K Number

Device Name

EchoGo Pro

Manufacturer

Ultromics Ltd

Date Cleared

2020-12-18

(191 days)

Product Code

POK

Regulation Number

892.2060

Intended Use

EchoGo Pro v1.0.2 is a machine learning-based decision support system, indicated as an adjunct to diagnotic 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 v 1.0.2 analysis. EchoGo Pro v 1.0.2 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.

Device Description

EchoGo Pro v1.0.2 is a standalone software application that utilizes anonymized DICOM 3.0 compliant stress echo (SE) datasets to provide a categorical assessment as to whether the data are suggestive of a higher or lower possibility of significant CAD. The software automatically registers images, and segments and analyses selected regions of interest (ROI). EchoGo Pro v1.0.2 utilizes standard clinical SE protocols that provide apical 2 chamber and parasternal short axis (SAX) views. Ultrasound images are acquired from a third-party acquisition device. The incoming DICOM study is checked for consistency and completeness, i.e. whether all required views labels are present in metadata. Once the technical QC has been performed on the DICOM datasets, the algorithm for automated contour detection of the endocardium of the LV is applied and presented for review and approval by trained Operators. An auto-contouring algorithm places a trace around the LV that sufficiently captures the LV shape. Outlining is detected for all frames in between and including end-systole (ES) and end-diastole (ED) for AP2, AP4, and mid-ventricular short axis (SAX) views of the LV at both rest and peak stress. Trained operators review and approve of the contour traces. The approved contour traces are used in calculations for geometric parameters. Geometric parameters are calculated from the approved contours and are fed into a fixed classification model that has been previously trained on datasets with known outcomes. The output of the pre-trained model generates a report which contains a categorical assessment as to whether the data are consistent with significant CAD or not. Significant CAD as determined by EchoGo Pro, based on LV segmentation (ROI) analysis, and was defined as ≥70% stenosis in the proximal to mid LAD, proximal left circumflex or proximal to mid RCA as measured by invasive angiography performed within 6 months of stress echocardiogram.

More Information

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Type

Center

Panel

Date Received

Product Code

Subsequent Product Codes

Applicant Name (Manufacturer)

Device Name

Decision

Street 1

Street 2

City

State

Country Code

ZIP

Postal Code

Class Advise Committee

SSP Indicator

Third Party Reviewed

Expedited Review

Predicate Devices

K190442

Reference Devices

K190442

AI/ML (Artificial Intelligence / Machine Learning)

Yes
The intended use and device description explicitly state that the device is a "machine learning-based decision support system" and utilizes a "fixed classification model that has been previously trained".

Therapeutic

No
The device is a machine learning-based decision support system intended as an adjunct to diagnostic stress echocardiography, providing information useful for diagnosis, not for treatment or therapy.

Diagnostic

Yes

Explanation: The "Intended Use / Indications for Use" section explicitly states that the device is "indicated as an adjunct to diagnostic stress echocardiography" and "provides information that may be useful in rendering an accurate diagnosis." These phrases directly align with the function of a diagnostic device.

SaMD (Software as a Medical Device)

Yes

The device description explicitly states "EchoGo Pro v1.0.2 is a standalone software application". While it processes data from a third-party acquisition device (ultrasound), the device itself is the software.

IVD (In Vitro Diagnostic)

No, this device is not an IVD (In Vitro Diagnostic).

Here's why:

IVD Definition: In vitro diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections.
EchoGo Pro's Function: EchoGo Pro analyzes ultrasound images of the heart (specifically the Left Ventricle) acquired from a third-party device. It does not analyze biological samples taken from the patient.
Intended Use: The intended use is as an adjunct to diagnostic stress echocardiography, which is an imaging procedure, not an in vitro test.

Therefore, based on the provided information, EchoGo Pro v1.0.2 is not an In Vitro Diagnostic device.

PCCP (Predetermined Change Control Plan) Authorized

No
The letter does not state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.

Overview

Intended Use / Indications for Use

EchoGo Pro v1.0.2 is a machine learning-based decision support system, indicated as an adjunct to diagnotic 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 v 1.0.2 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 graff (CABG) surgery

Product codes

POK

Device Description

Ultrasound images are acquired from a third-party acquisition device. The incoming DICOM study is checked for consistency and completeness, i.e. whether all required views labels are present in metadata. Once the technical QC has been performed on the DICOM datasets, the algorithm for automated contour detection of the endocardium of the LV is applied and presented for review and approval by trained Operators. An auto-contouring algorithm places a trace around the LV that sufficiently captures the LV shape. Outlining is detected for all frames in between and including end-systole (ES) and end-diastole (ED) for AP2, AP4, and mid-ventricular short axis (SAX) views of the LV at both rest and peak stress. Trained operators review and approve of the contour traces. The approved contour traces are used in calculations for geometric parameters.

Geometric parameters are calculated from the approved contours and are fed into a fixed classification model that has been previously trained on datasets with known outcomes. The output of the pre-trained model generates a report which contains a categorical assessment as to whether the data are consistent with significant CAD or not. Significant CAD as determined by EchoGo Pro, based on LV segmentation (ROI) analysis, and was defined as ≥70% stenosis in the proximal to mid LAD, proximal left circumflex or proximal to mid RCA as measured by invasive angiography performed within 6 months of stress echocardiogram.

Mentions image processing

Yes

Mentions AI, DNN, or ML

Yes

Input Imaging Modality

Ultrasound modality

Anatomical Site

LV (Left Ventricle)

Indicated Patient Age Range

Not Found

Intended User / Care Setting

interpreting physician

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

Not Found

Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)

Performance / Clinical Testing:
The difference between the diagnostic performance of readers when interpreting ultrasound studies with and without the assistance of EchoGo Pro v1.0.2 is equivalent or better than that of the predicate device.
The difference between inter-operator agreement when interpreting ultrasound studies with and without the assistance of EchoGo Pro v1.0.2. is equivalent or better than that of the predicate device.

ROC curves were generated and analyzed. All AUROC curves were computed via the trapezoidal approximation. Based on the standard error measurements, the error was propagated to estimate the mean performance interface and 95% confidence interval. This was found to be 0.054 (95%CI 0.032, 0.077) at p = 0.02, satisfying the acceptance criteria for the primary endpoint. The increase in reader performance with EchoGo Pro v 1.0.2 exceeded the mean reader improvement in the AUROC reported for Koios DS in K 190442, which was 0.034. The system achieved a native system performance of 0.927 AUROC, with specificity of 0.927 (95% CI 0.878, 0.976) and a sensitivity of 0.844 (95% CI 0.739, 0.950), greater than the native system performance reported for the predicate device (K190442) of 0.882.

To characterize the effect of the EchoGo Pro v1.0.2 (USE + EGP) system on inter-clinician variability, the Kendall Tau-B correlation coefficient was computed in a pairwise manner for all readers. The metric was > 0 for all reader pairs. The standard error for USE + EGP and USE Alone was computed to assess if the shifts in the metric were significant. The average Kendall Tau-B of USE Alone was 0.58 (0.48, 0.69) and the average Kendall Tau-B of USE + EGP was 0.82 (0.72, 0.93) with 95% CI demonstrating a significant increase in the metric (p

Regulation Number and Section

§ 892.2060 Radiological computer-assisted diagnostic software for lesions suspicious of cancer.

(a)
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.(b)
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).

Summary

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December 18, 2020

Ultromics Ltd. % Mr. Neela Hassanali Head of Regulatory 4630 Kingsgate, Cascade Way, Oxford Business Park South Oxford. Oxfordshire OX4 2SU UNITED KINGDOM

Re: K201555 Trade/Device Name: EchoGo Pro 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: November 8, 2020 Received: November 10, 2020

Dear Mr. Hassanali:

We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal 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 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (OS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about mediation-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,

Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

Indications for Use

510(k) Number (if known) K201555

Device Name EchoGo Pro

Indications for Use (Describe)

Limitations: EchoGo Pro v1.0.2 has not been validated on patients who underwent previous coronary artery bypass graff (CABG) surgery

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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6. 510(K) SUMMARY

The Company's 510(k) Summary is provided on the following pages.

Image /page/4/Picture/0 description: The image shows the logo for ULTROMICS. The logo consists of a circular graphic above the company name. The graphic is a ring made up of blue and green hues, with the colors blending together to create a gradient effect. The company name, "ULTROMICS," is written in bold, sans-serif font below the graphic.

510(k) SUMMARY

1. Submitter

| Company & Address: | Ultromics Ltd
4630 Kingsgate
Cascade Way
Oxford Business Park South
Oxford, OX4 2SU
203-906-4644 |
|--------------------|-----------------------------------------------------------------------------------------------------------------|
| Contact Person: | Neelam Hassanali |
| Contact Email: | neelam.hasssanali@ultromics.com |
| Date Prepared: | June 8, 2020 |
| Date Updated: | December 16, 2020 |

2. Device

Name of Device:	EchoGo Pro
Common or Usual Name:	Computer-Assisted Diagnostic Software (CADx)
Classification Name:	Computer-Assisted Diagnostic Software (CADx) for lesions
suspicious for cancer
Regulation:	21 CFR §892.2060
Regulatory Class:	II
Product Code:	POK

3. Predicate Device

Koios Medical, Inc. Koios DS For Breast device (K190442)

The predicate device has not been subject to a design-related recall.

4. Device Description

Ultrasound images are acquired from a third-party acquisition device. The incoming DICOM study is checked for consistency and completeness, i.e. whether all required views labels are present in

Image /page/5/Picture/0 description: The image shows the logo for ULTROMICS. The logo consists of a circular graphic with a gradient of blue and green, positioned above the company name, which is written in bold, sans-serif font. The color scheme suggests a modern and technological feel.

metadata. Once the technical QC has been performed on the DICOM datasets, the algorithm for automated contour detection of the endocardium of the LV is applied and presented for review and approval by trained Operators. An auto-contouring algorithm places a trace around the LV that sufficiently captures the LV shape. Outlining is detected for all frames in between and including end-systole (ES) and end-diastole (ED) for AP2, AP4, and mid-ventricular short axis (SAX) views of the LV at both rest and peak stress. Trained operators review and approve of the contour traces. The approved contour traces are used in calculations for geometric parameters.

5. Indications for Use

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 arterv 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 vl.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."

6. Comparison of Intended Use

The intended use of the EchoGo Pro and the predicate, Koios DS For Breast device (K190442), is to act as a diagnostic aid using machine learning to assist trained interpreting physicians in analyzing ultrasound images and characterize ultrasound image features for given regions of interest (ROIs) to generate a categorical output. Both devices:

are used as an aid to diagnosis (not for primary diagnosis). .
are intended for patients being referred for further diagnostic ultrasound examination. ●

Image /page/6/Picture/0 description: The image shows the logo for Ultromics. The logo consists of a circular design with a gradient of blue and green colors, resembling a swirling or rotating shape. Below the circular design, the word "ULTROMICS" is written in bold, dark blue letters. The logo appears clean and modern.

requires a user to select multiple ROIs, from multiple orthogonal views, that represent a single anatomical feature to be processed.
are intended to help trained interpreting physicians improve their overall accuracy as well . as reduce inter-operator variability.
. intended use population includes patients presenting with disease through any form of imaging or physical examination resulting in a referral for diagnostic ultrasound - thus covering diagnostic imaging workup or the evaluation of known disease.

The indications for use for the EchoGo Pro and Koios DS for Breast differ in that the EchoGo Pro is indicated for patients undergoing assessment for coronary artery disease, whereas the Koios DS for Breast is indicated for patients with soft tissue breast lesions examination. As such, the subject device provides a categorical output related to coronary artery disease whereas the predicate device provides a categorical output that aligns to BI-RADS and auto-classified shape and orientation.

The difference in Indications for Use statement does not affect the safety and effectiveness of the device when used as labeled. Both the subject and predicate devices have the same intended use for characterizing image features for given ROIs to generate categorical output that is used as an aid to diagnosis.

7. Comparison of Technological Characteristics with the Predicate Device

The technical principle of both the EchoGo Pro and the predicate is characterization of ultrasound image features with ROIs to generate a categorical output. The results of the output are used as an aid to diagnosis. Both devices provide computer analytics based on morphological and enhancement characteristics that the predicate device refers to as imaging features that are then synthesized by an artificial intelligence (machine learning) algorithm into a categorical output. At a high level, the subject and predicate devices are based on the following same technological elements:

Generation of a categorical output that analyzes imaging features relative to a dataset of . known ground truth
. Software as a medical device (SaMD)
Use of artificial intelligence / Machine Learning-Based Algorithm ●
Characterization of image features with ROIs to generate categorical output ●
Ultrasound modality for analysis .
Input is images provided in a DICOM format ●
Images can be viewed .
Selection of multiple views for processing ●

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for ROIs to be selected in multiple views for the assessment of one anatomical feature
Assess views that are orthogonal .
Operates on a Windows platform ●
. Operates on off the shelf hardware
Output is a report which is returned to the physician for review
. Output of the system is to be used as a diagnostic aid

The following technological differences exist between the subject and predicate devices:

ROI are selected automatically by the subject device and by the user in the predicate device. .
The output of the subject pre-trained model gives a categorical assessment as to whether . the data are suggestive of a higher or lower possibility of significant coronary artery disease or not, whereas the predicate device uses a pre-trained model to provide an indication of the probability as to whether the data are indicative of cancer or not.
While both devices generate a categorical output that analyzes imaging features relative to . a dataset of known ground truth, the predicate device generates a four-level and the subject device generates a two-level output. The predicate device provides four categorical outputs: Benign, Probably Benign, Suspicious, Probably Malignant vs. two for the subject device: Stress Echocardiogram ROIs are suggestive of a higher possibility of significant CAD, Stress Echocardiogram ROIs are suggestive of a lower possibility of significant CAD. The predicate device also provides output related to shape, orientation, and confidence level. The subject device does not provide this information on the report; however, the user can see shape and orientation of the image while using the device.

8. Performance Data

Software Verification and Validation Testing

Software verification and validation testing were conducted, and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." The software for this device is considered as a "moderate" level of concern, since a failure or latent design flaw could indirectly result in minor injury to the patient or operator through incorrect or delayed information or through the action of a care provider.

Performance / Clinical Testing

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performance testing was to assess the clinical indication and substantial equivalence of EchoGo Pro by demonstrating:

The difference between the diagnostic performance of readers when interpreting ultrasound studies with and without the assistance of EchoGo Pro v1.0.2 is equivalent or better than that of the predicate device.
. The difference between inter-operator agreement when interpreting ultrasound studies with and without the assistance of EchoGo Pro v1.0.2. is equivalent or better than that of the predicate device.

Per the primary endpoint of the study, ROC curves were generated and analyzed. All AUROC curves were computed via the trapezoidal approximation. Based on the standard error measurements, the error was propagated to estimate the mean performance interface and 95% confidence interval. This was found to be 0.054 (95%CI 0.032, 0.077) at p = 0.02, satisfying the acceptance criteria for the primary endpoint. The increase in reader performance with EchoGo Pro v 1.0.2 exceeded the mean reader improvement in the AUROC reported for Koios DS in K 190442, which was 0.034. The system achieved a native system performance of 0.927 AUROC, with specificity of 0.927 (95% CI 0.878, 0.976) and a sensitivity of 0.844 (95% CI 0.739, 0.950), greater than the native system performance reported for the predicate device (K190442) of 0.882.