The detection of arrhythmias and other cardiac conditions is done on the ECG monitoring device, not on the EMMA, and is not scope of this 510k. EMMa is provided to be installed in a Telemonitoring Service Centre (TSC). ECG-technicians / Agents working there generate patient reports from physiological data with the aim to send the reports to the patients' physicians. No interpretation of data is performed by the server software. The generated reports support physicians in adapting the therapy. EMMa is not designed for and compatible with iOS and Android.

The provided text does not contain detailed acceptance criteria for the EMMa device's performance, nor does it describe a study specifically designed to "prove" the device meets such criteria in terms of clinical accuracy or effectiveness.

The document is a 510(k) premarket notification letter and summary, primarily focusing on establishing substantial equivalence to a predicate device. It highlights software verification and validation activities and other non-clinical performance data, but clinical performance data was not required for the substantial equivalence determination.

Therefore, many of the requested details, such as specific performance metrics, sample sizes for test sets, expert qualifications, adjudication methods, MRMC studies, or standalone algorithm performance, are not available in the provided text.

Here's what can be extracted and what information is missing:

Missing Information:

• A table of acceptance criteria and reported device performance (specifically for clinical accuracy)
• Sample size used for the test set
• Data provenance for clinical testing
• Number of experts used to establish ground truth
• Qualifications of experts establishing ground truth
• Adjudication method for the test set
• Whether a multi-reader multi-case (MRMC) comparative effectiveness study was done, or the effect size of human reader improvement with AI assistance
• Whether a standalone (algorithm only) performance study was done (the document states arrhythmia detection is done on the ECG monitoring device, not the EMMa software)
• The type of ground truth used for clinical effectiveness (as clinical data was not required)
• Sample size for the training set (no mention of a training set for clinical performance)
• How ground truth for the training set was established (no mention of a training set for clinical performance)

Available Information (related to non-clinical performance and design):

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

   • The document implies that the device "meets all the stated requirements and passed all the testing noted above." However, specific numerical performance metrics (e.g., sensitivity, specificity for arrhythmia detection) are not provided. The "acceptance criteria" appear to be compliance with relevant standards (IEC 62304, IEC 62366-1) and software verification/validation.

   Acceptance Criterion (Implied)	Reported Device Performance
   Compliance with IEC 62304 (Medical Device Software life cycle)	Passed; documentation provided
   Compliance with FDA Guidance for Software Contained in Medical Devices	Software verification and validation conducted; documentation provided
   Software Level of Concern (Moderate) satisfied	Yes
   Compliance with IEC 62366-1 (Usability engineering)	Passed
   Meeting "all stated requirements" (General design/functionality)	EMMa meets all stated requirements

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

   • Not provided for clinical performance. The "test set" mentioned refers to software verification and validation, not a clinical dataset for diagnostic accuracy.

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

   • Not applicable/Not provided for clinical performance. Clinical ground truth establishment was not part of the required testing for the 510(k) pathway for this device.

4. Adjudication method for the test set:

   • Not applicable/Not provided for clinical performance.

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:

   • No. The document explicitly states "Clinical data were not required to support the safety and effectiveness of the device EMMa." Furthermore, the EMMa is a server system designed to receive data and present it for human review by ECG-technicians and physicians; it does not perform final diagnostic interpretation or AI-driven assistance that would typically be evaluated in an MRMC study comparing human performance with and without AI. The arrhythmia detection algorithm itself resides on the ambulatory ECG monitoring devices (e.g., Kate Loop, Kate MCT), not on the EMMa server, and "is not scope of this 510k."

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

   • No. The document clarifies that the arrhythmia detection is done on the ECG monitoring device, not on the EMMa software. EMMa is a server system for managing, storing, and forwarding data for human review.

7. The type of ground truth used:

   • For the non-clinical software testing, the "ground truth" would be the expected functional behavior and output based on design specifications and regulatory standards. Clinical ground truth (e.g., pathology, outcomes data) was not used as clinical data was not required.

8. The sample size for the training set:

   • Not provided. Clinical training data is not discussed as clinical studies were not performed for this 510(k).

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

   • Not provided. (See point 8).

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Diagnostic evaluation of patients with asymptomatic disturbances of cardiac rhythm such as: -Dizziness -Heart race -Palpitation -Syncopesof unknown cause

The ECG monitoring devices are post-event recorders, also called tele-ECG devices, which realize one-channel (100BT) or three- channel (300BT) recordings of short ECG sections, their memorization as well as the preparation of the transmission. Therefore, the purpose of the recorders is the derivation and memorization of an ECG section in combination with a compatible telemedicine system. The recorder provides physicians with information used for the diagnosis of cardiac arrhythmias. The Post Event 300BT device is a modification to Vitaphone's previously cleared Post Event 110BT device cleared pursuant to K100383. The modification consists of the availability of three recording channels rather than one recording channel. There is no change to intended use, patient contact materials or core technology. The hardware of the 100BT and the 300BT is basically the same. The differences between both devices is the activation of the channels by the software. The device supports the transmission of the data. The recorders are battery operated devices. The recorders do not possess a separate on/off switch.

The provided document is a 510(k) summary for the Vitaphone Post-Event Recorder 300 BT. This document focuses on demonstrating substantial equivalence to a predicate device (Vitaphone Post Event Recorder 100BT - K100383) rather than defining specific performance acceptance criteria for a new AI/algorithm-driven device and a study to prove it.

The Vitaphone Post-Event Recorder 300 BT is a cardiac event recorder, not an AI or algorithm-driven diagnostic device in the sense of image analysis or complex pattern recognition. Its function is to record ECG signals and transmit them. The "performance data" section indicates compliance with safety and essential performance standards for medical electrical equipment.

Therefore, many of the requested points regarding AI/algorithm performance (e.g., sample size for test/training sets, ground truth establishment methods, expert adjudication, MRMC studies, standalone performance) are not applicable to this type of device and its 510(k) submission as presented.

Based on the provided text, here is what can be extracted and what cannot:

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

The document does not explicitly present a table of "acceptance criteria" in the typical sense of diagnostic performance metrics (sensitivity, specificity, AUC). Instead, it relies on compliance with established medical device standards and functional equivalence to the predicate device.

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

• Not applicable / Not provided. The document describes engineering validation and verification testing against standards, not a clinical study with a "test set" of patient data for diagnostic algorithm performance.

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. This is not an AI/algorithm for diagnostic interpretation. Ground truth for ECG signal quality would be assessed through technical means, not expert consensus on diagnoses.

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

• Not applicable / Not provided. No such adjudication for diagnostic performance is mentioned.

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

• No. This is not an AI-assisted diagnostic device; therefore, no MRMC study or AI-assistance effect size is relevant or provided.

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

• Not applicable / No. The device is a recorder, not a standalone diagnostic algorithm. It enables healthcare professionals to acquire ECGs for later diagnostic evaluation.

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

• Not applicable / Not provided. For a simple ECG recorder, the "ground truth" relates to the accurate acquisition and transmission of the electrical signals from the heart, which is verified against known electrical inputs and validated against performance standards rather than clinical ground truth like pathology for a diagnostic algorithm.

8. The sample size for the training set

• Not applicable / Not provided. This device is hardware with firmware, not a machine learning model that requires a "training set."

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

• Not applicable / Not provided. As above, no training set for a machine learning model is involved.

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Diagnostic evaluation of patients with asymptomatic and symptomatic disturbances of the cardiac rhythm such as:
– Dizziness

• Heart race
• Palpitations
• Syncopes of unknown cause

The Vitaphone 100 BT device is single-channel cardiac event recorder for transmitting multiple ECG recordings via land-line or GSM telephony networks to a compatible ECG receiving system, such as REMOS ECG Receiving Software (510(k) K050670) or compatible ECG receivers.

The Vitaphone 100 BT device is intended for patient activated event recordings. It is battery driven and utilizes a Flash memory to store ECG data with an adjustable event time.

This 510(k) submission for the Vitasystems GmbH Post-Event Recorder Vitaphone 100 BT focuses on demonstrating substantial equivalence to predicate devices, rather than presenting a detailed clinical study with specific acceptance criteria and performance metrics in the way a novel AI algorithm would.

Therefore, many of the requested categories regarding acceptance criteria, study design, and performance metrics are not applicable or cannot be extracted directly from this document. This submission relies on proving the new device performs similarly to
already approved devices under established safety and performance standards.

Here's an breakdown of the available information based on your request, with an explanation of why some sections are not applicable:

1. Table of Acceptance Criteria and Reported Device Performance

This document does not provide a specific table of acceptance criteria and reported device performance in terms of diagnostic accuracy metrics (e.g., sensitivity, specificity, accuracy, F1 score) that would be relevant for an AI/CADe device. Instead, the "acceptance criteria" for this type of device are centered around meeting established safety and performance standards for electrophysiological equipment and demonstrating substantial equivalence to predicate devices.

Acceptance Criteria (Implied from the 510(k) and applicable standards):

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

• Not Applicable. This submission is for a hardware device (cardiac event recorder) that records and transmits ECGs. It does not involve a "test set" of data for diagnostic performance evaluation of an algorithm in the way an AI/CADe device would. The evaluation is against engineering standards and comparison to predicate devices, not against a clinical dataset for diagnostic accuracy.

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. As above, there is no "test set" requiring ground truth establishment by experts for diagnostic performance.

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

• Not Applicable. No test set requiring adjudication in this context.

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. This is a standalone device, not an AI-assisted diagnostic tool.

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

• Not Applicable. This is a hardware device for recording and transmitting ECGs. While its function implicitly involves "sensing" and "recording," it is not an "algorithm only" device in the AI sense, nor does it perform automated diagnosis. Its "standalone performance" is related to its ability to accurately capture and transmit ECG signals, which is assessed through compliance with technical standards rather than an algorithmic evaluation.

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

• Not Applicable. For a device of this nature, the "ground truth" would relate to the accuracy of its electrical measurements and data transmission properties, verified against calibrated equipment and engineering specifications, rather than clinical ground truth like pathology or expert consensus on a diagnosis.

8. The Sample Size for the Training Set

• Not Applicable. This is not an AI/ML device that requires a "training set."

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

• Not Applicable. No training set exists for this type of device.

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