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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是一款软件分析库，旨在评估成人受试者的动态心电图（ECG）心律。

1. 验收标准和报告的设备性能表

2. 测试集使用的样本量和数据来源（例如，数据来源国、回顾性或前瞻性）

• 测试集样本量： 未明确说明具体的样本量。报告指出，算法性能测试使用了来自“ANSI/AAMI EC57:2012标准”的ECG数据库以及“AliveCor专有数据库”。这些标准数据库通常包含大量的ECG记录，但具体用于测试的样本量未公布。
• 数据来源：
  • 国家/地区： 未明确说明数据的具体来源国家。
  • 回顾性/前瞻性： 报告未明确说明数据的回顾性或前瞻性。ANSI/AAMI EC57:2012标准数据库通常是回顾性的，AliveCor的专有数据库也可能是回顾性的。

3. 用于建立测试集真实值的专家数量和专家资质（例如，拥有10年经验的放射科医生）

报告中未明确说明用于建立测试集真实值的专家数量和专家资质。

4. 测试集的裁决方法（例如，2+1、3+1、无）

报告中未明确说明裁决方法。由于使用了ANSI/AAMI EC57:2012标准和AliveCor专有数据库，通常这类数据库的真实值会经过心电图专家的人工判读和多方共识。

5. 是否进行了多读者多案例（MRMC）比较效果研究，如果是，人类读者在AI辅助下相对于没有AI辅助的改进效果量是多少

报告中未提及MRMC比较效果研究。本备案文件主要关注KardiaAI算法的独立性能，而非人类读者在AI辅助下的表现。

6. 是否进行了独立（即，仅算法，无人机参与性能）研究

是的，报告明确指出进行了独立研究。非临床测试的重点是评估KardiaAI算法的性能，验证其是否按预期执行。测试结果证明KardiaAI的性能符合其规范，并达到其预期用途。报告还提到，“KardiaAI和Kardia Band System之间的重叠算法满足了相同的性能验收标准。”

7. 使用的真实值类型（专家共识、病理学、结果数据等）

基于“ANSI/AAMI EC57:2012标准”的ECG数据库和“AliveCor专有数据库”的使用，真实值很可能基于专家共识。标准ECG数据库的注释通常由经过认证的心电图专家或心脏病专家进行，以确保准确性。

8. 训练集样本量

报告中未明确说明训练集的样本量。

9. 训练集真实值是如何建立的

报告中未明确说明训练集真实值是如何建立的。但是，鉴于测试集真实值的可能建立方式（专家共识）以及这类医疗AI设备的典型开发流程，训练集的真实值很可能也是通过专家共识或由临床专家对ECG数据进行人工标注来建立的。

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