(137 days)
Not Found
Yes
The device description explicitly states that "Machine learning based view classification and border detection form the basis for this automated analysis." and that "both use machine learning algorithms to automate the measurement of transthoracic cardiac images."
No
The device is described as an "image post-processing analysis software device" that aids in diagnostic review and analysis of echocardiographic data. Its primary function is to provide quantitative echocardiographic measurements and improve workflow. It does not exert any direct therapeutic action on the patient.
Yes
The device processes acquired images to analyze and make measurements for automated estimation of cardiac structural parameters. The data produced supports qualified practitioners for clinical decision-making, and the device is intended to "aid diagnostic review and analysis of echocardiographic data".
Yes
The device description explicitly states "Us2.v2 is an image post-processing analysis software device used for viewing and quantifying cardiovascular ultrasound images in DICOM format." It focuses solely on the processing and analysis of existing image data and does not mention any associated hardware components being part of the device itself.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs are used to examine specimens derived from the human body. The intended use and device description clearly state that Us2.v2 processes and analyzes transthoracic cardiac ultrasound images. These are images of the heart within the body, not specimens taken from the body.
- The analysis is performed on imaging data, not biological samples. The software analyzes measurements and parameters derived from the ultrasound images, such as dimensions, volumes, and function. This is distinct from analyzing blood, urine, tissue, or other biological samples.
Therefore, Us2.v2 falls under the category of medical image analysis software, not an In Vitro Diagnostic device.
No
The provided text does not contain any explicit statement that the FDA has reviewed, approved, or cleared a PCCP for this specific device. The entire section for "Control Plan Authorized (PCCP) and relevant text" is listed as "Not Found".
Intended Use / 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 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.
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.
Product codes (comma separated list FDA assigned to the subject device)
QIH
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.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
transthoracic cardiac ultrasound images
Anatomical Site
cardiac
Indicated Patient Age Range
adult patients
Intended User / Care Setting
qualified cardiologists, sonographers, or other licensed professional healthcare practitioners
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
The automated analysis generated by Us2.v2 will be compared head-to-head 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.
The clinical characteristics of US-based cohorts used in Us2.v2 testing are outlined in the table below.
Dataset 1 (n=3029): Age, years, mean +/- SD: 73.7 +/- 8.4; Gender, male: 1425 (47.1%); Ethnicity, n (%): African American 758 (25%), White 1207 (39.9%), Hispanic 657 (21.7%), Others 407 (13.4%).
Dataset 2 (n=260): Age, years, mean +/- SD: 67.5 +/- 9.4; Gender, male: 141 (54.2%); Ethnicity, n (%): African American 121 (46.5%), White 49 (18.8%), Hispanic 67 (25.8%), Others 20 (7.7%).
Dataset 3 (n=192): Age, years, mean +/- SD: 74 +/- 9; Gender, male: 84 (44.2%); Ethnicity, n (%): African American 7 (3.7%), White 167 (87.9%), Hispanic 0 (0%), Others 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.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Bench study to validate its performance in real-world conditions.
Sample Size: Dataset 1 (n=3029), Dataset 2 (n=260), Dataset 3 (n=192)
Key results:
Global Longitudinal Strain RMSE: 2.6 - 4.12
Regional Longitudinal Strain RMSE: 4.84 - 9.54
ICC (lower 95% CI to ICC) for Dataset 1:
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
ICC (lower 95% CI to ICC) for Dataset 2 + Dataset 3:
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
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Root Mean Square Error (RMSE), Intraclass Correlation Coefficient (ICC)
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Not Found
§ 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).
0
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).
1
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
2
Indications for Use
510(k) Number (if known) K233676
Device Name Us2.v2
Indications for Use (Describe)
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.
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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3
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.v1 |
| Manufacturer: Eko.ai Pte Ltd | |
| 2 College Road, #02-00 | |
| Singapore 169850 | |
| Regulation Number: 21 CFR 892.2050 | |
| Regulation Name: System, Image Processing, Radiological | |
| Device Class: Class II | |
| Product Code: QIH | |
| 510(k) Number: K210791 | |
| 510(k) Clearance Date: Jul 27, 2021 |
4
5.1 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.
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.
5
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.
6
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) | Subject Device (Us2.v2) | |
|---|---|---|---|
| Physical | |||
| Characteristics | Software package that operates | ||
| utilizing off-the-shelf hardware | Same | ||
| DICOM Standard | |||
| Compliance | The software processes DICOM | ||
| compliant image data | Same | ||
| Modalities | Cardiac echocardiogram | Same | |
| User Interface | The software is designed for use on a | ||
| personal computer that has been | |||
| received images from a compatible | Same | ||
| Automation level | Fully automated, including clip | ||
| selection | Same | ||
| User | |||
| confirmation/ | |||
| rejection of result | Yes | Yes | |
| Manual editing | |||
| of automated | |||
| result by user | Yes (in application) | Same | |
| Automated | |||
| calculations | 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.
7
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).
8
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.