Automated Aortic Stenosis Software (AutoAS)

K254161 · GE Medical Systems Ultrasound & Primary Care Diagnostics, LLC · POK · Mar 27, 2026 · Radiology

Device Facts

• Record ID: K254161
• Device Name: Automated Aortic Stenosis Software (AutoAS)
• Applicant: GE Medical Systems Ultrasound & Primary Care Diagnostics, LLC
• Product Code: POK · Radiology
• Decision Date: Mar 27, 2026
• Decision: SESE
• Submission Type: Traditional
• Regulation: 21 CFR 892.2060
• Device Class: Class 2
• Attributes: AI/ML, Software as a Medical Device

AI Performance

Output	Acceptance	Observed	Dev DS	Test DS
Moderate/Severe Aortic Stenosis Detection (standalone)	Statistically significantly greater than the predefined performance target.	Area Under the ROC Curve: 93.2% [95% CI: 90.5% - 95.6%]; Specificity: 92.4% [95% CI: 86.3% - 98.4%]; Sensitivity: 75.2% [95% CI: 67.4% - 83.0%].	Not provided.	Standalone Performance Assessment: 401 studies from 401 unique patients from four different U.S. institutions, including GE Healthcare and Philips Healthcare ultrasound models.
Moderate/Severe Aortic Stenosis Detection (reader improvement)	Superiority of the 'Aided' group in the critical region of the ROC curve.	Improvement in sensitivity: +5.5% [95% CI: 1.5%, 9.5%]; Partial AUROC difference: 8.9% [95% CI: 1.2%, 20.5%].	Not provided.	Clinical Performance Assessment (MRMC study): 220 unique studies across 220 unique patients from three different U.S. institutions.

Indications for Use

AutoAS is a software application intended to assist medical professionals in the assessment of moderate/severe aortic stenosis (AS). The software uses an artificial intelligence (AI) algorithm to process previously acquired two-dimensional transthoracic echocardiography (2D-TTE) images to provide a suggestion of moderate/severe aortic stenosis along with an associated confidence metric that can be a diagnostic aid to a physician in a point of care or similar setting in determining if further evaluation is needed, including whether a full echocardiogram (2D, Doppler) needs to be performed. The results of AutoAS are not intended to be used on a stand-alone basis for clinical decision making and are not intended to supplement or replace a full echocardiographic examination. AutoAS results, along with the obtained ultrasound images, must be reviewed by a qualified physician. The AutoAS product is not intended to be used on patients who have prosthetic valves and/or have had prior valve repair or replacement. AutoAS software is indicated for use in adult patients and is intended to be an accessory to compatible ultrasound systems in environments where healthcare is provided.

Device Story

Software accessory for compatible ultrasound systems; processes 2D-TTE B-mode images (PLAX, PSAX-AV, AP5 views). Employs AI algorithm to classify presence/severity of aortic stenosis (moderate/severe vs. not suggestive); provides confidence metric. Used in point-of-care or clinical settings by physicians. Operates on previously acquired or concurrent ultrasound exams; generates report for physician review. Output serves as adjunctive diagnostic aid; does not replace full echocardiographic examination or physician judgment. Benefits include identifying patients requiring further evaluation (e.g., Doppler echocardiography).

Clinical Evidence

Two studies: 1) Standalone performance (n=401): AUC 93.2% (95% CI: 90.5-95.6%), sensitivity 75.2%, specificity 92.4% against a panel of 3 level-III cardiologists. 2) MRMC study (n=220): Aided readers showed statistically significant sensitivity improvement (+5.5%) and higher inter-rater agreement (89.0% vs 81.9%) compared to unaided readers, with comparable specificity.

Technological Characteristics

Software-based; AI/ML algorithm; B-mode 2D-TTE input; compatible with GE HealthCare ultrasound systems; standalone/integrated deployment; Class II; POK product code.

Indications for Use

Indicated for adult patients undergoing 2D-TTE for assessment of moderate/severe aortic stenosis. Contraindicated for patients with prosthetic valves or prior valve repair/replacement.

Regulatory Classification

Identification

A radiological computer-assisted diagnostic software for lesions suspicious of cancer is an image processing prescription device intended to aid in the characterization of lesions as suspicious for cancer identified on acquired medical images such as magnetic resonance, mammography, radiography, or computed tomography. The device characterizes lesions based on features or information extracted from the images and provides information about the lesion(s) to the user. Diagnostic and patient management decisions are made by the clinical user.

Special Controls

A radiological computer-assisted diagnostic (CADx) software for lesions suspicious for cancer must comply with the following special controls: 1. Design verification and validation must include: i. A detailed description of the image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, and algorithm limitations. ii. A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will improve reader performance as intended. iii. Results from performance testing protocols that demonstrate that the device improves reader performance in the intended use population when used in accordance with the instructions for use. The performance assessment must be based on appropriate diagnostic accuracy measures (e.g., receiver operator characteristic plot, sensitivity, specificity, predictive value, and diagnostic likelihood ratio). The test dataset must contain sufficient numbers of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment. iv. Standalone performance testing protocols and results of the device. v. Appropriate software documentation (e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, results, and cybersecurity). 2. Labeling must include: i. A detailed description of the patient population for which the device is indicated for use. ii. A detailed description of the intended reading protocol. iii. A detailed description of the intended user and recommended user training. iv. A detailed description of the device inputs and outputs. v. A detailed description of compatible imaging hardware and imaging protocols. vi. Warnings, precautions, and limitations, including situations in which the device may fail or may not operate at its expected performance level (e.g., poor image quality or for certain subpopulations), as applicable. vii. Detailed instructions for use. viii. A detailed summary of the performance testing, including: test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders (e.g., lesion and organ characteristics, disease stages, and imaging equipment).

*Classification.* Class II (special controls). The special controls for this device are:(1) Design verification and validation must include: (i) A detailed description of the image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, and algorithm limitations. (ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will improve reader performance as intended. (iii) Results from performance testing protocols that demonstrate that the device improves reader performance in the intended use population when used in accordance with the instructions for use. The performance assessment must be based on appropriate diagnostic accuracy measures ( *e.g.,* receiver operator characteristic plot, sensitivity, specificity, predictive value, and diagnostic likelihood ratio). The test dataset must contain sufficient numbers of cases from important cohorts (*e.g.,* subsets defined by clinically relevant confounders, effect modifiers, concomitant diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Standalone performance testing protocols and results of the device. (v) Appropriate software documentation ( *e.g.,* device hazard analysis; software requirements specification document; software design specification document; traceability analysis; and description of verification and validation activities including system level test protocol, pass/fail criteria, results, and cybersecurity).(2) Labeling must include: (i) A detailed description of the patient population for which the device is indicated for use. (ii) A detailed description of the intended reading protocol. (iii) A detailed description of the intended user and recommended user training. (iv) A detailed description of the device inputs and outputs. (v) A detailed description of compatible imaging hardware and imaging protocols. (vi) Warnings, precautions, and limitations, including situations in which the device may fail or may not operate at its expected performance level ( *e.g.,* poor image quality or for certain subpopulations), as applicable.(vii) Detailed instructions for use. (viii) A detailed summary of the performance testing, including: Test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders ( *e.g.,* lesion and organ characteristics, disease stages, and imaging equipment).

Predicate Devices

• EchoGo Pro (K201555)

Submission Summary (Full Text)

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FDA U.S. FOOD & DRUG ADMINISTRATION

March 27, 2026

GE Medical Systems Ultrasound & Primary Care Diagnostics, LLC
Tahir Rizvi
Sr. Director of Regulatory Affairs
3200 N Grandview Blvd.
Waukesha, Wisconsin 53188

Re: K254161
Trade/Device Name: Automated Aortic Stenosis Software (AutoAS)
Regulation Number: 21 CFR 892.2060
Regulation Name: Radiological computer-assisted diagnostic software for lesions suspicious of cancer
Regulatory Class: Class II
Product Code: POK
Dated: February 27, 2026
Received: February 27, 2026

Dear Tahir Rizvi:

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 (the 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 available 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.

U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov

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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).

Your device is also subject to, among other requirements, the Quality Management System Regulation (QMSR) (21 CFR Part 820), which includes, but is not limited to, ISO 13485 clause 7.3 (Design controls), ISO 13484 clause 8.3 (Nonconforming product), and ISO 13485 clause 8.5 (Corrective and preventative action). Please note that regardless of whether a change requires premarket review, the QMSR requires device manufacturers to review and approve changes to device design and production (ISO 13485 clause 7.3 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).

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 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 Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 Management System Regulation (QMSR) (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.

All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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

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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 ([e-mail]) or phone [phone] or 301-796-7100).

Sincerely,


Jessica Lamb, Ph.D.
Assistant Director
Imaging Software Team
DHT8B: Division of Radiological Imaging Devices and Electronic Products
OHT8: Office of Radiological Health
Office of Product Evaluation and Quality
Center for Devices and Radiological Health

Enclosure

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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)
K254161

Device Name
Automated Aortic Stenosis Software (AutoAS)

Indications for Use (Describe)
AutoAS is a software application intended to assist medical professionals in the assessment of moderate/severe aortic stenosis (AS). The software uses an artificial intelligence (AI) algorithm to process previously acquired two-dimensional transthoracic echocardiography (2D-TTE) images to provide a suggestion of moderate/severe aortic stenosis along with an associated confidence metric that can be a diagnostic aid to a physician in a point of care or similar setting in determining if further evaluation is needed, including whether a full echocardiogram (2D, Doppler) needs to be performed.

The results of AutoAS are not intended to be used on a stand-alone basis for clinical decision making and are not intended to supplement or replace a full echocardiographic examination. AutoAS results, along with the obtained ultrasound images, must be reviewed by a qualified physician. The AutoAS product is not intended to be used on patients who have prosthetic valves and/or have had prior valve repair or replacement.

AutoAS software is indicated for use in adult patients and is intended to be an accessory to compatible ultrasound systems in environments where healthcare is provided.

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)

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# 510(K) SUMMARY

In accordance with 21 CFR 807.92 the following summary of information is provided:

| 510(k) Number | K254161 |
| --- | --- |
| Date | March 26, 2026 |
| Submitter | GE Medical Systems Ultrasound & Primary Care Diagnostics LLC
3200 N Grandview Blvd.
Waukesha, WI, 53188, United States |
| Primary Contact Person | Zahra Ghanian
Email: [e-mail]
Phone: [phone] |
| Secondary Contact Person | Tahir Rizvi, Sr. Director of Regulatory Affairs
Email: [e-mail]
Phone: [phone] |
| Device Trade Name | Automated Aortic Stenosis Software |
| Common/Usual Name | AutoAS |
| Classification Name | 892.2060 - Radiological computer-assisted diagnostic software for lesions suspicious of cancer |
| Regulatory Class | Class II |
| Product Code | POK |
| Predicate Device | EchoGo Pro (K201555)
Ultromics Ltd |

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# 1. DEVICE DESCRIPTION

Automated Aortic Stenosis Software (AutoAS) is a breakthrough¹ software product that assesses the presence and severity of aortic stenosis (AS) in B-mode cardiac ultrasound scans. The software can be integrated with a compatible ultrasound device in a headless manner. The AutoAS software is intended to be an accessory to compatible ultrasound systems. The AutoAS software is intended for use in adult patients undergoing transthoracic cardiac ultrasound examinations in whom assessment for aortic stenosis (AS) is clinically relevant. The indicated population includes patients who are being evaluated for the presence or likelihood of moderate to severe aortic stenosis as part of a routine or targeted echocardiographic study.

AutoAS processes relevant ultrasound images acquired from a concurrent and/or previously acquired ultrasound exam, employing advanced algorithms to generate AS predictions and supporting outputs for the user. The AutoAS software operates on B-mode transthoracic cardiac ultrasound images acquired during a standard ultrasound examination using a compatible GE HealthCare ultrasound system. The reading protocol is designed to ensure that AutoAS outputs are used as adjunctive information and are interpreted within the context of a comprehensive clinical and echocardiographic evaluation by a qualified physician. The AS prediction, severity, and supporting outputs are summarized as a report that is available after the exam for the user to review. The report can also be exported to an archive with the ultrasound images ensuring seamless integration with the patient's record and facilitating downstream clinical workflows.

The software's algorithms process specific views obtained during an ultrasound study. These views may include the parasternal long axis (PLAX), parasternal short axis at the aortic valve level (PSAX-AV), and apical five-chamber (AP5). The AS predictions come in the form of a severity prediction: 1) Suggestive of moderate to severe AS or 2) Not suggestive of moderate to severe AS with associated information on the confidence of the algorithm's prediction.

The AutoAS results along with ultrasound images must be reviewed by a qualified physician as the AutoAS software does not diagnose Aortic Stenosis (AS) but rather indicates the likelihood of AS. Interpretation of AutoAS results must be performed by a qualified physician with training and experience in cardiac ultrasound and echocardiographic interpretation. The results of AutoAS are not intended to be used on a stand-alone basis for clinical decision making and are not intended to supplement or replace a full echocardiographic examination. The physician must review the AutoAS outputs in conjunction with the underlying ultrasound images and relevant clinical information. The user is responsible for determining the clinical relevance of the AutoAS findings and for integrating the software outputs into the overall diagnostic impression.

# 2. INTENDED USE/INDICATIONS FOR USE

AutoAS is a software application intended to assist medical professionals in the assessment of moderate/severe aortic stenosis (AS). The software uses an artificial intelligence (AI) algorithm to process previously acquired two-dimensional transthoracic echocardiography (2D-TTE) images to provide a suggestion of moderate/severe aortic stenosis along with an associated confidence metric that can be a

¹ AutoAS received Breakthrough Device designation from the U.S. FDA

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diagnostic aid to a physician in a point of care or similar setting in determining if further evaluation is needed, including whether a full echocardiogram (2D, Doppler) needs to be performed.

The results of AutoAS are not intended to be used on a stand-alone basis for clinical decision making and are not intended to supplement or replace a full echocardiographic examination. AutoAS results, along with the obtained ultrasound images, must be reviewed by a qualified physician. The AutoAS product is not intended to be used on patients who have prosthetic valves and/or have had prior valve repair or replacement.

AutoAS software is indicated for use in adult patients and is intended to be an accessory to compatible ultrasound systems in environments where healthcare is provided.

# 3. SUMMARY OF TECHNOLOGICAL CHARACTERISTICS

Automated Aortic Stenosis Software (AutoAS) has similar intended use and similar indications, technological characteristics, and principles of operation as the previously cleared EchoGo Pro device manufactured by Ultromics Ltd. A substantial equivalence chart comparing the similarities and differences between the AutoAS, and EchoGo Pro is provided at the end of the summary document below (Table 2). The minor differences in the technological characteristics do not raise different questions of safety or efficacy. Software verification, validation and performance data demonstrates that the AutoAS is substantially equivalent to its predicate device.

# 4. NONCLINICAL TESTING

Software verification and validation testing were conducted, and documentation was provided as recommended by FDA's Guidance "Content of Premarket Submission for Device Software Functions" (issued June 14, 2023) for devices classified as "Basic" Level of Concern as defined in the Guidance. AutoAS was developed and tested in accordance with GE HealthCare's Quality Management System. Software documentation generated as part of the design process included:

- Software/Firmware Description
- Risk Management File
- Software Requirements Specifications
- System and Software Architecture
- Software Lifecycle Process Description
- Software Testing as Part of Verification and Validation
- Software Version/Revision Level History
- Unresolved Software Anomalies
- Cybersecurity

In addition to software verification and validation testing, standalone non-clinical testing of the algorithm components of AutoAS was performed. A summary is provided below:

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| Test | Purpose | Result |
| --- | --- | --- |
| Confidence Metric | AutoAS produces a confidence metric, also known simply as “confidence”, whenever it makes a prediction. With this metric, the algorithm aims to highly correlate with the true probability of a successful binary classification of the severity of aortic stenosis. | Testing demonstrated a statistically monotonically increasing relationship between the confidence value and the probability of accurately detecting whether moderate / severe aortic stenosis was present. |
| Clip Annotator | Before analysis, AutoAS evaluates each clip with a clip annotator. The function of the clip annotator is to confirm if the clip is B-mode and part of “valid” views (such as PLAX, PSAX-AV etc.) and rejects any other views. | Testing demonstrated both a positive predictive value (PPV) and Sensitivity of 100% (95% CI: (98.5%, 100.0%)) across all view types (i.e., PLAX, AP5, PSAX-AV, and all other views) when classifying the B-mode image. For any image that was classified as B-mode, the ability to accurately classify the view was also tested, and the verification test results revealed a PPV of at least 97.1% (95% CI: (94.2%, 98.8%)) and a Sensitivity of at least 87.5% (95% CI: (83.1%, 91.2%)) across all view types. |
| Heart Rate Estimation | AutoAS has the ability to predict a patient’s heart period by looking solely at the video clip. The estimated heart rate is not reported to the user and is used only internally to the software. | The verification testing demonstrated a statistically significantly lesser MAD / MAE than the established benchmark for all views (AP5, PLAX, and PSAX). Based on these results, there were no clinically significant differences between the estimated heart rates by the software and the reference measurements. |

# 5. CLINICAL PERFORMANCE TESTING

Clinical testing to evaluate the performance of the AutoAS consisted of two studies:

- Standalone Performance Assessment, comparing the performance of the software against a panel of level III cardiologists independently reading full echocardiography studies
- Clinical Performance Assessment (i.e., multi-case multi reader performance study), in which the performance of reading clinicians was assessed with and without the help of AutoAS software

## Standalone Performance Assessment:

A validation dataset was retrospectively obtained consisting of a total 401 studies from 401 unique patients from four different U.S. institutions. The validation dataset consisted of echocardiographic studies from multiple ultrasound models from two different ultrasound manufacturers:

- GE Healthcare: “Vivid E95”, “Vivid E9”, “Vivid i”, and “Vscan Air SL”
- Philips Healthcare: “iE33”, “CX50”, “EPIQ 5C”, “EPIQ CVx”, and “EPIQ 7C

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A full read was performed to establish the reference standard, with each of the studies assessed independently by each of three (3) level-III echocardiographers for AS severity according to Aortic Valve Area (AVA) per clinical guidelines from the American Society of Echocardiography (ASE).

Each echocardiographer was blinded to the interpretation by the other two echocardiographers and blinded to the original AS interpretation result from the original study. Each echocardiographer had access to complete study data and imaging as available from the echocardiographic study. The reference standard was the majority vote of the 3 echocardiographers (also known as the statistical mode).

# Results:

The standalone assessment findings demonstrated strong overall performance; Area Under the ROC Curve, $93.2\%$ [95% CI: 90.5% - 95.6%] which is statistically significantly greater than the predefined performance target. Specificity of $92.4\%$ [95% CI: 86.3% - 98.4%] and sensitivity of $75.2\%$ [95% CI: 67.4% - 83.0%] were observed, on par with original reading cardiologists when compared to the same reference panel). Consistency was noted in the performance metrics across relevant sub-group parameters such as the age of the subject, BMI, gender, device manufacturer, and the site location used during the examination.

Table 1. Consistency of performance metrics across relevant sub-group parameters

| Area under the ROC Curve | | |
| --- | --- | --- |
| Parameter | Sample Size | Point Estimate |
| Age (Years) | | |
| < 65 | 104 | 0.964 |
| ≥ 65 | 278 | 0.908 |
| BMI | | |
| < 25 | 121 | 0.944 |
| 25 - 30 | 126 | 0.903 |
| ≥ 30 | 135 | 0.959 |
| Gender | | |
| Female | 187 | 0.947 |
| Male | 195 | 0.920 |
| Site Location | | |
| Site: Group #1 | 218 | 0.922 |
| Site: Group #2 | 116 | 0.945 |
| Site: Group #3 | 48 | 0.953 |

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| Sensitivity | | |
| --- | --- | --- |
| Parameter | Sample Size | Point Estimate |
| Age (Years) | | |
| < 65 | 45 | 0.711 |
| ≥ 65 | 193 | 0.762 |
| BMI | | |
| < 25 | 86 | 0.733 |
| 25 - 30 | 73 | 0.740 |
| ≥ 30 | 79 | 0.785 |
| Gender | | |
| Female | 113 | 0.726 |
| Male | 125 | 0.776 |
| Site Location | | |
| Site: Group #1 | 140 | 0.757 |
| Site: Group #2 | 73 | 0.699 |
| Site: Group #3 | 25 | 0.880 |
| Specificity | | |
| --- | --- | --- |
| Parameter | Sample Size | Point Estimate |
| Age (Years) | | |
| < 65 | 59 | 0.983 |
| ≥ 65 | 85 | 0.882 |
| BMI | | |
| < 25 | 35 | 1.000 |
| 25 - 30 | 53 | 0.849 |
| ≥ 30 | 56 | 0.946 |
| Gender | | |
| Female | 74 | 0.973 |

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| Male | 70 | 0.871 |
| --- | --- | --- |
| Site Location | | |
| Site: Group #1 | 78 | 0.885 |
| Site: Group #2 | 43 | 1.000 |
| Site: Group #3 | 23 | 0.913 |

## Clinical Performance Assessment:

A multi-reader, multi-case (MRMC) study was conducted to assess the diagnostic performance of AutoAS, with five (5) expert echocardiography readers reviewing all studies with AutoAS (aided) and without AutoAS (unaided). A subset of validation data from the standalone performance assessment was used for the clinical performance assessment. This dataset consisted of 220 unique studies across 220 unique patients from three different U.S. institutions. A randomized crossover approach was utilized in which readers were randomly assigned to the unaided or aided arm and then were switched to the other arm after a one (1) month wash-out period.

## Results:

A statistically significant improvement in sensitivity was observed for the “Aided” readers compared to the “Unaided” readers (+ 5.5%, 95% CI: (1.5%, 9.5%)), while maintaining comparable specificity (0.897 vs. 0.900). Furthermore, when comparing the diagnostic performance of the two reader groups, the critical region of the ROC curve revealed superiority for the “Aided” group with an 8.9% [95% CI: 1.2%, 20.5%] difference in partial AUROC. In addition, aided readers demonstrated higher inter-rater agreement (89.0%) than unaided readers (81.9%), comparable to the reference standard (88.7%), reflecting improved reader consistency and diagnostic performance.

## 6. CONCLUSION

AutoAS is substantially equivalent to EchoGo Pro, cleared under K201555. Both devices share a similar intended use, indications, technological characteristics, and principles of operation. Both devices are software-based medical tools employing machine-learning algorithms to aid in the assessment of heart conditions, although they focus on different clinical applications. AutoAS targets aortic stenosis (AS), while EchoGo Pro assesses coronary artery disease (CAD). Despite minor differences in specific technological features and conditions evaluated, these distinctions do not affect its safety and effectiveness when used as labeled. Extensive performance testing has demonstrated that the AutoAS is substantially equivalent to its predicate device.

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Table 2: Substantial Equivalence Comparison Chart

| Category | Subject Device | Predicate Device | Discussion |
| --- | --- | --- | --- |
| Manufacturer | GE HealthCare | Ultromics Ltd. | N/A |
| Device | AutoAS | EchoGo Pro (K201555) | N/A |
| Intended Use/Indications for Use | AutoAS is a software application intended to assist medical professionals in the assessment of moderate/severe aortic stenosis (AS). The software uses an artificial intelligence (AI) algorithm to process previously acquired two-dimensional transthoracic echocardiography (2D-TTE) images to provide a suggestion of moderate/severe aortic stenosis along with an associated confidence metric that can be a diagnostic aid to a physician in a point of care or similar setting in determining if further evaluation is needed, including whether a full echocardiogram (2D, Doppler) needs to be performed.
The results of AutoAS are not intended to be used on a stand-alone basis for clinical decision making and are not intended to supplement or replace a full echocardiographic examination. AutoAS results, along with the obtained ultrasound images, must be reviewed by a qualified physician. | EchoGo Pro v1.0.2 is a machine learning-based decision support system, indicated as an adjunct to diagnostic stress echocardiography for patients undergoing assessment for coronary artery disease (CAD). When utilized by an interpreting physician, this device provides information that may be useful in rendering an accurate diagnosis. Patient management decisions should not be made solely on the results of the EchoGo Pro v1.0.2 analysis. EchoGo Pro v1.0.2 is to be used with stress echo exam protocols that contain A2C, A4C, and mid-ventricular short-axis views at rest and at peak stress. EchoGo Pro v1.0.2 is not intended for the assessment of mild or moderate myocardial ischemia, or localization of coronary artery disease, or for the assessment of myocardial perfusion, myocardial viability, or valve disease. Limitations: EchoGo Pro v1.0.2 has not been validated on patients who underwent previous coronary artery bypass graft (CABG) surgery. | Similar. Both devices are intended to be used as diagnostic aids for cardiac evaluation. Both devices specify in the indications for use that patient management should not be driven solely by the output of the devices. and both devices are intended to be used by the reading physician as part of patient evaluation i.e., obtained images must be formally interpreted and reported by a qualified physician. Therefore, both devices are not intended to replace the skill and judgment of a qualified medical practitioner. There is a minor difference in the specific condition that is being automatically detected between the devices, with the predicate assessing coronary artery disease (CAD) and the subject device assessing AS; however, both devices are designed to operate within the same anatomical area. |

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| Category | Subject Device | Predicate Device | Discussion |
| --- | --- | --- | --- |
| | The AutoAS product is not intended to be used on patients who have prosthetic valves and/or have had prior valve repair or replacement.

AutoAS software is indicated for use in adult patients and is intended to be an accessory to compatible ultrasound systems in environments where healthcare is provided. | | |
| Classification Name | Radiological computer-assisted diagnostic software for lesions suspicious of cancer | Radiological computer-assisted diagnostic software for lesions suspicious of cancer | Identical |
| Product Code | POK | POK | Identical |
| Regulation Number | 21 CFR 892.2060 | 21 CFR 892.2060 | Identical |
| Modality | Ultrasound (Echocardiography) | Ultrasound (Echocardiography) | Identical |
| Anatomical Site | Cardiovascular | Cardiovascular | Identical |
| Clinical Condition | Aortic Stenosis | Coronary Plaques | Similar. Despite minor difference in specific conditions evaluated, this distinction does not raise new safety or efficacy concerns as both clinical conditions are diagnostic in nature in the same anatomical site. |
| Echocardiogram Views | PLAX, PSAX-AV, AP5 | A2C, A4C, Mid-ventricle AX | While the specific image views required differ slightly between the devices, both incorporate quality-control measures to |

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| Category | Subject Device | Predicate Device | Discussion |
| --- | --- | --- | --- |
| | | | ensure sufficient input data and generate user-facing reports to support cardiac evaluations. |
| Machine-Learning Based Algorithm | Yes | Yes | Substantially equivalent. Both devices utilize deep-learning artificial intelligence as the core technology to provide diagnostic aid to the user in the assessment of heart conditions. |
| Clinical Output | Two-level (Binary) Output:
Suggestive of moderate to severe AS
Or
Not suggestive of moderate to severe AS
Confidence score generated at study-level. | Two-level (Binary) Output:
Suggestive of a lower risk of prognostically significant coronary artery disease
Or
Suggestive of a higher risk of prognostically significant coronary artery disease. | Both devices process ultrasound images to generate a two-level diagnostic output intended to assist medical professionals. The subject device includes a confidence score whereas the predicate device does not. The confidence score indicates how certain the algorithm is in its prediction, based on the clips analyzed.

This difference does not raise safety or efficacy concerns because the confidence score is only intended as supporting information but does not replace physician responsibility for diagnosis. |
| Labelled for Interpreting Physician Read | Yes | Yes | Identical. Both devices do not replace clinical judgment, emphasizing that all exams must |

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| Category | Subject Device | Predicate Device | Discussion |
| --- | --- | --- | --- |
| | | | be reviewed by an interpreting reading physician. |

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