Us2.v2 software is used to process acquired transthoracic cardiac ultrasound images, to analyze and make measurements on images in order to provide automated estimation of several cardiac structural parameters, including left right atrial and ventricular linear dimensions, volumes, systolic function, measured by B mode, M mode and Doppler (PW, CW, tissue) modalities. The data produced by this software is intended to be used to support qualified cardiologists, sonographers, or other licensed professional healthcare practitioners for clinical decision-making. Us2.v2 is indicated for use in adult patients.

Device Description

Us2.v2 is an image post-processing analysis software device used for viewing and quantifying cardiovascular ultrasound images in DICOM format. The device is intended to aid diagnostic review and analysis of echocardiographic data, patient record management and reporting. The primary intended function of Us2.v2 is to automatically provide clinically relevant and reproducible quantitative echocardiographic measurements, while reducing echocardiographic analysis time. In doing so, the primary benefit of Us2.v2 is to improve clinical echocardiographic workflow, enabling clinicians to generate and edit reports faster. with precision and with full control. Because Us2.v2 measurements cover the minimum echocardiographic dataset f or a standard adult echocardiogram (by European Society of Cardiovascular Imaging, British Society of Echocardiography and American Society of Echocardiography guidelines), our software is applicable to the vast majority of adult transthoracic echocardiograms. Our current sof tware aims to automate measurements of cardiac dimensions and lef t ventricular function and are applicable regardless of normal or disease states. We specifically indicate that our current product will not be reporting measurements associated with intra-cardiac lesions ( e.g. tumours, thrombi), nor complex adult congenital heart disease. The sof tware provides automated markup and analysis to generate a f ull report, on which a qualif ied sonographer/ reviewing physician could perf orm edits/ revise the markup on the echocardiographic image measurement during their approval process. The markup includes: the cardiac segments captured. measurements of distance, time, area and blood flow. quantitative analysis of cardiac function, and a summary report. The software allows the sonographer to enter their markup manually. It also provides automated markup and analysis, which the sonographer may choose to accept outright, to accept partially and modify, or to reject and ignore. Machine learning based view classification and border detection form the basis for this automated analysis. Additionally, the software has features for organizing, displaying and comparing to reference guidelines the quantitative data from cardiovascular images acquired from ultrasound scanners.

AI/ML Overview

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria provided for Left Ventricular Strain are based on Root Mean Square Error (RMSE). For other measurements, Intraclass Correlation Coefficient (ICC) is used, with the column indicating the lower bound of the 95% confidence interval for ICC, and the ICC itself.

Measurement Category	Performance Metric	Acceptance Criteria	Reported Device Performance
Left Ventricular Strain
Global Longitudinal Strain	RMSE	Not explicitly stated as a numerical threshold, but implies "against reference values generated using the comparator device."	2.6 - 4.12
Regional Longitudinal Strain	RMSE	Not explicitly stated as a numerical threshold, but implies "against reference values generated using the comparator device."	4.84 - 9.54
Other Us2.v2 Measurements
LVOT Diameter (mm)	ICC lower 95% CI	Not explicitly stated as a numerical threshold.	0.77
LVOT Diameter (mm)	ICC	Not explicitly stated as a numerical threshold.	0.78
RV a' (cm/s)	ICC lower 95% CI	Not explicitly stated as a numerical threshold.	0.84
RV a' (cm/s)	ICC	Not explicitly stated as a numerical threshold.	0.85
RV e' (cm/s)	ICC lower 95% CI	Not explicitly stated as a numerical threshold.	0.85
RV e' (cm/s)	ICC	Not explicitly stated as a numerical threshold.	0.86
RV s' (cm/s)	ICC lower 95% CI	Not explicitly stated as a numerical threshold.	0.89
RV s' (cm/s)	ICC	Not explicitly stated as a numerical threshold.	0.90
TAPSE (mm)	ICC lower 95% CI	Not explicitly stated as a numerical threshold.	0.72
TAPSE (mm)	ICC	Not explicitly stated as a numerical threshold.	0.74
AoV Pmax (mmHg)	ICC lower 95% CI	Not explicitly stated as a numerical threshold.	0.95
AoV Pmax (mmHg)	ICC	Not explicitly stated as a numerical threshold.	0.96
AoV Pmean (mmHg)	ICC lower 95% CI	Not explicitly stated as a numerical threshold.	0.97
AoV Pmean (mmHg)	ICC	Not explicitly stated as a numerical threshold.	0.98
AoV Vmax (m/s)	ICC lower 95% CI	Not explicitly stated as a numerical threshold.	0.98
AoV Vmax (m/s)	ICC	Not explicitly stated as a numerical threshold.	0.98
AoV VTI (cm)	ICC lower 95% CI	Not explicitly stated as a numerical threshold.	0.96
AoV VTI (cm)	ICC	Not explicitly stated as a numerical threshold.	0.97
AVA (cm^2)	ICC lower 95% CI	Not explicitly stated as a numerical threshold.	0.78
AVA (cm^2)	ICC	Not explicitly stated as a numerical threshold.	0.82
LVOT Pmax (mmHg)	ICC lower 95% CI	Not explicitly stated as a numerical threshold.	0.88
LVOT Pmax (mmHg)	ICC	Not explicitly stated as a numerical threshold.	0.90
LVOT Pmean (mmHg)	ICC lower 95% CI	Not explicitly stated as a numerical threshold.	0.90
LVOT Pmean (mmHg)	ICC	Not explicitly stated as a numerical threshold.	0.91
LVOT Vmax (m/s)	ICC lower 95% CI	Not explicitly stated as a numerical threshold.	0.91
LVOT Vmax (m/s)	ICC	Not explicitly stated as a numerical threshold.	0.92
LVOT VTI (cm)	ICC lower 95% CI	Not explicitly stated as a numerical threshold.	0.89
LVOT VTI (cm)	ICC	Not explicitly stated as a numerical threshold.	0.91
VR	ICC lower 95% CI	Not explicitly stated as a numerical threshold.	0.93
VR	ICC	Not explicitly stated as a numerical threshold.	0.94
Sinotub Junction (mm)	ICC lower 95% CI	Not explicitly stated as a numerical threshold.	0.74
Sinotub Junction (mm)	ICC	Not explicitly stated as a numerical threshold.	0.78
Sinus Valsalva (mm)	ICC lower 95% CI	Not explicitly stated as a numerical threshold.	0.78
Sinus Valsalva (mm)	ICC	Not explicitly stated as a numerical threshold.	0.82

Note: The document states that "Acceptance criteria were based on Root Mean Square Error against reference values generated using the comparator device" for Left Ventricular Strain, and for other measurements, ICC is used. However, specific numerical thresholds for these criteria are not explicitly stated in the provided text. The tables only show the reported performance values.

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

Test Set Sample Sizes:
- Dataset 1: n = 3029
- Dataset 2: n = 260
- Dataset 3: n = 192
Data Provenance: The document states "US-based cohorts used in Us2.v2 testing." It also specifies that "Test datasets are strictly segregated from algorithm training datasets, as they are from completely separate cohorts." The study is described as a "bench study to validate its performance in real-world conditions" using "the same patient data and the same images" as manual analysis, implying retrospective data from clinical settings. It doesn't explicitly state if it was prospective or retrospective, but the phrasing "same patient data and the same images" for comparison with manual analysis strongly suggests retrospective use of existing data.

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

The document states that the performance of Us2.v2 measurements was compared "against manual analysis (of the same patient data and the same images) generated by trained echocardiography technicians or cardiologists, both in 'gold standard' reference echo core labs and 'real world' clinical settings."
It does not specify the exact number of experts (technicians or cardiologists) used, nor their specific qualifications (e.g., years of experience).

4. Adjudication Method for the Test Set

The document does not describe a specific adjudication method (e.g., 2+1, 3+1). It states that the "manual analysis...generated by trained echocardiography technicians or cardiologists" was the reference. It doesn't mention how discrepancies among multiple human readers (if any were used per case) were resolved.

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 explicitly described. The study compared the device's automated measurements against a "manual analysis" reference, which was performed by "trained echocardiography technicians or cardiologists." There is no mention of human readers improving with AI vs. without AI assistance. The study focuses on the performance of the algorithm compared to human-generated measurements.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, the described study appears to be a standalone performance evaluation. The device's automated analysis is compared directly against manual measurements, demonstrating the algorithm's performance without explicitly including a human-in-the-loop workflow. The description "The automated analysis generated by Us2.v2 will be compared head-to-head against manual analysis" supports this.

7. The Type of Ground Truth Used

The ground truth used was expert consensus / manual analysis. Specifically, it was established by "trained echocardiography technicians or cardiologists, both in 'gold standard' reference echo core labs and 'real world' clinical settings."

8. The Sample Size for the Training Set

The sample size for the training set is not provided in the given text. The document only states that "Test datasets are strictly segregated from algorithm training datasets, as they are from completely separate cohorts."

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

The document mentions that "Machine learning based view classification and border detection form the basis for this automated analysis" and that the test datasets are "strictly segregated from algorithm training datasets." However, it does not describe how the ground truth for the training set was established.

Summary

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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo in blue, with the words "U.S. FOOD & DRUG ADMINISTRATION" in blue as well. The FDA is a federal agency responsible for regulating and supervising the safety of food, drugs, and other products.

Eko.ai Pte. Ltd d/b/a Us2.ai Hui Qun Tay RAQA Manager 2 College Road, #02-00 Singapore

Re: K233676

April 1, 2024

Trade/Device Name: Us2.v2 Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: February 29, 2024 Received: February 29, 2024

Dear Hui Qun Tay:

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.

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).

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Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 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 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-reportingcombination-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 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.

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-device-safety/medical-device-reportingmdr-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/medicaldevices/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-device-advice-comprehensive-regulatoryassistance/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.

Jessica Lamb

Jessica Lamb, Ph.D. Assistant Director Imaging Software Team DHT 8B: Division of Radiological Imaging Devices and Electronic Products OHT 8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

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Indications for Use

510(k) Number (if known) K233676

Device Name Us2.v2

Indications for Use (Describe)

Please note the following limitations:

Poor image capture will lead to poor annotations and subsequent measurements. Multiple image quality algorithms are used to filter out images of poor quality.
Our software complements good patient care and does not exempt the responsibility to provide supervision, clinically review the patient, and make appropriate clinical decisions.
If no gender is present, female referenced guideline values will be used for conclusions.
If Body Surface Area (BSA) is not present, indexed values cannot be provided.
During image acquisition, inappropriate use of the echo machine, use of non-cardiac ultrasound probes, use of suboptimal settings (e.g. gain, contrast, depth), or lack of electrocardiogram capture may lead to lower accuracy of the software.

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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5 510(k) Summary

510(k) - Us2.v2

Table 5-1. Subject Device Overview.
Submitter's Name:	Eko.ai Pte. Ltd. (d/b/a Us2.ai)
Address:	2 College Road, #02-00, Singapore 169850
Contact Person:	Hui Qun, Tay
Title:	Regulatory Affairs and Quality Assurance Manager
Telephone Number:	+65 83994056
Fax Number:	+65 83994056
Email:	huiqun@us2.ai
Date Summary Prepared:	Nov 9, 2023
Device Proprietary Name:	Us2.v2
Model Number:	V 2.0.0
Common Name:	Us2.v2
Classification Number:	21 CFR 892.2050
Classification Name:	Automated Radiological Image Processing Software
Product Code:	QIH
Device Class:	Class II
Predicate Device	Trade name: Us2.v1Manufacturer: Eko.ai Pte Ltd2 College Road, #02-00Singapore 169850Regulation Number: 21 CFR 892.2050Regulation Name: System, Image Processing, RadiologicalDevice Class: Class IIProduct Code: QIH510(k) Number: K210791510(k) Clearance Date: Jul 27, 2021

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5.1 Device Description

The primary intended function of Us2.v2 is to automatically provide clinically relevant and reproducible quantitative echocardiographic measurements, while reducing echocardiographic analysis time. In doing so, the primary benefit of Us2.v2 is to improve clinical echocardiographic workflow, enabling clinicians to generate and edit reports faster. with precision and with full control.

Because Us2.v2 measurements cover the minimum echocardiographic dataset f or a standard adult echocardiogram (by European Society of Cardiovascular Imaging, British Society of Echocardiography and American Society of Echocardiography guidelines), our software is applicable to the vast majority of adult transthoracic echocardiograms.

Our current sof tware aims to automate measurements of cardiac dimensions and lef t ventricular function and are applicable regardless of normal or disease states. We specifically indicate that our current product will not be reporting measurements associated with intra-cardiac lesions ( e.g. tumours, thrombi), nor complex adult congenital heart disease.

The sof tware provides automated markup and analysis to generate a f ull report, on which a qualif ied sonographer/ reviewing physician could perf orm edits/ revise the markup on the echocardiographic image measurement during their approval process. The markup includes: the cardiac segments captured. measurements of distance, time, area and blood flow. quantitative analysis of cardiac function, and a summary report.

The software allows the sonographer to enter their markup manually. It also provides automated markup and analysis, which the sonographer may choose to accept outright, to accept partially and modify, or to reject and ignore. Machine learning based view classification and border detection form the basis for this automated analysis. Additionally, the software has features for organizing, displaying and comparing to reference guidelines the quantitative data from cardiovascular images acquired from ultrasound scanners.

5.2 Indications for Use

Us2.v2 software is used to process acquired transthoracic cardiac ultrasound images, to analyze and make measurements on images in order to provide automated estimation of several cardiac structural and functional parameters, including left/ right atrial and ventricular linear dimensions, volumes, systolic function and diastolic function, measured by B mode. M mode and Doppler (PW. CW. tissue) modalities. The data produced by this software is intended to be used to support qualified cardiologists, sonographers, or other licensed professional healthcare practitioners for clinical decision-making. Us2.v2 is indicated for use in adult patients.

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Please note the following limitations:

· Poor image capture will lead to poor annotations and subsequent measurements. Multiple image quality algorithms are used to filter out images of poor quality.
Our software complements good patient care and does not exempt the user from the . responsibility to provide supervision, clinically review the patient, and make appropriate clinical decisions.
If no gender is present, female referenced guideline values will be used for conclusions. ●
If Body Surface Area (BSA) is not present, indexed values cannot be provided. .
During image acquisition, inappropriate use of the echo machine, use of non-cardiac ultrasound probes, use of suboptimal settings (e.g. gain, contrast, depth), or lack of electrocardiogram capture may lead to lower accuracy of the software.

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Summary of Technological Characteristics Comparison 5.3

Table 5-2 shows the similarities and differences between the technological characteristics of the two products. Testing demonstrates that the differences do not raise new questions of safety or effectiveness.

Topic	Predicate Device (Us2.v1)
PhysicalCharacteristics	Software package that operatesutilizing off-the-shelf hardware	Same
DICOM StandardCompliance	The software processes DICOMcompliant image data	Same
Modalities	Cardiac echocardiogram	Same
User Interface	The software is designed for use on apersonal computer that has beenreceived images from a compatible	Same
Automation level	Fully automated, including clipselection	Same
Userconfirmation/rejection of result	Yes	Yes
Manual editingof automatedresult by user	Yes (in application)	Same
Automatedcalculations	Same as the predicate plus:
	LV DecT	LV LV GLS
	LV MV-A	LV A4C LV GLS
	LV MV-Adur	LV A3C LV GLS
	LV MV-E	LV A2C LV GLS
	LV e' lateral	LV LV Regional Strain
	LV e' septal	RV TAPSE
	LV a' lateral	RV RV E'
	LV a' septal	RV RV A'
	LV s' lateral	RV RV S'
	LV s' septal	Aorta Sinotubular Junction
	LV LVEDV MOD biplane	Aorta Sinus valsalva
	LV LVEF MOD biplane	LVOT LVOT Diameter
	LV LVESV MOD biplane	LVOT PW LVOT Vmax
	LV LVSV MOD biplane	LVOT PW LVOT VTI
	LV IVsD	LVOT PW LVOT Pmax
	LV LVIDd	LVOT PW LVOT Pmean
	LV LVIDs	AoV CW AoV Vmax
	LV LVPWd	AoV CW AoV VTI
	LV E/e' mean	AoV CW AoV Pmax
	RV RVIDd	AoV CW AoV Pmean
	LA LAESV MOD biplane	AoV AVA
	RA Raa	AoV VR

Table 5-2. Summary of Technological Characteristics Comparison.

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5.4 Performance Data

Us2.v2 was developed and tested in accordance with Us2.ai's Design Control processes. The device has been subject to extensive safety and performance testing. Non-clinical verification and validation test results have confirmed that the device meets its design requirements and intended use. Specifically, software verification was conducted at unit, module, and system integration levels. Risk management analysis generated multiple risk mitigation measures and verification activities. Cybersecurity Analysis and Data Security testing were conducted to ensure that robust measures for safeguarding data and protected health information of patients are included into the software design. A Human Factors/Usability Engineering study was performed according to the principles of AAMI/ANSI HE75 to validate the device's usability within the intended user population.

Us2.v2 has chosen to perform the following bench study to validate its performance in realworld conditions. The automated analysis generated by Us2.v2 will be compared head-tohead against manual analysis (of the same patient data and the same images) generated by trained echocardiography technicians or cardiologists, both in "gold standard" reference echo core labs and "real world" clinical settings. Test datasets are strictly segregated from algorithm training datasets, as they are from completely separate cohorts. Two statistical metrics, Root Mean Square Error (RMSE) and Intraclass Correlation Coefficient (ICC) are used to evaluate the performance of the Us2.v2 measurements against expert human measurements.

5.4.1 Clinical Cohorts

The clinical characteristics of US-based cohorts used in Us2.v2 testing are outlined in the table below.

Clinical characteristic Dataset 1 (n=3029)	Dataset 2 (n=260)	Dataset 3 (n=192)
Age, years, mean ± SD	$73.7\pm8.4$	$67.5\pm9.4$	$74\pm9$
Gender, male	1425 (47.1%)	141 (54.2%)	84 (44.2%)
Ethnicity, n (%)
- African American	758 (25%)	121 (46.5%)	7 (3.7%)
- White	1207 (39.9%)	49 (18.8%)	167 (87.9%)
- Hispanic	657 (21.7%)	67 (25.8%)	0 (0%)
- Others	407 (13.4%)	20 (7.7%)	16 (8.4%)

Us2.ai software has been tested on data from 8 ultrasound device vendors, representing the manufacturers most widely used in US current clinical practice.

5.4.2 Results for Left Ventricular Strain

We used the GE EchoPac as a comparator device as it is one of the most widely used, FDAcleared Left Ventricular strain devices in the world. Clinically, measurements from any strain device are used interchangeably (Farsalinos et al., 2015, p. 1171-1181).

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Eko.ai Pte. Ltd. d/b/a Us2.ai

Acceptance criteria were based on Root Mean Square Error against reference values generated using the comparator device. Performance was tested on Dataset 1 described in 5.4.1.

Measurement	RMSE
Global Longitudinal Strain	2.6 - 4.12
Regional Longitudinal Strain	4.84 - 9.54

5.4.3 Results for Other Us2.v2 Measurements

Dataset 1
Measurement	ICC lower 95% CI	ICC
LVOT Diameter (mm)	0.77	0.78
RV a' (cm/s)	0.84	0.85
RV e' (cm/s)	0.85	0.86
RV s' (cm/s)	0.89	0.90
TAPSE (mm)	0.72	0.74
Dataset 2 + Dataset 3
Measurement	ICC lower 95% CI	ICC
AoV Pmax (mmHg)	0.95	0.96
AoV Pmean (mmHg)	0.97	0.98
AoV Vmax (m/s)	0.98	0.98
AoV VTI (cm)	0.96	0.97
AVA (cm^2)	0.78	0.82
LVOT Pmax (mmHg)	0.88	0.90
LVOT Pmean (mmHg)	0.90	0.91
LVOT Vmax (m/s)	0.91	0.92
LVOT VTI (cm)	0.89	0.91
VR	0.93	0.94
Sinotub Junction (mm)	0.74	0.78
Sinus Valsalva (mm)	0.78	0.82

5.5 Substantial Equivalence Conclusion

Us2.v2 is an image processing software which has similar intended use and indications for use statement as the predicate device. The two devices have similar technological characteristics: both use machine learning algorithms to automate the measurement of transthoracic cardiac images. Though the subject device provides more measurements than the predicate, this does not in and of itself produce different questions of safety and effectiveness. This 510(k) submission includes information on the Us2.v2 technological characteristics, as well as performance data and verification and validation activities. Despite the subject device providing more measurements than the predicate, the enclosed information demonstrates that Us2.v2 is as safe and effective as the predicate, and does not raise different questions of safety and effectiveness.

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).