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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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The clue medical and clue medical BASIC are handheld, battery operated single channel electrocardiographs intended for recording and transmitting ECG data by patients who are concerned about their heart rhythm. The clue medical BASIC allow the patient to record their ECG data into the device memory and transmit it for display by healthcare professionals.

Specifically, the clue medical and clue medical BASIC are intended for patients that are concerned about their heart rhythm or have experienced the following symptoms that are suggestive of abnormal heart rhythm:

Skipped beats Pounding heart (palpitations)

History of arrhythmia

The clue medical and clue medical BASIC ECG data is intended to be used by a licensed health care practitioner. These measurements are not intended for any specific clinical diagnosis. The clinical significance must be determined by the physician.

The clue medical is an handheld battery operated single-channel ECG recorder featuring four fixed electrodes on the back of the device, an ECG lead connector, an IrDA interface and one single button to record and store up to ten ECG recordings with 30 seconds or two minutes duration. The stored ECG tests are processed into a printable Portable Document Format (pdf) that can be transmitted two ways. The pdf can be transmitted via IrDA to an IrDA capable local PC or printer. If the receiving device is not IrDA capable, an optional IrDA USB stick is provided with the clue medical. The pdf file can then be saved, sent, displayed or printed. Alternatively, the pdf can be transmitted via an IrDA capable cell phone directly to the Telovital server for storage.

The clue medical BASIC is identical to the clue medical in design. The only difference is that the clue medical BASIC does not provide the added capability of sending the ECG data via cell phone directly to the Telovital server, it is only capable of IrDA transmission to a local PC or printer.

The provided text describes the "clue medical and clue medical BASIC Telemedical Systems" and states that "Performance Testing" was conducted to meet several IEC standards. However, it does not provide detailed acceptance criteria with specific thresholds or reported device performance metrics in a quantitative manner that would allow for the creation of a table.

Here's an analysis based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance:

The document states that the devices "have been tested to meet all of the following standards," implying that compliance with these standards represents the acceptance criteria. However, it does not provide any quantitative performance results from these tests. Therefore, a table with specific performance metrics cannot be generated from the given text.

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

The document mentions "Performance Testing" but does not specify any sample size for a test set (e.g., number of patients, number of ECG recordings). There is also no information regarding data provenance (e.g., country of origin, retrospective or prospective nature).

3. Number of Experts and Qualifications for Ground Truth:

The document does not mention the use of experts to establish ground truth for any test set or the qualifications of such experts. The study described focuses on compliance with electrical and safety standards, not on clinical performance evaluated by human experts against a ground truth.

4. Adjudication Method for the Test Set:

Since there is no mention of a test set being evaluated by experts or any clinical outcome, there is no adjudication method described.

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

No Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done or mentioned. The submission describes compliance with technical standards for an electrocardiograph and its electrical safety, not a study evaluating human reader performance with or without AI assistance. The device is a "single channel electrocardiograph intended for recording and transmitting ECG data," not an AI-powered interpretation system.

6. Standalone (Algorithm Only) Performance Study:

No standalone (algorithm only) performance study was conducted or described in the provided text. The device itself is hardware for recording ECG data; it doesn't appear to have an independent algorithm for interpretation (beyond potentially saving it as a PDF for healthcare professionals). The output is intended to be used by a "licensed health care practitioner" for determination of clinical significance.

7. Type of Ground Truth Used:

The study referenced is focused on compliance with electrical safety and performance standards (e.g., IEC 60601 series), not clinical diagnostic accuracy. Therefore, the "ground truth" for this "study" would be the specifications and requirements outlined in those standards, rather than expert consensus, pathology, or outcomes data.

8. Sample Size for the Training Set:

No training set is mentioned or implied as the device is not described as utilizing machine learning or artificial intelligence that would require a training set.

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

Since there is no training set, this question is not applicable.

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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 3100 Series device is single-channel looping cardiac event recorder for transmitting multiple ECG recordings via land-line or GSM telephony networks to a compatible ECG receiving system, such as "sensor mobile" REMOS ECG Receiving Software (510(k) K050670) or compatible standard acoustic ECG receivers. The 3100 series device is intended for auto-triggered and patient activated event recordings (Bradycardia, Tachycardia and Atrial Fibrillation). It is battery driven and utilizes a loop-memory to capture ECG data with an adjustable pre- and post-event time.

This 510(k) submission for the TMS Telemedizinische Systeme GmbH Loop-Recorder vitaphone 3100 Series does not appear to contain a study specifically demonstrating the device meets acceptance criteria in the manner typically seen for complex AI/ML-based diagnostic devices. Instead, the submission focuses on demonstrating substantial equivalence to predicate devices, which is common for hardware devices with established product codes.

Based on the provided documents, here's an analysis of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

This information is not explicitly stated in the provided 510(k) summary. For devices like the vitaphone 3100 Series (a cardiac event recorder), acceptance criteria would typically revolve around:

• ECG Signal Quality: Ability to accurately capture and store ECG signals.
• Event Detection Accuracy: For auto-triggered events (Bradycardia, Tachycardia, Atrial Fibrillation), the accuracy of the device in identifying and logging these events.
• Loop Memory Functionality: Correct operation of the pre- and post-event loop recording.
• Data Transmission Reliability: Successful and accurate transmission of ECG data via land-line or GSM.
• Battery Life: Meeting specified operational hours.
• Safety Standards: Compliance with electrical safety and EMC standards.

The 510(k) summary only states: "The technical specification comparison reveals no substantial differerence between the 3100 Series device and the predicate devices and no differences which affect safety or efficacy." This implies that the device is expected to perform comparably to the predicate devices, which are already on the market and presumably meet established performance standards for cardiac event recorders.

To provide a placeholder, a potential table might look like this (hypothetical, as actual criteria are not given):

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

This information is not provided in the given 510(k) summary. For a hardware device, "test set" might refer to device-level testing (e.g., bench testing, electrical tests, performance verification) rather than a clinical dataset in the way it's used for AI/ML devices. There is no mention of patient data (retrospective or prospective) being used to prove specific performance metrics.

3. Number of Experts Used to Establish Ground Truth and Qualifications

This information is not applicable and not provided. The device is a "single-channel looping cardiac event recorder" which captures raw ECG data. The interpretation of this data would typically be done by clinicians (cardiologists, electrophysiologists) after the data is transmitted, not by the device itself establishing "ground truth" for diagnostic purposes. The 510(k) emphasizes the device's role in transmitting recordings for diagnostic evaluation, not performing the diagnosis itself.

4. Adjudication Method for the Test Set

This information is not applicable and not provided. As there's no mention of a human-adjudicated test set, there's no adjudication method described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, an MRMC comparative effectiveness study was not done and is not mentioned. Such studies are typically performed for AI/ML diagnostic tools to assess how the AI impacts human reader performance. This device is a hardware recorder, not an AI diagnostic tool.

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

No, a standalone study in the context of an algorithm's diagnostic performance was not done. The device itself has internal algorithms for auto-triggering based on detected heart rate deviations or fibrillation patterns, but the 510(k) focuses on the device's equivalence to predicates for its core function as a recorder and transmitter. Standalone performance for a diagnostic algorithm is not the focus here.

7. The Type of Ground Truth Used

This is largely not applicable in the context of clinical ground truth (e.g., pathology, outcomes data) for diagnosis provided by the device. The "ground truth" for this device's functionality would be based on engineering specifications, electrical performance tests, and comparison to the known operational characteristics of predicate devices. For its event detection capabilities, the ground truth would inherently be based on expert cardiology interpretation of the ECGs it records. However, the 510(k) does not describe a study where the device's event detection was compared against expert consensus or pathology.

8. The Sample Size for the Training Set

This is not applicable and not provided. This device is a hardware medical device with embedded firmware/algorithms, but there is no indication of it being an AI/ML device that underwent a "training" process with a large dataset in the modern sense. Its event detection logic would be based on established physiological thresholds and patterns, manually programmed, not learned via machine learning from a training set.

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

This is not applicable and not provided for the reasons stated above (not an AI/ML device in the context of a training set).

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