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The KardiaMobile Card System is intended to record, store and transfer single-channel electrocardiogram (ECG) rhythms. The KardiaMobile Card System also displays ECG rhythms and output of ECG analysis from AliveCor's KardiaAl platform including detecting the presence of normal sinus rhythm, atrial fibrillation, bradycardia, and others. The KardiaMobile Card System is intended for use by healthcare professionals, patients with known or suspected heart conditions and health-conscious individuals. The device has not been tested and is not intended for pediatric use.

The AliveCor KardiaMobile Card System is a single-channel ambulatory electrocardiogram (ECG) device that is intended to record, store, transfer, display, and analyze single-channel ECG rhythms in an ambulatory setting. The device utilizes the computing power of Apple iOS- and Google Android-based smartphones to obtain and analyze single-channel ECGs. These smartphones are termed Mobile Computing Platforms (MCPs). The device consists of the hardware (that has the electrodes), and the Kardia phone app (installed on an MCP). The same software is implemented in the iOS and Android MCP. In either configuration, the same hardware is used to sense the ECG. The KardiaMobile Card Hardware transmits the ECG signal from the electrode to the Kardia phone app on the MCP to be analyzed and presented to the user. All ECGs are synced with the user's account.

The provided text, K211668, is an FDA 510(k) clearance letter for the AliveCor KardiaMobile Card System. It outlines the device description, indications for use, and a comparison to a predicate device. However, it does not include detailed information regarding clinical study data to demonstrate the device meets acceptance criteria for the KardiaAI platform's specific ECG analysis functionalities (detecting normal sinus rhythm, atrial fibrillation, bradycardia, and others).

The "PERFORMANCE DATA" section primarily describes bench testing related to the hardware modifications (change in data transmission method from ultrasonic acoustics to BLE, and change in hardware material). It lists various IEC and ISO standards for ECG acquisition, transmission, biocompatibility, electrical safety, and electromagnetic compatibility.

Therefore, I cannot provide a complete answer to your request based on the provided text, as it lacks the specific clinical study data (acceptance criteria, reported performance, sample sizes, expert ground truth establishment, MRMC studies, standalone performance, training set details) related to the diagnostic performance of the KardiaAI platform itself.

The document states: "No modifications were made to the Kardia app software's clinical functionalities with respect to ECG acquisition, display, and analysis because of this change since its prior clearance under K191406, K182396 and K201985." This implies that the clinical performance evaluation of the KardiaAI platform was conducted during the clearance of the predicate devices (K191406, K182396, K201985), and this 510(k) focuses on the substantial equivalence of the new hardware (KardiaMobile Card) to those previously cleared systems.

To answer your question fully, information from the 510(k) submissions for K191406, K182396, and/or K201985 would be required, as they would contain the detailed clinical performance studies for the KardiaAI platform.

Based only on the provided K211668 document, I can infer the following about what is not present:

• No acceptance criteria for diagnostic performance: The document does not state specific accuracy, sensitivity, or specificity thresholds for detecting cardiac rhythms.
• No reported device performance for diagnostic accuracy: There are no tables or summaries of clinical performance metrics for the KardiaAI algorithms.
• No sample sizes for a clinical test set (for diagnostic performance): The document only mentions bench testing for hardware.
• No details on data provenance (for diagnostic performance): No information on origin, retrospective/prospective nature of a clinical test set.
• No information on experts for ground truth (for diagnostic performance): No details on number, qualifications, or adjudication methods for ECG interpretations.
• No MRMC study details: The document does not describe any human reader studies.
• No standalone (algorithm-only) performance data (for diagnostic performance): The focus is on hardware changes, not re-evaluation of the AI algorithms.
• No information on training set size or ground truth establishment for the AI algorithms.

In summary, the provided document K211668 does not contain the information requested about the acceptance criteria and the study that proves the device meets diagnostic performance acceptance criteria for the KardiaAI platform. It only addresses the substantial equivalence of the hardware changes for the KardiaMobile Card.

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KardiaAI is a software analysis library intended to assess ambulatory electrocardiogram (ECG) rhythms from adult subjects (when prescribed or used under the care of a physician). The device supports analyzing data recorded in compatible formats from any ambulatory ECG devices such as event recorders, or other similar devices. The library is intended to be integrated into other device software. The library is not intended for use in life supporting, or sustaining systems, or ECG monitors, or cardiac alarm, or OTC use only devices.

The KardiaAI library provides the following capabilities:

• Filtering ECG noise,
• Reporting heart rate measurement from ECGs,
• Detecting noisy ECGs.
• Reporting ECG rhythm analysis for the presence of sinus rhythm, atrial fibrillation, bradycardia, for ECGs detected as sinus rhythm, detecting normal sinus rhythm with with wide QRS, sinus rhythm with premature ventricular contractions (PVC), and sinus rhythm with supraventricular ectopy;
• Detecting QRS complexes in an ECG.
• For ECGs detected as sinus rhythm, classifying individual beats as a PVC or non-PVC beat, and
• Generating an average beat from an ECG

The device is not intended for use in patients who have pacemakers, ICDs, or other implanted electronic devices.

KardiaAI is a software library that implements various ECG processing and analysis algorithms. This Software as a Medical Device (SaMD) computes various physiologic parameters from an ECG and provides these capabilities in the form of an Application Program Interface (API) library. AliveCor-designed ECG devices ("target device") incorporate the API library into their device software to enable algorithmic analysis of ECGs to provide analytical capabilities. KardiaAI provides ECG processing functions, including ECG noise filtering and detection of noisy ECGs. It performs rhythm analysis on ECGs, specifically detecting atrial fibrillation, bradycardia, tachycardia and sinus rhythm, which can be further classified as normal sinus rhythm, sinus rhythm with wide QRS, sinus rhythm with premature ventricular contractions (PVCs), and sinus rhythm with supraventricular ectopy. It further provides beat-level annotations, including beat-level ORS locations, and, for sinus rhythm ECGs, PVC/not-PVC annotations. It also provides an average beat ECG representation, and the R-R interval tachogram. Recording and viewing of ECGs and the results of the KardiaAI analyses are to be provided by other AliveCor FDA-cleared devices (i.e., the target devices) into which the API library is incorporated, such as AliveCor's Triangle System (K183319) and KardiaMobile System (K182396).

The provided text describes the KardiaAI, a software analysis library intended to assess ambulatory electrocardiogram (ECG) rhythms. The information regarding acceptance criteria and the study proving the device meets these criteria is fragmented across different sections.

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

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

The document states that "All analysis outputs were found to meet their performance specifications" and "it was found that the subject device demonstrated equivalent performance to the predicate device." However, specific numerical acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds) and their corresponding reported device performance values are not explicitly detailed in the provided text. The table below represents the types of performance claimed to be met, but the precise numerical targets and outcomes are absent.

2. Sample size 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 explicitly stated. The document mentions an "AliveCor proprietary ECG database" and "databases from the ANSVAAMI EC57" were used for algorithm performance testing. No specific number of ECGs or patients is given for either database.
• Data Provenance:
  • AliveCor proprietary ECG database: No information on country of origin.
  • ANSVAAMI EC57 databases: No information on country of origin.
  • Retrospective or Prospective: 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. The method for establishing ground truth is mentioned as "AliveCor proprietary ECG database" and "databases from the ANSVAAMI EC57", but details on expert involvement and qualifications are missing.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study focused on human readers improving with AI assistance was not described in the provided text. The document refers to "comparative testing" between the subject device and the predicate device's algorithm performance, but this is a comparison of algorithms, not human readers with and without AI assistance.

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

Yes, a standalone algorithm-only performance study was conducted. The "Nonclinical Testing Summary" states: "Specifically, algorithm performance testing was assessed using an AliveCor proprietary ECG database. Additional comparative testing was also performed on databases from the ANSVAAMI EC57. All analysis outputs were found to meet their performance specifications." This indicates testing of the algorithm's performance independent of human intervention.

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

The document does not explicitly state the specific type of ground truth used. It mentions using an "AliveCor proprietary ECG database" and "databases from the ANSVAAMI EC57" for algorithm performance testing. This implies that these databases contained pre-established "ground truth" annotations for the ECGs, but the method by which that ground truth was established (e.g., expert interpretation, comparison to other diagnostic tests) is not detailed.

8. The sample size for the training set

The document does not provide the sample size for the training set. It only mentions the databases used for "algorithm performance testing," which typically refers to evaluation on a test set, distinct from a training set.

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

The document does not provide information on how the ground truth for the training set was established, nor does it explicitly mention details about a training set.

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