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IM007 is intended for use by qualified healthcare professionals for the assessment of arrhythmias in Insertable Cardiac Monitor (ICM) ECG data.

IM007 supports downloading and analyzing data recorded in compatible formats from ICMs. This version of the IM007 only supports ECG data from Medtronic ICMs.

IM007 is intended to be electronically interfaced with other computer systems (remote monitoring platform) that supply the ECG data to IM007, and receive the output of IM007 (analysis) for viewing by the healthcare professionals. IM007 provides ECG signal processing and analysis, to detect asystole, bradycardia, atrial tachycardia or atrial fibrillation, ventricular tachycardia, normal rhythm and artifact.

IM007 is not for use in life supporting or sustaining systems or ECG monitor and Alarm devices.

IM007 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.

IM007 is a software medical device for the analysis of ECG signals from Insertable Cardiac Monitor (ICM) devices and confirms the presence or absence of arrhythmias. When it is interfaced with a compatible remote monitoring platform, IM007 provides additional data to healthcare professionals to support the analysis of abnormal episodes detected by ICM devices.

IM007 receives as input an ECG data signal via the Implicity remote monitoring platform, then processes the signal with a proprietary algorithm designed to detect arrhythmias and generates as output the result of the analysis to a remote monitoring platform.

IM007 comprises:

• . An algorithm (the Algorithm) that analyzes ECG files in order to detect cardiac rhythm abnormalities.
• A communication interface to external applications with the Algorithm and processing of ECG files. The API consists of 2 messaging queues (an input and an output).

IM007 works as follows:

• . IM007 receives input data (an ECG file and device parameters) from the remote monitoring platform using the input queue.
• . The file is processed by the Algorithm which delineates zones with abnormal waveforms (ECG signals not defined as normal sinus rhythm). The output format is a sequence of waveform labels/start time/end time.
• . IM007 sends a response to the Remote Monitoring Platform using the output queue.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Important Note: The provided text is a 510(k) summary and FDA clearance letter, which focuses on demonstrating substantial equivalence to a predicate device. It typically does not contain detailed descriptions of clinical studies, raw data, or specific statistical results as would be found in a full clinical trial report or scientific publication. Therefore, some information requested might be incomplete or inferred from the high-level descriptions.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of "acceptance criteria" with numerical targets. Instead, it refers to "specifications" and "intended use" being met by the device. The reported device performance is described as meeting these specifications and operating as intended. The "Non-clinical Performance" section states: "The results of the testing demonstrate that IM007 performs to its specifications and meets its intended use, which is substantially equivalent to that of the predicate device."

However, we can infer the types of performance criteria from the device's function: detecting specific arrhythmias. The comparison tables (Table 3) list the output classifications, implying that accurate detection of these events is the core performance metric.

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

• Sample Size for Test Set: The document simply states "ECG databases from the ANSI/AAMI EC57:2012 standard as well as Implicity proprietary databases." It does not specify the exact number of ECGs or patients in the test set.
• Data Provenance:
  • Country of Origin: Not specified in the text.
  • Retrospective or Prospective: Not specified, but generally, tests against established databases (like ANSI/AAMI EC57:2012) are retrospective in nature.

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

The document does not provide details on the number or qualifications of experts used to establish ground truth for the test sets. It mentions "qualified healthcare professionals" and "physicians and clinicians" in the context of the device's intended use and advisory nature, but not for the ground truth creation within the non-clinical performance study.

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1) used for establishing ground truth classifications within the test set.

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

• Was a MRMC study done? No, the document does not mention any MRMC comparative effectiveness study involving human readers with and without AI assistance. The non-clinical performance section describes algorithm-only testing ("algorithm performance") against databases.
• Effect size of human improvement with AI vs. without AI: Not applicable, as no MRMC study was described.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• Was a standalone study done? Yes. The "Non-clinical Performance" section explicitly states that "Algorithm performance testing was assessed using ECG databases from the ANSI/AAMI EC57:2012 standard as well as Implicity proprietary databases." This indicates testing of the algorithm itself, without human intervention during the assessment, to ensure it "performs to its specifications and meets its intended use."

7. Type of Ground Truth Used

The type of ground truth used is implied to be expert consensus or established annotations from standard databases. The reference to "ECG databases from the ANSI/AAMI EC57:2012 standard" suggests a comparison to pre-annotated data, often derived from expert review. For the "Implicity proprietary databases," it would likely also involve expert adjudication, but this is not explicitly detailed.

8. Sample Size for the Training Set

The document does not provide the sample size used for the training set. It only mentions that the algorithm is "based on Machine Learning technology" and was tested on "ECG databases from the ANSI/AAMI EC57:2012 standard as well as Implicity proprietary databases." It's common for these databases to serve dual purposes (training and testing, with appropriate splitting), but specific numbers are not given for either.

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

The document does not specify how the ground truth for the training set was established. Given the mention of "Machine Learning technology" and "ECG databases from the ANSI/AAMI EC57:2012 standard," it is highly probable that the training data and its ground truth were derived from:

• Expert Consensus: Cardiologists or electrophysiologists reviewed and annotated ECG waveforms.
• Established Annotations: Standard, publicly or privately curated databases often come pre-annotated by clinical experts.

However, the specific methods for ground truth establishment for the training set are not detailed in this 510(k) summary.

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The base is intended to be used as part of the CareLink SmartSync device manager system. Clinicians use the base to analyze the electrical performance of cardiac leads during device implant or invasive troubleshooting. Clinicians use the base's ECG connections along with the app display to view, measure, and record live cardiac waveforms. The base is intended to be used by healthcare professionals only in operating environments under direct medical supervision.

The CareLink SmartSync Device Manager system is comprised of the Model 24970A Base and CareLink SmartSync Device Manager app installed and running on your mobile device. The Base pairs with the app on your mobile device to analyze the cardiac lead system for an implantable Medtronic device. The base includes analyzer hardware and patient cable connections, ECG cable connections, and Bluetooth wireless technology. The Base contains a microprocessor that maintains the pacing engine logic function. The PSA hardware within the Base consists of two main integrated circuits (ICs): a Micro Controller Unit and a Mixed Signal Integrated Circuit. The App is the primary user interface and includes a Host Application, a Platform Application, a Common Application, and a PSA Application component. The Analyzer tools in the App display and report on the cardiac lead and ECG data transmitted from the base.

The provided text describes a 510(k) premarket notification for the Medtronic CareLink SmartSync Device Manager Pacing System Analyzer (K163008). It focuses on demonstrating substantial equivalence to a predicate device, rather than proving the device meets specific acceptance criteria through a clinical study with a test set, ground truth experts, and MRMC analysis as typically understood for an AI/ML medical device.

Therefore, many of the requested elements for an AI/ML device study (e.g., sample size for test set, data provenance, number of experts for ground truth, adjudication method, MRMC study, standalone performance, training set details) are not applicable (N/A) based on the provided document.

The document outlines performance data collected to support the substantial equivalence claim, which includes: Biocompatibility testing, Electrical safety and electromagnetic compatibility (EMC) testing, Software Verification and Validation Testing, and Mechanical Testing.

Here's a breakdown of the information that is available in the document, mapped against your request:

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

The document does not present a formal "acceptance criteria" table with corresponding "reported device performance" in the typical sense of a clinical validation study for an AI/ML device. Instead, it demonstrates compliance with relevant standards and functionality of a medical device (a Pacing System Analyzer). The "performance" is primarily shown through a comparison to predicate devices and adherence to regulatory standards.

Here's an interpretation based on the provided "Key Performance Specifications/Characteristics of the Device" table, which functions like a set of performance criteria, and the statement of compliance:

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

• Sample Size for Test Set: Not applicable. This submission is for a hardware/software medical device (Pacing System Analyzer), not an AI/ML algorithm requiring a specific patient test set for performance evaluation in the clinical sense you described. Performance is demonstrated through engineering verification and validation, and comparison to a predicate device.
• Data Provenance: Not applicable for an AI/ML algorithm test set. The testing conducted is primarily laboratory-based engineering and software verification.

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)

• N/A. Ground truth establishment by experts for a test set is not relevant to this type of device submission. The device's function is to objectively measure electrical performance of cardiac leads and display waveforms, not to interpret medical images or signals in a way that requires human expert adjudication for "ground truth."

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

• N/A. Adjudication methods are not applicable for this device submission.

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

• N/A. This device is a Pacing System Analyzer, not an AI-assisted diagnostic tool that would typically undergo an MRMC study to assess human reader performance improvement.

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

• N/A. The device itself is a "standalone" system in that it performs its measurements according to its specifications. There isn't an "algorithm only" component that generates a decision or output that would then be compared to human performance in the context of an AI/ML device. Its function is to provide objective electrical measurements to a clinician.

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

• N/A. The "ground truth" for this device's performance relates to its ability to accurately measure electrical parameters (e.g., amplitude, impedance, rates) and display waveforms as per engineering specifications and comparison to predicate devices, rather than a clinical ground truth like disease presence or absence. Measurements are validated against known inputs and established engineering principles.

8. The sample size for the training set

• N/A. This document does not describe the development or validation of an AI/ML algorithm that requires a "training set" in the machine learning sense. The software described is traditional deterministic software.

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

• N/A. As no AI/ML training set is mentioned or implied, this question is not applicable.

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