(23 days)
Yes
The device description explicitly states that it uses an "artificial intelligence-based algorithm" and was "trained to predict Low LVEF". The "Mentions AI, DNN, or ML" section also repeatedly refers to the algorithm as "artificial intelligence-based" and "Machine learning based algorithm". The regulation name is also listed as "Cardiovascular machine learning-based notification software".
No
The device is described as software intended to aid in screening for LVEF less than or equal to 40% and is not intended to be a stand-alone diagnostic device for cardiac conditions or for monitoring patients. It is used to generate a prediction of the likelihood of low LVEF rather than providing direct therapy.
No
The intended use statement explicitly states, "Anumana Low Ejection Fraction Al-ECG Algorthm is not intended to be a stand-alone diagnostic device for cardiac conditions". It is described as software intended to "aid in screening" and to "aid in screening for LVEF less than or equal to 40% and making a decision for further cardiac evaluation."
Yes
The device description explicitly states that the subject device is a "Software as a Medical Device (SaMD) provided as a software module packaged in a Docker container." It processes existing 12-lead ECG data, which is acquired by separate, compatible ECG machines (hardware). The device itself is the software algorithm that interprets this data.
Based on the provided information, the Anumana Low Ejection Fraction AI-ECG Algorithm is an IVD (In Vitro Diagnostic).
Here's why:
- Intended Use: The intended use explicitly states that the software is "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 is a diagnostic purpose, even though it's described as an "aid in screening" and "not intended to be a stand-alone diagnostic device." The output is a prediction of the likelihood of a specific medical condition (Low LVEF).
- Input Data: The device analyzes "12-lead ECG voltage times series data," which is physiological data obtained from a patient. While ECGs are typically considered in vivo measurements, the analysis of this data by the software to provide a diagnostic aid falls under the scope of IVD regulations in many jurisdictions. The software is processing biological signals to provide information about a patient's health status.
- Output: The output is a "prediction of likelihood of LVEF (ejection fraction less than or equal to 40%)". This is a result that is used to inform clinical decision-making regarding a potential medical condition.
- Regulatory Context: The mention of "Regulation Name: Cardiovascular machine learning-based notification software" and the listing of predicate devices (DEN230003; VIZ HCM) which are also likely regulated medical devices, further supports its classification as a medical device, and given the nature of the input and output, an IVD.
While the device is not a traditional laboratory test analyzing biological samples like blood or urine, the regulatory definition of an IVD often includes devices that analyze data derived from the human body to provide diagnostic information. The Anumana Low Ejection Fraction AI-ECG Algorithm fits this description by analyzing ECG data to aid in the screening for a specific cardiac condition.
Yes
The letter explicitly states that the submission proactively pre-specifies intended modifications that do not require additional marketing submission, which is the definition of a PCCP.
Intended Use / Indications for Use
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 AI-ECG 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 Anumana Low Ejection Fraction AI-ECG Algorithm should be applied jointly with clinician judgment.
Product codes
QYE
Device Description
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 12-lead 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 Ejection Fraction AI-ECG Algorithm to aid in screening for LVEF less than or equal to 40% and making a decision for further cardiac evaluation.
Mentions image processing
Not Found
Mentions AI, DNN, or ML
The Low Ejection Fraction AI-ECG Algorithm interprets 12-lead ECG voltage times series data using an artificial intelligence-based algorithm.
To develop the Anumana Low Ejection Fraction AI-ECG (Low EF ECG-AI) algorithm, 93,722 patients with an ECG and transthoracic echocardiogram (TTE) performed within a 2-week interval were identified from a research use authorized clinical database from Mayo Clinic.
The Anumana Low EF ECG-AI device achieved a sensitivity of 84.5% (95% CI of 82.2% to 86.6%), a specificity of 83.6% (95% CI of 82.9% to 84.2%), a positive predictive value of 30.5% (PPV, 95% CI of 28.8% to 32.1%), and a negative predictive value of 98.4% (NPV, 95% CI of 98.2% to 98.7%).
Machine learning based algorithm with a predetermined change control plan (PCCP)
Input Imaging Modality
12-lead ECG waveform in digital format
Anatomical Site
Heart (Left Ventricular Ejection Fraction)
Indicated Patient Age Range
Adults
Intended User / Care Setting
cardiologists, front-line clinicians at primary care, urgent care, and emergency care settings.
Description of the training set, sample size, data source, and annotation protocol
To develop the Anumana Low Ejection Fraction AI-ECG (Low EF ECG-AI) algorithm, 93,722 patients with an ECG and transthoracic echocardiogram (TTE) performed within a 2-week interval were identified from a research use authorized clinical database from Mayo Clinic. For each patient, the left ventricular ejection fraction (LVEF) measurement from the earliest TTE was selected and paired with the closest ECG recording within a 2-week interval of the TTE. The disease cohort was defined as LVEF 40% and consisted of 89% of the overall cohort. The cohorts were split into training (50%), tuning (20%) and set-aside testing (30%) datasets.
Description of the test set, sample size, data source, and annotation protocol
Within the set-aside test set, the sensitivity and specificity of the algorithm were 85.7% and 84.2%, respectively.
The performance profile of the Anumana Low Ejection Fraction AI-ECG (Low EF AI-ECG) algorithm was validated in a retrospective study of 16,000 patient records across 4 health systems across the United States and 16,000 patients. The sole inclusion criteria for a subject was age >= 18 and the availability of at least one digital 12-lead ECG paired with an echocardiogram with quantitative left ventricular ejection fraction (LVEF) information within 30 days following the date of the ECG. Candidate cases were identified in reverse chronological order from the date the site was activated. For each patient, the most recent echocardiogram was paired with the most recent ECG (without pacing present) acquired for that patient prior to the echocardiogram, up to the 30 days limit. Four models of 12-L ECGs (GE Dash 3000, GE MAC® 5500 Resting Analysis System, GE MAC VU360 Resting ECG, and Philips PageWriter TC70 ECG machines (model 860315) were represented in the ECG inputs to the algorithm.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Study Type: Retrospective study
Sample Size: 16,000 patient records
Key Results: The Anumana Low EF ECG-AI device achieved a sensitivity of 84.5% (95% CI of 82.2% to 86.6%), a specificity of 83.6% (95% CI of 82.9% to 84.2%), a positive predictive value of 30.5% (PPV, 95% CI of 28.8% to 32.1%), and a negative predictive value of 98.4% (NPV, 95% CI of 98.2% to 98.7%).
Subgroup Analysis: Diagnostic performance varied across age strata (p
N/A
FDA 510(k) Clearance Letter - Low Ejection Fraction AI-ECG Algorithm
Page 1
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
Doc ID # 04017.06.03
September 28, 2023
Anumana, Inc.
℅ Alexia Haralambous
Senior Principal
RQM+
2251 San Diego Ave, Suite B-257
San Diego, California 92110
Re: K232699
Trade/Device Name: Low Ejection Fraction AI-ECG Algorithm
Regulation Number: 21 CFR 870.2380
Regulation Name: Cardiovascular Machine Learning-Based Notification Software
Regulatory Class: Class II
Product Code: QYE
Dated: September 3, 2023
Received: September 5, 2023
Dear Alexia Haralambous:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal
Page 2
K232699 - Alexia Haralambous
Page 2
statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting (reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Robert T. Kazmierski -S
for
LCDR Stephen Browning
Assistant Director
Division of Cardiac Electrophysiology,
Diagnostics, and Monitoring Devices
Office of Cardiovascular Devices
Office of Product Evaluation and Quality
Center for Devices and Radiological Health
Enclosure
Page 3
FORM FDA 3881 (8/23)
Page 1 of 1
PSC Publishing Services (301) 443-6740 EF
DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration
Indications for Use
Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.
510(k) Number (if known): K232699
Device Name: Low Ejection Fraction AI ECG Algorithm
Indications for Use (Describe)
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 AI-ECG 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 Anumana Low Ejection Fraction AI-ECG Algorithm should be applied jointly with clinician judgment.
Type of Use (Select one or both, as applicable)
☒ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)
CONTINUE ON A SEPARATE PAGE IF NEEDED.
This section applies only to requirements of the Paperwork Reduction Act of 1995.
DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.
The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:
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"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."
Page 4
510(k) Summary
Low Ejection Fraction AI ECG Algorithm
Applicant Name: Anumana, Inc.
One Main Street, Suite 400
East Arcade 4th Floor
Cambridge, MA 02142
Contact Person: Suzanne Goodman
VP Regulatory and Quality
Anumana, Inc.
Tel. (919) 608-6082
sgoodman@nference.net
Consultant: Alexia Haralambous, MS, RAC
Allison Komiyama, Ph.D, RAC
Erin Gontang, Ph.D.
RQM+
aharalambous@rqmplus.com
Tel. (267) 778-5114
Date Summary Prepared: September 22, 2023
Proprietary Name of Device: Low Ejection Fraction AI ECG Algorithm
Common or Usual Name: ECG AI analysis tool
Classification Panel: Cardiology
Regulation Number: 21 CFR § 870.2380
Regulation Name: Cardiovascular machine learning-based notification software
Regulation Class: Class II
Product Code: QYE
1 Predicate Device
Device Name: VIZ HCM
Manufacturer: Viz.ai
Application Number: DEN230003
2 Device Description
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 12-lead 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
K232699 Page 1 of 9
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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 Ejection Fraction AI-ECG Algorithm to aid in screening for LVEF less than or equal to 40% and making a decision for further cardiac evaluation.
3 Intended Use/Indications for Use
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 AI-ECG 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 Anumana Low Ejection Fraction AI-ECG Algorithm should be applied jointly with clinician judgment.
4 Technological Characteristics
The subject device, Anumana's Low Ejection Fraction AI-ECG Algorithm, is a Software as a Medical Device (SaMD) provided as a software module packaged in a Docker container. The Low Ejection Fraction AI-ECG Algorithm does not provide a graphical user interface (GUI) of its own. It is integrated with other medical systems such as Electronic Medical Record (EMR) systems or ECG Management Systems (EMS). The third-party integrating software furnishes a 12-lead ECG digital waveform as input to the Low Ejection Fraction AI-ECG Algorithm and records the algorithm output for display via the integrated medical system or for printing in an offline report.
5 Summary of Clinical Performance Data
The performance characteristics for the Low Ejection Fraction AI-ECG Algorithm have been clinically validated for the presence of a low ejection fraction of less than or equal to 40% in patients, with a sensitivity and specificity greater than the study's predetermined acceptance criteria (80%).
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Table 1: Performance Characteristics
| Performance Characteristic | Value |
|---|---|
| Sensitivity | 80% or higher |
| Specificity | 80% or higher |
Algorithm Training
To develop the Anumana Low Ejection Fraction AI-ECG (Low EF ECG-AI) algorithm, 93,722 patients with an ECG and transthoracic echocardiogram (TTE) performed within a 2-week interval were identified from a research use authorized clinical database from Mayo Clinic. For each patient, the left ventricular ejection fraction (LVEF) measurement from the earliest TTE was selected and paired with the closest ECG recording within a 2-week interval of the TTE. The disease cohort was defined as LVEF ≤ 40% and consisted of 11% of the overall cohort. The control cohort was defined as LVEF > 40% and consisted of 89% of the overall cohort. The cohorts were split into training (50%), tuning (20%) and set-aside testing (30%) datasets. Within the set-aside test set, the sensitivity and specificity of the algorithm were 85.7% and 84.2%, respectively.
Performance analyses were also conducted in subgroups of age, sex, and race. Within the training set-aside test dataset, 91.2% of patients were White, 1.4% Black/African American, 1.3% Asian, 0.4% American Indian and 5.7% were of other or unknown races. Patients were 57.6% male and 43.4% female. The average age was 61.5 (± 16.4) years and 11.2% of patients were under age 40, 10.0% were 40-49, 18.1% were 50-59, 25.9% were 60-69, 22.3% were 70-79, and 12.5% were over 80.
Summary of Clinical Validation
Study Design
The performance profile of the Anumana Low Ejection Fraction AI-ECG (Low EF AI-ECG) algorithm was validated in a retrospective study of 16,000 patient records across 4 health systems across the United States and 16,000 patients. The objective of the study was to establish the diagnostic performance of the device for the purpose of detecting the presence of an EF ≤ 40% in a clinically and demographically diverse population. The sole inclusion criteria for a subject was age ≥ 18 and the availability of at least one digital 12-lead ECG paired with an echocardiogram with quantitative left ventricular ejection fraction (LVEF) information within 30 days following the date of the ECG. Candidate cases were identified in reverse chronological order from the date the site was activated. For each patient, the most recent echocardiogram was paired with the most recent ECG (without pacing present) acquired for that patient prior to the echocardiogram, up to the 30 days limit. Four models of 12-L ECGs (GE Dash 3000, GE MAC® 5500 Resting Analysis System, GE MAC VU360 Resting ECG, and Philips PageWriter TC70 ECG machines (model 860315) were represented in the ECG inputs to the algorithm.
Summary of Study Results
Each of the 4 sites contributed 4,000 patient-ECG pairs to a final pool of 16,000 patient-ECG pairs. The study sample was representative of the US population and was 65.9% White, 11.0%
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Hispanic, 10.1% Black/African American, 5.3% American Indian or Alaska Native, 2.2% Asian, and 0.4% Native Hawaiian or Pacific Islander. The sample consisted of 52% male and 48% female participants. The average age as 66 and 11.6% of participants were under age 40, 9.2% were 40-49, 15.8% were 50-59, 22.8% were 60-69, 21.6% were 70-79, and 19% were over 80. A total of 2,040 records were excluded as a result of the quality checks performed by the Anumana Low Ejection Fraction AI-ECG (Low EF AI-ECG) algorithm.
Within this diverse study sample, a total of 1,096 LVEF ≤ 40% cases were identified (prevalence of 7.9%) from 13,960 samples. The Anumana Low EF ECG-AI device achieved a sensitivity of 84.5% (95% CI of 82.2% to 86.6%), a specificity of 83.6% (95% CI of 82.9% to 84.2%), a positive predictive value of 30.5% (PPV, 95% CI of 28.8% to 32.1%), and a negative predictive value of 98.4% (NPV, 95% CI of 98.2% to 98.7%). Results are provided in Figure 1: Matrix of primary results with subgroup analyses results provided in Table 2: Subgroup Analyses.
Figure 1: Matrix of primary results
Specificity Sensitivity
83.6% (83.0%, 84.3%) 84.5% (82.2%, 86.6%)
+ 2,122 928 PPV
False Positive (FP) True Positive (TP) 30.4% (28.8%, 32.1%)
Predicted
- 10,848 170 NPV
True Negative (TN) False Negative (FN) 98.5% (98.2%, 98.7%)
- +
Truth
Subgroup Analyses
Subgroup assessments of diagnostic performance were conducted to determine if there was heterogeneity in device performance across clinical sites, demographics, clinical characteristics, conduction disorders, ECG manufacturer, and ECG devices. To assess for heterogeneity, Breslow-Day tests for the strata were conducted on the odds ratio for condition presence given a positive ECG-AI result. The results are summarized in Table 2 below.
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Table 2: Subgroup Analyses
| Subgroup Analysis | Result of Test for Heterogeneity |
|---|---|
| Clinical Site | Not statistically significant |
| Biological Sex | Not statistically significant |
| Race/Ethnicity | Not statistically significant |
| Age Group | Diagnostic performance varied across age strata (p 17% for the three conditions) and the algorithm had higher sensitivity and lower specificity. This combination, at the higher prevalence values, resulted in robust positive predictive value (>35%) and negative predictive value (>94%). |
| Conduction Disorders | Not statistically significant |
| ECG Manufacturer | Not statistically significant |
| ECG Device | Not statistically significant |
6 Summary of Non-Clinical Performance Data
The performance characteristics for the Low Ejection Fraction AI-ECG Algorithm have been evaluated with the following non-clinical testing: software verification and validation (per IEC 62304), cybersecurity, labeling validation, and human factors.
Substantial Equivalence Conclusion
The subject device, Anumana Low Ejection Fraction AI-ECG Algorithm, is substantially equivalent to the predicate device Viz HCM (DEN230003). The devices have similar intended uses, principles of operation, and technical characteristics. Where differences occur between the subject device and the predicate, results of clinical performance and results of non-clinical verification and validation demonstrate that the subject device is as safe and as effective as the predicate.
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Table 3: Substantial Equivalence Comparison of Subject Device to the Predicate
| Subject Device | Predicate Device | Comparison | |
|---|---|---|---|
| Product Name | Anumana Low Ejection Fraction AI-ECG Algorithm | Viz HCM | |
| Application No. | K232699 | DEN230003 | - |
| Product Codes | QXX | ||
| DQK | QXO | - | |
| Regulation No. | 21 CFR 870.2380 | 21 CFR 870.2380 | |
| 21 CFR 870.1425 | - | ||
| Regulation Name | Cardiovascular machine learning-based notification software | Cardiovascular machine learning-based notification software | - |
| Rx / OTC | Rx | Rx | Same |
| Intended Use/ Indications for Use | 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 AI-ECG Algorithm is not intended to be a stand-alone diagnostic device for | Viz HCM, a standalone electrocardiogram (ECG) analysis software to identify patients 18 or older for further follow-up for hypertrophic cardiomyopathy (HCM), a rare condition in which the heart muscle becomes abnormally thick and makes it difficult for the heart to pump blood. This device can analyze recordings from compatible 12-lead ECGs, detect signs associated with HCM, and allow the user to view the ECG and software analysis. The device does not provide a diagnosis of HCM and is not intended for use on patients with implanted pacemakers | Similar – The subject device is intended to aid in screening for Left Ventricular Ejection Fraction (LVEF) less than or equal to 40% in adults at risk for heart failure, whereas the predicate device is an analysis software used to identify patients for further follow-up for hypertrophic cardiomyopathy (HCM). |
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| Subject Device | Predicate Device | Comparison | |
|---|---|---|---|
| Product Name | Anumana Low Ejection Fraction AI-ECG Algorithm | Viz HCM | |
| 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. | | |
| Operational Mode | Spot check | Spot check | Same |
| Motion | None | Hemosphere Advanced Monitoring Platform and ClearSight Module (K201446)
WorkMate Claris System (K210392) | Similar – The subject device is a SaMD that does not need to take into account motion correction. |
| Patient Population | Adults | Adults | Same |
| Environment of Use | Primary care, urgent care, and emergency care settings | Hospital and Pre-Hospital Setting | Similar – Both devices are to be used in professional healthcare settings |
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| Subject Device | Predicate Device | Comparison | |
|---|---|---|---|
| Product Name | Anumana Low Ejection Fraction AI-ECG Algorithm | Viz HCM | |
| Algorithm | Machine learning based algorithm with a predetermined change control plan (PCCP) | Machine learning based algorithm | Similar – The subject device submission proactively pre-specifies intended modifications (and their method of implementation) that do not require additional marketing submission. |
| Algorithm Calculation and Output | Detection of LVEF (Left Ventricular Ejection Fraction less than or equal to 40%) from an ECG signal | Presence of signs associated with HCM derived from an ECG signal | Similar – Both devices identify a single cardiovascular condition |
| Ground Truth for Model Training | Transthoracic echocardiogram (TTE) with disease | Cardiologist adjudicated signs associated with a disease using an ECG waveform | Similar – Both devices rely upon established clinical diagnostic methods as ground truth |
| Physiological Parameter Inputs | 12-Lead ECG waveform in digital format | Parameters gathered from the Hemosphere Advanced Monitoring Platform and ClearSight Module (K201446) and WorkMate Claris System (K210392). Includes oximetry, heart rate, and additional cardiac parameters (e.g., blood pressure) using an EKG. | Similar – Both devices utilize ECG digital waveforms as input |
| Data Displayed | Algorithm output is provided to third party software that displays the result to clinicians. Output provided for each ECG is "Low LVEF Detected" "Low LVEF Not Detected" or "Error"). | Device flags suspected ECGs and categorizes patients as requiring further follow up. | Similar – Both devices provide data suggesting the likelihood of a cardiovascular disease or condition for further referral or diagnostic follow-up. |
| Hardware | Compatible 12-Lead diagnostic ECG machines with 500Hz digital output | Hemosphere Advanced Monitoring Platform and ClearSight Module (K201446) and WorkMate Claris System (K210392) | Similar – Both devices are compatible with devices providing digital ECG outputs |
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| Subject Device | Predicate Device | Comparison | |
|---|---|---|---|
| Product Name | Anumana Low Ejection Fraction AI-ECG Algorithm | Viz HCM | |
| Software | Anumana proprietary algorithm and application | Viz.AI proprietary algorithm and application | Similar – Both devices incorporate their own proprietary algorithms and applications. |
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