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The ECG-AI LEF 12-Lead algorithm is software intended to aid in earlier detection of Left Ventricular Ejection Fraction (LVEF) less than or equal to 40% in adults at risk for heart failure. This population includes, but is not limited to:

• patients with cardiomyopathies
• patients who are post-myocardial infarction
• patients with aortic stenosis
• patients with chronic atrial fibrillation
• patients receiving pharmaceutical therapies that are cardiotoxic, and
• postpartum women.

The ECG-AI LEF 12-Lead algorithm is not intended to be a stand-alone diagnostic device for cardiac conditions, should not be used for monitoring of patients, and should not be used on ECGs with a paced rhythm.

A positive result may suggest the need for further clinical evaluation in order to establish a diagnosis of Left Ventricular Ejection Fraction (LVEF) less than or equal to 40%. Additionally, if the patient is at high risk for the cardiac condition, a negative result should not rule out further non-invasive evaluation.

The ECG-AI LEF 12-Lead Algorithm should be applied jointly with clinician judgment.

The ECG-AI LEF 12-Lead algorithm interprets 12-lead ECG voltage times series data using an artificial intelligence-based algorithm. The device analyzes 10 seconds of a single 12-lead ECG acquisition, and within seconds provides likelihood of LVEF (ejection fraction less than or equal to 40%) to third party software. The results are displayed by the third party software on a device such as a smartphone, tablet, or PC. The ECG-AI LEF 12-Lead algorithm was trained to detect Low LVEF using positive and control cohorts, and the detection of Low LVEF in patients is generated using defined conditions and covariates.

The ECG-AI LEF 12-Lead algorithm device is intended to address the unmet need for a point-of-care screen for LVEF less than or equal to 40% and is expected to be used by cardiologists, frontline clinicians at primary care, urgent care, and emergency care settings, where cardiac imaging may not be available or may be difficult or unreliable for clinicians to operate. Clinicians will use the ECG-AI LEF 12-Lead algorithm to aid in earlier detection of LVEF less than or equal to 40% and making a decision for further cardiac evaluation.

The software module can be integrated into a client application to be accessed by clinicians and results viewed through an Electronic Medical Record (EMR) system or an ECG Management System (EMS) accessed via a PC, mobile device, or another medical device. In each case, the physician imports 12-lead ECG data in digital format. The tool analyzes the 10 seconds or longer duration of voltage data collected during a standard 12-lead ECG and outputs a binary result of the likelihood of low ejection fraction as an API result.

The provided text is a 510(k) clearance letter and summary for the Anumana, Inc. ECG-AI Low Ejection Fraction (LEF) 12-Lead Algorithm ([K250652](https://510k.innolitics.com/search/K250652)). While it describes the device, its intended use, and substantial equivalence to a predicate device, it does not contain the detailed performance study results, acceptance criteria tables, sample sizes, or ground truth establishment methods that would typically be found in the clinical study section of a full 510(k) submission.

The document discusses a "Predetermined Change Control Plan (PCCP)" which mentions future performance enhancement validation studies, but it doesn't present the specific results of the validation study that led to this clearance ([K250652](https://510k.innolitics.com/search/K250652)). It only states that "The performance characteristics for the ECG-AI LEF 12-Lead algorithm were evaluated through software verification and labeling verification," which refers to non-clinical data.

Therefore, many of the requested details cannot be extracted from the provided text. I will populate the table and answer the questions based only on the information available in the given document.

Acceptance Criteria and Device Performance Study (Extracted from provided 510(k) Summary)

The provided 510(k) summary (K250652) serves as an update to a previously cleared device (K232699). It focuses on expanding compatibility and minor changes, asserting substantial equivalence based on the predicate's performance rather than detailing a new, comprehensive clinical study for this specific submission. The document emphasizes "software verification and labeling verification" as the evaluation methods for performance characteristics for this particular submission, rather than a clinical performance study with specific metrics for acceptance criteria.

The Predetermined Change Control Plan (PCCP) section alludes to future performance enhancements and their validation, stating: "To be implemented, a modified version must demonstrate improved performance by meeting pre-specified acceptance criteria. These criteria require the new version's sensitivity and specificity point estimates to be greater than or equal to the previous version, with an improvement shown by either an increased point estimate or a tighter confidence interval lower bound for at least one of these metrics." However, these are future criteria for updates, not the current acceptance criteria for the clearance of K250652 based on a new clinical study.

Therefore, the specific quantitative acceptance criteria and reported device performance for the clinical study supporting the K250652 clearance are not explicitly stated in the provided text. The clearance is largely based on demonstrating substantial equivalence to the predicate (K232699) and software/labeling verification.

Based on the provided text, the specific details regarding the clinical performance study (including acceptance criteria, reported performance values, sample sizes, expert details, adjudication methods, MRMC studies, standalone performance, and ground truth establishment for the test set) are NOT available.

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

As noted above, the provided text does not contain a table of explicit quantitative acceptance criteria or reported device performance metrics (e.g., sensitivity, specificity, AUC) from a clinical study for K250652. The document claims "The performance characteristics for the ECG-AI LEF 12-Lead algorithm were evaluated through software verification and labeling verification" for this submission, indicating that a new, detailed clinical performance study with such metrics was not the basis for this specific clearance. The PCCP section specifies criteria for future updates, but not for this clearance.

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 specified in the provided document.
• Data Provenance: Not specified in the provided document. The PCCP mentions "multi-center retrospective clinical study" for future validations, but this isn't linked to the original clearance's 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 specified in the provided document.

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

• Not specified in the provided 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

• Not specified in the provided document. The current indication is "to aid in earlier detection" and "applied jointly with clinician judgment," which implies human-in-the-loop, but an MRMC study comparing performance with and without AI assistance is not detailed.

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

• The document states: "The ECG-AI LEF 12-Lead algorithm is not intended to be a stand-alone diagnostic device for cardiac conditions," and "should be applied jointly with clinician judgment." This implies the device is not intended for standalone use in practice. However, whether a standalone performance study was conducted to assess its raw diagnostic capability (e.g., area under the curve) is not explicitly stated. The statement "outputs a binary result of the likelihood of low ejection fraction as an API result" suggests a standalone algorithm output, but the FDA's clearance is for an "aid," not a primary diagnostic tool.

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

• The document mentions the device "was trained to detect Low LVEF using positive and control cohorts." For LVEF, the common ground truth is often echocardiography (measuring ejection fraction), but the specific method used for ground truth (e.g., echocardiography, MRI, or a combination/adjudication) is not specified.

8. The sample size for the training set

• Not specified in the provided document.

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

• The document states the device "was trained to detect Low LVEF using positive and control cohorts," but it does not describe how the ground truth was established for these training cohorts (e.g., type of diagnostic test, clinical adjudication process).

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The Anumana Low Ejection Fraction AI-ECG Algorithm is software intended to aid in screening for Left Ventricular Ejection Fraction (LVEF) less than or equal to 40% in adults at risk for heart failure. This population includes, but is not limited to:
· patients with cardiomyopathies

• patients who are post-myocardial infarction
• · patients with aortic stenosis
• · patients with chronic atrial fibrillation
• · patients receiving pharmaceutical therapies that are cardiotoxic, and
  • postpartum women.

Anumana Low Ejection Fraction Al-ECG Algorthm is not intended to be a stand-alone diagnostic device for cardiac conditions, should not be used for monitoring of patients, and should not be used on ECGs with a paced rhythm.

A positive result may suggest the need for further clinical evaluation in order to establish a diagnosis of Left Ventricular Ejection Fraction (LVEF) less than or equal to 40%. Additionally, if the patient is at high risk for the cardiac condition, a negative result should not rule out further non-invasive evaluation.

The Anumana Low Ejection Fraction AI-ECG Algorithm should be applied jointly with clinician judgment.

The Low Ejection Fraction AI-ECG Algorithm interprets 12-lead ECG voltage times series data using an artificial intelligence-based algorithm. The device analyzes 10 seconds of a single 12lead ECG acquisition, and within seconds provides a prediction of likelihood of LVEF (ejection fraction less than or equal to 40%) to third party software. The results are displayed by the third-party software on a device such as a smartphone, tablet, or PC. The Low Ejection Fraction AI-ECG Algorithm was trained to predict Low LVEF using positive and control cohorts, and the prediction of Low LVEF in patients is generated using defined conditions and covariates. The Low Ejection Fraction AI-ECG Algorithm device is intended to address the unmet need for a point-of-care screen for LVEF less than or equal to 40% and is expected to be used by cardiologists, front-line clinicians at primary care, urgent care, and emergency care settings, where cardiac imaging may not be available or may be difficult or unreliable for clinicians to operate. Clinicians will use the Low Eiection Fraction AI-ECG Algorithm to aid in screening for LVEF less than or equal to 40% and making a decision for further cardiac evaluation.

Here's a breakdown of the acceptance criteria and the study proving the device meets those criteria, based on the provided FDA 510(k) clearance letter for the Low Ejection Fraction AI-ECG Algorithm:

Low Ejection Fraction AI-ECG Algorithm: Acceptance Criteria and Performance Study

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: The clinical validation study included 16,000 patient records initially, though 2,040 records were excluded due to quality checks, resulting in a final analysis sample of 13,960 patient-ECG pairs.
• Data Provenance: The data was retrospective, collected from 4 health systems across the United States.

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

The document does not specify the number of experts or their qualifications used to establish the ground truth for the clinical validation test set. The ground truth (LVEF <= 40% or > 40%) was derived from transthoracic echocardiogram (TTE) measurements. While TTE interpretation requires expertise, the document doesn't detail the method of expert review or consensus for these TTE results themselves for the test set.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1) for the ground truth for the test set. The ground truth was established by TTE measurements.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The study evaluated the standalone performance of the AI algorithm against a ground truth without human readers in the loop.

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

Yes, a standalone performance study was done. The reported sensitivity and specificity values are for the algorithm's performance alone in detecting low LVEF.

7. The Type of Ground Truth Used

The type of ground truth used for both training and validation was objective clinical measurements from Transthoracic Echocardiogram (TTE), specifically the Left Ventricular Ejection Fraction (LVEF) measurement. An LVEF of $\le$ 40% was defined as the disease cohort, and > 40% as the control cohort.

8. The Sample Size for the Training Set

The training set for the algorithm development consisted of 93,722 patients with an ECG and TTE performed within a 2-week interval. These were split into:

• Training dataset: 50% of the 93,722 patients.
• Tuning dataset: 20% of the 93,722 patients.
• Set-aside testing dataset: 30% of the 93,722 patients (used for internal validation during development, distinct from the independent clinical validation study).

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

The ground truth for the training set was established using LVEF measurements obtained from transthoracic echocardiograms (TTE). Specifically, for each patient, the LVEF measurement from the earliest TTE within a 2-week interval of an ECG was paired with the closest ECG recording. LVEF $\le$ 40% defined the disease cohort, and LVEF > 40% defined the control cohort. This data was identified from a research-use authorized clinical database from Mayo Clinic.

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