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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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The KardiaAI is a software analysis library intended to assess ambulatory electrocardiogram (ECG) rhythms from adult subjects. 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 cardiac alarm, or OTC use only devices.

The KardiaAI library provides the following capabilities:

• ECG noise filtering.
• heart rate measurement from ECGs,
• detection of noisy ECGs, and
• ECG rhythm analysis for detecting the presence of normal sinus rhythm, atrial fibrillation, bradycardia (when prescribed or used under the care of a physician).

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 a 30-second ECG and provides these capabilities in the form of an Application Program Interface (API) library. ECG devices can incorporate the API library into ECG device ("target device") software to enable algorithmic analysis of ECGs to provide analytical capabilities. The device provides ECG noise filtering and detection of noisy ECGs as well as identifies normal sinus rhythm, atrial fibrillation, bradycardia, and tachycardia.

The KardiaAI device, a software analysis library for assessing ambulatory ECG rhythms, underwent non-clinical testing to demonstrate its performance and substantial equivalence to predicate devices.

1. Table of Acceptance Criteria and Reported Device Performance

The provided text states that "The overlapping AF, NSR, and noise algorithms for KardiaAI and Kardia Band System met the same performance criteria." It also mentions that "Testing also ensured that differences in technological characteristics between the KardiaAI and the Kardia Band System (primary predicate) (i.e., bradycardia and tachycardia algorithms as well as multilead ambulatory ECG input) perform as intended and do not raise different questions of safety or effectiveness."

However, specific numerical acceptance criteria (e.g., sensitivity, specificity thresholds for AF, NSR, bradycardia, tachycardia, or noise detection) and the corresponding reported device performance values are not explicitly detailed in the provided document. The document primarily describes that acceptance criteria were met and that the device performs as intended, rather than listing the criteria themselves with quantitative results.

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

The document states that "Algorithm performance testing was assessed using ECG databases from the ANSI/AAMI EC57:2012 standard as well as AliveCor proprietary databases."

• Sample Size: The specific sample sizes (number of ECGs or patients) from the ANSI/AAMI EC57:2012 standard databases and the AliveCor proprietary databases used for testing are not explicitly stated.
• Data Provenance:
  • ANSI/AAMI EC57:2012 standard databases: These are standardized databases, typically containing diverse ECG recordings established for performance evaluation of ECG devices. The country of origin is not specified but these are internationally recognized standards.
  • AliveCor proprietary databases: The country of origin is not explicitly stated, nor is whether the data is retrospective or prospective.

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

The document does not explicitly state the number of experts used to establish the ground truth for the test set or their specific qualifications. It implicitly refers to "ECG databases from the ANSI/AAMI EC57:2012 standard" which typically have established ground truths, but the methodology for these specific tests is not detailed.

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method used for the test set (e.g., 2+1, 3+1, none).

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study, nor does it provide an effect size for human readers improving with AI vs. without AI assistance. The testing described is focused on the standalone algorithm performance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, a standalone study was done. The document explicitly states: "Non-clinical testing was conducted to assess algorithm performance and to verify that KardiaAI performs as intended. Algorithm performance testing was assessed using ECG databases..." This indicates a focus on the algorithm's performance in isolation.

7. The Type of Ground Truth Used

The ground truth for the test set seems to be derived from expert consensus embedded within the "ECG databases from the ANSI/AAMI EC57:2012 standard" and "AliveCor proprietary databases." While not explicitly stated, standardized ECG databases are typically annotated by cardiologists or other qualified experts, aligning with an expert consensus type of ground truth.

8. The Sample Size for the Training Set

The document does not explicitly state the sample size used for the training set. It only mentions the databases used for testing.

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

The document does not explicitly state how the ground truth for the training set was established. While it implies the use of "AliveCor proprietary databases," the method for their ground truth annotation (e.g., expert review, pathology, outcomes data) is not detailed.

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