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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 KardiaMobile System is intended to record, store and transfer single-channel electrocardiogram (ECG) rhythms. The KardiaMobile 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 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 KardiaMobile System is a trans-telephonic (transmission by telephone) ECG (electrocardiogram) event recorder that records, stores and transfers single-channel electrocardiogram rhythms. The device utilizes the computing power of Apple iOS- and Google Android-based smartphones to obtain and analyze single-channel ECG. 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 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 describes the acceptance criteria and the study conducted for the KardiaMobile System, primarily focusing on proving that the device meets special controls for Electrocardiograph Software for Over-the-Counter Use, especially after the removal of a "clinician overread" function.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not provide a direct table of numerical "acceptance criteria" (e.g., minimum sensitivity/specificity percentages) and corresponding "reported device performance" values for the AI algorithm suite (KardiaAI SaMD). Instead, it states that the performance characteristics (sensitivity and specificity) were "tested to meet the system requirements" against ANSI/AAMI EC57:2012 databases and AliveCor's proprietary databases.

However, the "Special Control" table implicitly functions as acceptance criteria for different aspects of the device's performance and the "Summary of Conformance" column indicates the reported performance/compliance.

Acceptance Criteria (Implicit from Special Controls) and Reported Device Performance (Summary of Conformance):

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

• Sample Size for Algorithmic Performance (KardiaAI SaMD): The document states that the KardiaAI SaMD (K181823), leveraged by the KardiaMobile System for ECG analysis, was validated using "ANSI/AAMI EC57:2012 databases and AliveCor's proprietary databases." It also notes that these "validation datasets are representative of the patient population of the proposed device."
  • Specific sample sizes are NOT provided for these databases.
  • Data Provenance: Not explicitly stated regarding country of origin. The use of "ANSI/AAMI EC57:2012 databases" suggests a standardized, likely diverse, source, while "AliveCor's proprietary databases" could be from various global or specific regions. The document does not specify if the data was retrospective or prospective for the algorithmic validation, but typically such databases are compiled retrospectively.
• Sample Size for Human Factors and Usability Testing: Not explicitly stated, but it refers to "representative users."

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

• For Algorithmic Performance (KardiaAI SaMD): The document states that the detection algorithm performance was validated using a "previously adjudicated data set" (Special Control 3) and mentions that for the primary predicate, the "overread unlock" mechanism involved review by a "board-certified cardiologist." However, it does not explicitly state the number or specific qualifications (e.g., years of experience) of experts used to establish the ground truth for the test sets used for the KardiaAI algorithm validation. Adjudicated data implies expert review, but details are absent.

4. Adjudication Method for the Test Set

• For Algorithmic Performance (KardiaAI SaMD): The document mentions "previously adjudicated data set." No specific adjudication method (e.g., 2+1, 3+1) is detailed.

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

• No MRMC comparative effectiveness study, comparing human readers with AI vs. without AI assistance, is mentioned. The focus of the changes and testing described is on the device's standalone performance and human factors/usability for over-the-counter use after removing the "overread" requirement.

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

• Yes. The document explicitly states: "The KardiaMobile System leverages the KardiaAI SaMD (K181823) for ECG analysis. KardiaAI algorithm suite ECG detection algorithm outputs of Atrial Fibrillation, Normal, Bradycardia, Tachycardia, and Noise as well as the heart rate calculations were tested to meet the system requirements for sensitivity and specificity." This indicates that the core AI algorithm's performance was evaluated independently (without human-in-the-loop for its direct analytical output).

7. The Type of Ground Truth Used

• For Algorithmic Performance (KardiaAI SaMD): "Previously adjudicated data set." This typically implies expert consensus (e.g., cardiologists reviewing ECGs). It does not mention pathology or outcomes data as the ground truth directly for the AI algorithm's performance.

8. The Sample Size for the Training Set

• Not specified. The document focuses on the validation/test sets (ANSI/AAMI EC57:2012 and AliveCor's proprietary databases) for the KardiaAI algorithm. Information regarding the training set's size is not provided.

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

• Not specified. As the training set size itself is not mentioned, neither is the method for establishing its ground truth.

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