Search Results

The Powerheart® AED G3A is intended to be used by personnel who have been trained in its operation. The user should be qualified by training in basic life support or other physician-authorized emergency medical response.

The device is indicated for emergency treatment of victims exhibiting symptoms of sudden cardiac arrest who are unresponsive and not breathing. Post-resuscitation, if the victim is breathing, the AED should be left attached to allow for acquisition and detection of the ECG rhythm. If a shockable ventricular tachyarrhythmia recurs, the device will charge automatically and advise the operator to deliver therapy.

When the patient is a child or infant up to 8 years of age, or up to 55 lbs (25kg), the Powerheart® AED G3A should be used with the Model 9730 Pediatric Attenuated Defibrillation Electrodes. The therapy should not be delayed to determine the patient's exact age or weight.

The Powerheart® AED G3A is a portable, battery-operated, fully-automatic, low power DC defibrillator. The device is designed to diagnose and monitor the patient's cardiac rhythm and deliver the shock energy as required. The Powerheart® AED device in this submission is equivalent to the current Powerheart® AED and accessories in commercial distribution that was cleared under premarket 510(k) notifications K022929, K011901, K 982710 and K031987. The reason for this premarket notification is to introduce a fully -automation version of the device.

This product is an Automated External Defibrillator. The provided 510(k) summary (K040438) primarily focuses on establishing substantial equivalence to previously cleared AEDs, rather than presenting detailed new clinical study data with specific acceptance criteria and detailed performance metrics of the device as an AI-powered diagnostic tool. This is a common approach for device modifications or new versions of established devices when the underlying technology (e.g., software algorithm) remains the same or only minimally altered without impacting safety or effectiveness.

Here's an analysis based on the provided text, addressing your points where possible:

1. Table of acceptance criteria and the reported device performance

Based on the provided K040438 summary:

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

The document does not explicitly state a sample size for a "test set" in the context of diagnostic accuracy, nor does it detail data provenance. The testing mentioned refers to "performance software evaluations." This suggests testing of the software's functionality and adherence to specifications, rather than a clinical trial with a patient test set.

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. The submission does not describe a study involving expert-established ground truth for a test set of patient data. The evaluation appears to be internal software testing and comparison to predicate devices, not a diagnostic accuracy study using a panel of experts.

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

Not applicable. The document does not describe a test set requiring adjudication by experts.

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 K040438 summary does not mention a multi-reader multi-case (MRMC) comparative effectiveness study. AEDs like the Powerheart AED G3A are designed to automatically detect and advise on shockable rhythms, and in this fully-automatic version, they also deliver the therapy automatically. The role of "human readers" in the context of an MRMC study for an AED's primary function is not typically applicable in the same way as for image-based diagnostic AI.

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

Yes, in essence, the "performance software evaluations" and the device's inherent design as a "fully-automatic" AED (meaning it diagnoses and delivers shock energy autonomously) can be considered a standalone performance assessment. The device's primary function is to operate independently of real-time human diagnostic input.

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

The ground truth for the software evaluations would have been based on established engineering and cardiac rhythm analysis standards, likely involving:

• Simulated ECG waveforms: Known waveforms (shockable vs. non-shockable) would have been input to verify correct classification and action.
• Performance standards: Adherence to established standards for defibrillator performance (e.g., energy delivery, charge time, shock advisory accuracy).
• Predicate device's algorithm performance: The software algorithm is stated to be "identical... as the current commercially distributed Powerheart® AED," implying that the ground truth for its core functionality would have been established during the clearance of prior versions.

8. The sample size for the training set

The document does not specify a training set sample size. Given that the software algorithm is "identical... as the current commercially distributed Powerheart® AED," it implies the training (if any Machine Learning was involved, which is less common for traditional AED algorithms relying on rule-based detection) would have occurred for previous versions, and this submission is primarily about hardware/operational modifications (fully-automatic functionality) and demonstrating the continued validated performance of the existing algorithm.

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

Not explicitly stated for this particular submission, as it focuses on equivalence. For the original development of an AED's rhythm analysis algorithm, the ground truth for a training set (if applicable) would typically be established by:

• Expert cardiologists/electrophysiologists: Annotation of ECG recordings to identify shockable rhythms (e.g., ventricular fibrillation, pulseless ventricular tachycardia) and non-shockable rhythms.
• Clinical outcomes data: Correlation of algorithm's outputs with actual patient outcomes (e.g., successful defibrillation, survival).
• Standardized databases: Use of well-characterized ECG databases (e.g., AHA/MIT-BIH) with expert-verified annotations.

In summary, this 510(k) submission for the Powerheart® AED G3A is primarily a declaration of substantial equivalence to previously cleared AEDs, particularly a fully-automatic version from another manufacturer, and a semi-automatic version of their own device. The performance testing described focuses on software functionality and ensuring that the conversion to fully-automatic operation did not negatively impact safety or effectiveness, rather than a de novo clinical study establishing diagnostic accuracy with a new AI algorithm.

Ask a specific question about this device