EchoGo Amyloidosis (1.0)

K240860 · Ultromics Limited · SDJ · Nov 15, 2024 · Cardiovascular

Device Facts

• Record ID: K240860
• Device Name: EchoGo Amyloidosis (1.0)
• Applicant: Ultromics Limited
• Product Code: SDJ · Cardiovascular
• Decision Date: Nov 15, 2024
• Decision: SESE
• Submission Type: Traditional
• Regulation: 21 CFR 870.2200
• Device Class: Class 2
• Attributes: AI/ML, Software as a Medical Device

AI Performance

Output	Acceptance	Observed	Dev DS	Test DS
Cardiac Amyloidosis Status Indicator	Not explicitly stated as a threshold, but performance validated against reference ground truth.	Sensitivity: 84.5% (95% CI: 80.3%, 88.5%); Specificity: 89.7% (95% CI: 87.0%, 92.4%); PPV: 82.7% (95% CI: 78.8%, 86.5%); NPV: 90.9% (95% CI: 88.8%, 93.2%)	Independent training, tune and test datasets were used for training and performance assessment. Training data: 1,266 controls and 1,302 cases; age range broad, 79.7-80.6% White, 13.3% Black, 2.3-3.0% Asian, 3.7-4.0% Other.	Pivotal clinical validation study: 1,164 patients (749 controls and 415 cases) aged 65+ years with heart failure, collected from 12 US sites and 3 International sites.

Indications for Use

EchoGo Amyloidosis 1.0 is an automated machine learning-based decision support system, indicated as a screening tool for adult patients aged 65 years and over with heart failure undergoing cardiovascular assessment using echocardiography. When utilised by an interpreting physician, this device provides information alerting the physician for referral to confirmatory investigations. EchoGo Amyloidosis 1.0 is indicated in adult patients aged 65 years and over with heart failure. Patient management decisions should not be made solely on the results of the EchoGo Amyloidosis 1.0 analysis.

Device Story

EchoGo Amyloidosis 1.0 is a prescription-only, automated machine learning-based decision support system; operates as a screening tool for cardiac amyloidosis (CA). Input: 2D apical four-chamber (A4C) echocardiogram (DICOM format, no contrast, at least one full cardiac cycle). Processing: AI algorithm (convolutional neural network) analyzes input to generate a binary classification (suggestive/not suggestive of CA). Output: Binary classification decision. Usage: Used by interpreting clinicians in clinical settings; integrates via DICOM network interfaces. The device provides an alert for referral to confirmatory investigations; it does not provide a definitive diagnosis. Clinical decision-making remains the responsibility of the physician, incorporating patient history and other diagnostic tests. Benefits: Assists in identifying patients who may require further investigation for cardiac amyloidosis, potentially facilitating earlier detection and management.

Clinical Evidence

Retrospective case-control study (N=1,164; 749 controls, 415 cases) of patients 65+ with heart failure. Primary endpoints: sensitivity 84.5% (95% CI: 80.3%, 88.5%) and specificity 89.7% (95% CI: 87.0%, 92.4%). PPV 82.7% and NPV 90.9% at 36.7% prevalence. Device reported a 14.0% 'no classification' rate due to uncertainty. Repeatability showed 100% agreement; reproducibility showed 85.5% positive agreement and 79.9% negative agreement.

Technological Characteristics

Software-only SaMD; utilizes convolutional neural network AI. Inputs: DICOM echocardiogram clips. Connectivity: DICOM network interfaces. Standards: IEC 62304:2015, ISO 14971:2019, IEC 62366-1:2020, NEMA PS 3.1-3.20 (DICOM). Deployment: Hosted on Ultromics' platform or third-party infrastructure. Fully automated, no GUI.

Indications for Use

Indicated for adult patients aged 65+ with heart failure undergoing echocardiography as a screening tool to alert physicians for referral to confirmatory investigations.

Regulatory Classification

Identification

The adjunctive cardiovascular status indicator is a prescription device based on sensor technology for the measurement of a physical parameter(s). This device is intended for adjunctive use with other physical vital sign parameters and patient information and is not intended to independently direct therapy.

Special Controls

*Classification.* Class II (special controls). The special controls for this device are:(1) Software description, verification, and validation based on comprehensive hazard analysis must be provided, including: (i) Full characterization of technical parameters of the software, including any proprietary algorithm(s); (ii) Description of the expected impact of all applicable sensor acquisition hardware characteristics on performance and any associated hardware specifications; (iii) Specification of acceptable incoming sensor data quality control measures; and (iv) Mitigation of impact of user error or failure of any subsystem components (signal detection and analysis, data display, and storage) on accuracy of patient reports. (2) Scientific justification for the validity of the status indicator algorithm(s) must be provided. Verification of algorithm calculations and validation testing of the algorithm using a data set separate from the training data must demonstrate the validity of modeling. (3) Usability assessment must be provided to demonstrate that risk of misinterpretation of the status indicator is appropriately mitigated. (4) Clinical data must be provided in support of the intended use and include the following: (i) Output measure(s) must be compared to an acceptable reference method to demonstrate that the output measure(s) represent(s) the predictive measure(s) that the device provides in an accurate and reproducible manner; (ii) The data set must be representative of the intended use population for the device. Any selection criteria or limitations of the samples must be fully described and justified; (iii) Agreement of the measure(s) with the reference measure(s) must be assessed across the full measurement range; and (iv) Data must be provided within the clinical validation study or using equivalent datasets to demonstrate the consistency of the output and be representative of the range of data sources and data quality likely to be encountered in the intended use population and relevant use conditions in the intended use environment. (5) Labeling must include the following: (i) The type of sensor data used, including specification of compatible sensors for data acquisition; (ii) A description of what the device measures and outputs to the user; (iii) Warnings identifying sensor reading acquisition factors that may impact measurement results; (iv) Guidance for interpretation of the measurements, including warning(s) specifying adjunctive use of the measurements; (v) Key assumptions made in the calculation and determination of measurements; (vi) The measurement performance of the device for all presented parameters, with appropriate confidence intervals, and the supporting evidence for this performance; and (vii) A detailed description of the patients studied in the clinical validation ( *e.g.,* age, gender, race/ethnicity, clinical stability) as well as procedural details of the clinical study.

Predicate Devices

• EchoGo Heart Failure (K222463)

Submission Summary (Full Text)

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November 15, 2024

Ultromics Limited Jaco Jacobs Chief Regulatory and Compliance Officer 4630 Kingsgate Cascade Way, Oxford Business Park Oxford, OX4 2SU United Kingdom

Re: K240860

Trade/Device Name: EchoGo Amyloidosis (1.0) Regulation Number: 21 CFR 870.2200 Regulation Name: Adjunctive Cardiovasular Status Indicator Regulatory Class: Class II Product Code: SDJ Dated: November 13, 2024 Received: November 13, 2024

Dear Jaco Jacobs:

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.

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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 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.

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-device-advicecomprehensive-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-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-regulatory

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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,

# Stephen C. Browning -S

LCDR Stephen Browning Assistant Director Division of Cardiac Electrophysiology, Diagnostics, and Monitoring Devices Office of Cardiovascular Devices Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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# Indications for Use

Submission Number (if known)

K240860

Device Name

EchoGo Amyloidosis (1.0)

#### Indications for Use (Describe)

EchoGo Amyloidosis 1.0 is an automated machine learning-based decision support system, indicated as a screening tool for adult patients aged 65 years and over with heart failure undergoing cardiovascular assessment using echocardiography.

When utilised by an interpreting physician, this device provides information alerting the physician for referral to confirmatory investigations.

EchoGo Amyloidosis 1.0 is indicated in adult patients aged 65 years and over with heart failure. Patient management decisions should not be made solely on the results of the EchoGo Amyloidosis 1.0 analysis.

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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# 1 Submitter

| Company | Ultromics Limited<br>4630 Kingsgate Cascade Way, Oxford Business Park South, Oxford,<br>Oxfordshire, United Kingdom, OX4 2SU |
|---------------|------------------------------------------------------------------------------------------------------------------------------|
| Contact | Dr. Jaco Jacobs |
| Date Prepared | 15 Nov 2024 |

# 2 Subject Device

| Product Trade Name | EchoGo Amyloidosis |
|--------------------|-------------------------------------------------------|
| Model Number | 1.0 |
| 510(k) | K240860 |
| Manufacturer | Ultromics Limited |
| Medical Speciality | Cardiology |
| Regulation | 21 CFR 870.2200 - Cardiovascular Monitoring Devices |
| Product Code | SDJ - Adjunctive Cardiac Amyloidosis Status Indicator |
| Regulatory Class | II |

EchoGo Amyloidosis is the product trade name and 1.0 is the model number. For the avoidance of doubt, in this submission we combine the product trade name and model number and refer to the subject device as EchoGo Amyloidosis 1.0.

# 3 Predicate Device

| Predicate Device | EchoGo Heart Failure |
|------------------|----------------------|
| 510(k) | K222463 |
| Manufacturer | Ultromics Limited |

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## 4 Device Description

EchoGo Amyloidosis 1.0 takes a 2D echocardiogram of an apical four chamber (A4C) as its input and reports as an output a binary classification decision suggestive of the presence of Cardiac Amyloidosis (CA).

The binary classification decision is derived from an AI algorithm developed using a convolutional neural network that was pre-trained on a large dataset of cases and controls.

The A4C echocardiogram should be acquired without contrast and contain at least one full cardiac cycle. Independent training, tune and test datasets were used for training and performance assessment of the device.

EchoGo Amyloidosis 1.0 is fully automated without a graphical user interface.

The ultimate diagnostic decision remains the responsibility of the interpreting clinician using patient presentation, medical history, and the results of available diagnostic tests, one of which may be EchoGo Amyloidosis 1.0.

EchoGo Amyloidosis 1.0 is a prescription only device.

# 5 Comparison of Intended Use

#### 5.1 Indications for Use

We discuss similarities and differences of the subject and predicate devices.

#### 5.1.1 Subject Device

EchoGo Amyloidosis 1.0 is an automated machine learning-based decision support system, indicated as a screening tool for adult patients aged 65 years and over with heart failure undergoing cardiovascular assessment using echocardiography.

When utilised by an interpreting physician, this device provides information alerting the physician for referral to confirmatory investigations.

EchoGo Amyloidosis 1.0 is indicated in adult populations over 65 years of age with heart failure. Patient management decisions should not be made solely on the results of the EchoGo Amyloidosis 1.0 analysis.

#### 5.1.2 Predicate Device

EchoGo Heart Failure 1.0 is an automated machine learning-based decision support system, indicated as a diagnostic aid for patients undergoing routine functional cardiovascular assessment using echocardiography. When utilised by an interpreting clinician, this device provides information that may be useful in detecting heart failure with preserved ejection fraction (HFpEF).EchoGo Heart Failure 1.0 is indicated in adult populations over 25 years of age. Patient management

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decisions should not be made solely on the results of the EchoGo Heart Failure 1.0 analysis. EchoGo Heart Failure 1.0 takes as input an apical 4-chamber view of the heart that has been captured and assessed to have an ejection fraction ≥50%.

## 6 Discussion

Both subject and primary predicate devices are software-only devices and automated machine learning-based decision support system, indicated for patients undergoing routine cardiovascular assessment using echocardiography.

Both subject and primary predicate are indicated to provide information that may be useful for healthcare professionals in forming a diagnostic work up decisioning. The subject device is intended to be used "as a screening tool for patients undergoing routine cardiovascular assessment using echocardiography" and the primary predicate device is intended to be used "as diagnostic aid for patients undergoing routine functional cardiovascular assessment".

Both subject and primary predicate are intended to provide information on the cardiovascular state of patients. Both subject and primary predicate are used for "patients undergoing routine cardiovascular assessment using echocardiography ".

Both subject and primary predicate devices are indicated in adult populations. The subject device "EchoGo Amyloidosis 1.0 is indicated in adult populations over 65 years of age" and the primary predicate is indicated in adult populations over 25 years of age".

For both subject and primary predicate devices, the patient management decisions should not be made solely on device recommendations.

For purposes of substantial equivalence, the term intended use means the general purpose of the device or its function and encompasses the indications for use. The term indications for use describes the disease the device is intended to serve as a diagnostic aid including a description of the patient population for which the device is intended. Both devices are intended as adjunctive aids for cardiovascular states or conditions and both devices can be used on adult populations. It follows that both devices have intended use of providing adjunctive information on the cardiovascular condition of a patient.

Ultromics believes that general controls and special controls of the predicate device are sufficient to ensure the safety and effectiveness of the subject device.

## 7 Technological Characteristics

A full comparison of the technological characteristics of the subject and predicate devices follows:

- Both devices takes as input a DICOM file consisting of an echocardiogram from medical devices, such as Medical Ultrasound Equipment or Picture Archiving and Communication System (PACS). Both devices therefore receive as input data that is the output of a preceding medical device (e.g. Medical Ultrasound Equipment or PACS) .

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- The output of both devices is based on an artificial intelligence (AI) model developed using a convolutional neural network that produces a classification result.
- Both device reports a classification decision as suggestive or not suggestive of a specific cardiac condition. Both devices are adjunctive cardiovascular status indicators.
- The subject device reports a classification decision as suggestive or not suggestive of the presence of cardiac amyloidosis (CA), while the predicate device reports a classification decision as suggestive or not suggestive of the presence of heart failure with preserved ejection fraction (HFpEF).
- Both subject and primary predicate takes as input an apical 4-chamber view of the heart that has been captured. The subject device takes as input an apical 4-chamber view of the heart that has been captured, while the predicate device takes as input an apical 4chamber view of the heart that has been captured and assessed to have an ejection fraction ≥50%.
- Subject device includes application programming interfaces for input and output for interfacing with external applications and medical devices. This includes the DICOM network interfaces that were also present in the predicate device.
- Subject device allows deployment options permitting functionality to be distributed and replicated, ensuring the scalability, robustness and non-clinical performance of the device.

| Characteristic | Subject Device<br>EchoGo Amyloidosis 1.0 | Predicate Device<br>EchoGo Heart Failure<br>(K222463) |
|--------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Regulation | 21 CFR 870.2200 | 21 CFR 870.2200 |
| Generic Device Type | Adjunctive cardiovascular status<br>indicator | Adjunctive cardiovascular status<br>indicator |
| SaMD | Yes | Yes |
| Characteristic | Subject Device<br>EchoGo Amyloidosis 1.0 | Predicate Device<br>EchoGo Heart Failure<br>(K222463) |
| Indications for Use | EchoGo Amyloidosis 1.0 is an<br>automated machine learning-based<br>decision support system, indicated<br>as a screening tool for adult<br>patients over 65 years of age with<br>heart failure undergoing routine<br>cardiovascular assessment using<br>echocardiography.<br>When utilised by an interpreting<br>physician, this device provides<br>information alerting the physician<br>for referral to confirmatory<br>investigations.<br>EchoGo Amyloidosis 1.0 is indicated<br>in adult populations over 65 years<br>of age with heart failure. Patient<br>management decisions should not<br>be made solely on the results of<br>the EchoGo Amyloidosis 1.0<br>analysis. | EchoGo Heart Failure 1.0 is an<br>automated machine learning-<br>based decision support system,<br>indicated as a diagnostic aid for<br>patients undergoing routine<br>functional cardiovascular<br>assessment using<br>echocardiography.<br>When utilised by an interpreting<br>clinician, this device provides<br>information that may be useful in<br>detecting heart failure with<br>preserved ejection fraction<br>(HFpEF).<br>EchoGo Heart Failure 1.0 is<br>indicated in adult populations<br>over 25 years of age. Patient<br>management decisions should not<br>be made solely on the results of<br>the EchoGo Heart Failure 1.0<br>analysis.<br>EchoGo Heart Failure 1.0 takes as<br>input an apical 4-chamber view of<br>the heart that has been captured<br>and assessed to have an ejection<br>fraction ≥50%. |
| Population | Adults over the age of 65 | Adults over the age of 25 |
| Anatomical Site | Cardiovascular | Cardiovascular |
| Users | Interpreting clinician | Interpreting clinician |
| Machine Learning-<br>Based Algorithm | Yes | Yes |
| Operating platform | Hosted on Ultromics' platform or<br>on third party infrastructure. | Hosted on Ultromics' platform or<br>on third party infrastructure. |
| Interoperability | Interoperability testing conducted<br>with a device conformant with the<br>DICOM Standard (NEMA PS 3.1 -<br>3.20 2022d [Rec# 12-349]) | Interoperability testing conducted<br>with a device conformant with the<br>DICOM Standard (NEMA PS 3.1 -<br>3.20 2022d [Rec# 12-349]) |
| Characteristic | Subject Device<br>EchoGo Amyloidosis 1.0 | Predicate Device<br>EchoGo Heart Failure<br>(K222463) |
| Software | Complies with IEC 62304:2015 and<br>GPSV. Developed under an FDA<br>QSR and ISO 13485:2016 compliant<br>QMS incorporating risk<br>management per ISO 14971:2019.<br>Software verification and validation<br>testing conducted. | Complies with IEC 62304:2015 and<br>GPSV. Developed under an FDA<br>QSR and ISO 13485:2016 compliant<br>QMS incorporating risk<br>management per ISO 14971:2019.<br>Software verification and<br>validation testing conducted. |
| Risk Management | In accordance with ISO 14971:2019 | In accordance with ISO 14971:2019 |
| Cybersecurity | Post-market Management of<br>Cybersecurity in Medical Devices.<br>Content of Premarket Submissions<br>for Management of Cybersecurity in<br>Medical Devices.<br>Cybersecurity for Networked<br>Medical Devices Containing Off-<br>the-Shelf (OTS) Software:<br>Guidance for Industry. | Post-market Management of<br>Cybersecurity in Medical Devices.<br>Content of Premarket<br>Submissions for Management of<br>Cybersecurity in Medical Devices.<br>Cybersecurity for Networked<br>Medical Devices Containing Off-<br>the-Shelf (OTS) Software:<br>Guidance for Industry. |
| Usability | Complies with IEC 62366-1:2020<br>and general use of FDA guidance<br>documents on usability<br>engineering. Formative and<br>summative evaluations conducted<br>with clinicians specialising in<br>cardiology, with knowledge/<br>experience in echocardiography<br>(N=18). | Complies with IEC 62366-1:2020<br>and general use of FDA guidance<br>documents on usability<br>engineering. Formative and<br>summative evaluations conducted<br>with accredited cardiac<br>physiologists (N=2) and<br>cardiologists (N=5). |
| Pre-clinical<br>Performance Testing | No animal studies were conducted. | No animal studies were<br>conducted. |
| Bench Performance<br>Testing | Technical validation, numerical<br>stability, and regression testing. | Technical validation, numerical<br>stability, and regression testing. |
| Clinical Performance<br>Testing | Validated on a validation cohort<br>including 12 US sites and 3<br>International sites representative of<br>the intended use population. | Validated on a US cohort<br>population, comprising 8<br>independent clinical sites<br>representative of the intended use<br>population. |

The following table summarises technological characteristics.

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Any technological differences between the subject and predicate devices raise no new concerns with regards to safety and efficacy. In addition, Ultromics is of the view that general controls

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alongside special controls introduced under the primary product code of the predicate are sufficient to ensure safety and efficacy of the EchoGo Amyloidosis 1.0 device.

# 8 Special Controls

Special controls for regulation 21 CFR 870.2200 follows.

| Control | Description | Evidence |
|---------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1 | Software description, verification, and validation based on comprehensive hazard analysis: | |
| a | Full characterization of technical parameters of the software, including any proprietary algorithm(s); | A.22.1 ADP EchoGo Amyloidosis 1.0:<br>Algorithmic Design Plan<br>A.22.2 ADS EchoGo Amyloidosis 1.0:<br>Algorithm Design Specification<br>A.22.3 ADR EchoGo Amyloidosis 1.0:<br>Algorithmic Design Report |
| b | Description of the expected impact of all applicable acquisition hardware characteristics on performance and any associated hardware specifications; | A.7.1 EchoGo Amyloidosis 1.0 IFU:<br>Clinician Manual<br>A.23.2 PTR EchoGo Amyloidosis 1.0:<br>Clinical Performance Testing Report |
| c | Specification of acceptable data quality control measures | |
| d | Mitigation of impact of user error or failure of any components (data detection and analysis, data display, and storage) on accuracy of patient reports | 1. Data Validation for Prediction Safety<br>2. Fault Tolerance (Component Failure)<br>3. Prevention of Misreporting<br>A.13.1 SDS EchoGo Amyloidosis 1.0:<br>Software Design Specification<br>A.13.2 SDS Ultromics Image Processing<br>Library 2.0: Software Design<br>Specification<br>A.13.3 SDS Ultromics Repository Library<br>2.0: Software Design Specification<br>A.13.4 SDS Ultromics Dicom Library 2.0:<br>Software Design Specification<br>A.13.5 SDS Ultromics Reporting Library<br>3.0: Software Design Specification<br>A.11.2 SRS EchoGo Amyloidosis 1.0:<br>Software Requirement Specification |
| Control | Description | Evidence |
| 2 | Scientific justification for the validity of the<br>status indicator algorithm(s) must be<br>provided. Verification of algorithm<br>calculations and validation testing of the<br>algorithm using a data set separate from<br>the training data must demonstrate the<br>validity of modelling. | A.22.1 ADP EchoGo Amyloidosis 1.0:<br>Algorithmic Design Plan<br>A.22.2 ADS EchoGo Amyloidosis 1.0:<br>Algorithm Design Specification<br>A.22.3 ADR EchoGo Amyloidosis 1.0:<br>Algorithmic Design Report<br>A.7.1 EchoGo Amyloidosis 1.0 IFU:<br>Clinician Manual<br>A.23.1 PTP EchoGo Amyloidosis 1.0:<br>Clinical Performance Testing Plan<br>A.23.2 PTR EchoGo Amyloidosis 1.0:<br>Clinical Performance Testing Report |
| 3 | Usability assessment must be provided to<br>demonstrate that risk of misinterpretation<br>of the status indicator is appropriately<br>mitigated. | A.22.8.1 HFUEP EchoGo Amyloidosis 1.0:<br>Human Factors and Usability<br>Engineering Plan<br>A.22.8.2 USER EchoGo Amyloidosis 1.0 :<br>Usability Engineering Report<br>A.22.8.9 UATP EchoGo Amyloidosis 1.0:<br>User Acceptance Testing Plan<br>A.22.8.11 UATR EchoGo Amyloidosis 1.0:<br>User Acceptance Testing Report |
| 4 | Clinical data must be provided in support of the intended use and include the<br>following | |
| a | Output measure(s) must be compared to an<br>acceptable reference method to<br>demonstrate that the output measure(s)<br>represent(s) the predictive measure(s) that<br>the device provides in an accurate and<br>reproducible manner. | A.23.1 PTP EchoGo Amyloidosis 1.0:<br>Clinical Performance Testing Plan<br>A.23.2 PTR EchoGo Amyloidosis 1.0:<br>Clinical Performance Testing Report |
| b | The data set must be representative of the<br>intended use population for the device. Any<br>selection criteria or limitations of the<br>samples must be fully described and<br>justified. | |
| c | Agreement of the measure(s) with the<br>reference measure(s) must be assessed<br>across the full measurement range. | |
| Control | Description | Evidence |
| d | Data must be provided within the clinical<br>validation study or using equivalent<br>datasets to demonstrate the consistency of<br>the output and be representative of the<br>range of data sources and data quality<br>likely to be encountered in the intended<br>use population and relevant use conditions<br>in the intended use environment. | A.22.2 ADS EchoGo Amyloidosis 1.0:<br>Algorithm Design Specification<br>A.22.3 ADR EchoGo Amyloidosis 1.0:<br>Algorithmic Design Report<br>A.7.1 EchoGo Amyloidosis 1.0 IFU:<br>Clinician Manual<br>A.23.1 PTP EchoGo Amyloidosis 1.0:<br>Clinical Performance Testing Plan<br>A.23.2 PTR EchoGo Amyloidosis 1.0:<br>Clinical Performance Testing Report |
| 5 | Labeling must include the following: | |
| a | The type of input data used, including<br>specification of compatible hardware for<br>data acquisition | A.7.1 EchoGo Amyloidosis 1.0 IFU:<br>Clinician Manual<br>A.8.1 ADMAN EchoGo Amyloidosis 1.0:<br>Administration Manual - Overview |
| b | A description of what the device measures<br>and outputs to the user | A.8.2 ADMAN EchoGo Amyloidosis 1.0:<br>Administration Manual - InputAPI:<br>Specification and Usage<br>A.8.3 ADMAN EchoGo Amyloidosis 1.0:<br>Administration Manual - Configuration<br>Settings<br>A.8.4 ADMAN EchoGo Amyloidosis 1.0:<br>Administration Manual - Installation,<br>Management and Decommissioning<br>A.8.5 ADMAN EchoGo Amyloidosis 1.0:<br>Administration Manual - OutputAPI:<br>Specification and Usage<br>A.8.6 ADMAN EchoGo Amyloidosis 1.0:<br>Administration Manual - Security<br>A.8.7 ADMAN EchoGo Amyloidosis 1.0:<br>Administration Manual -<br>Troubleshooting<br>A.21.3 DICOM EchoGo Amyloidosis 1.0:<br>DICOM Conformance Statement |
| c | Warnings identifying acquisition or other<br>factors that may impact output measures | A.7.1 EchoGo Amyloidosis 1.0 IFU:<br>Clinician Manual |
| d | Guidance for interpretation of the output<br>measures, including warning(s) specifying<br>adjunctive use of the results | |
| Control | Description | |
| e | Key assumptions made in the calculation<br>and determination of results | |
| f | The measurement performance of the<br>device for all presented parameters, with<br>appropriate confidence intervals, and the<br>supporting evidence for this performance | |
| g | A detailed description of the patients<br>studied in the clinical validation (e.g., age,<br>gender, race/ethnicity, clinical stability) as<br>well as procedural details of the clinical<br>study. | |

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Image /page/13/Picture/0 description: The image shows the Ultromics logo. The logo consists of a circular graphic on the left and the word "ULTROMICS" in bold, dark blue letters on the right. The circular graphic is a gradient of blue and green.

# 9 Consensus Standards

The following consensus standards will be used in the design and manufacture of EchoGo Amyloidosis 1.0 .

| Standard | Recognition Number |
|-----------------------------------------------------------------------------------------------------------|--------------------|
| ISO 14971:2019 - Medical Devices - Application of Risk Management to<br>Medical Devices | 5-125 |
| IEC 62304:2015 - Medical Device Software - Software Life Cycle<br>Processes | 13-79 |
| IEC 62366-1:2020 - Medical Devices - Application of Usability<br>Engineering to Medical Devices | 5-129 |
| NEMA PS 3.1 - 3.20 2022d - Digital Imaging and Communications in<br>Medicine (DICOM) Set | 12-349 |
| IEC ISO 10918-1:1994 - Digital Compression and Coding of Continuous-<br>tone Still Images | 12-261 |
| ISO 14155:2020 - Clinical investigation of medical devices for human<br>subjects - Good clinical practice | 2-282 |

Ultromics Limited cites conformity to the voluntary standards above. In addition, EchoGo Amyloidosis 1.0 will be designed and manufactured under a QMS that fully conforms to US Quality System (QS) regulation (21 CFR Part 820) and ISO 13485:2016.

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## 10 Performance Data

#### 10.1 Software Verification and Validation

EchoGo Amyloidosis 1.0 software was developed and tested in accordance with Ultromics' Design Control processes and has been subjected to extensive safety and performance testing. Nonclinical verification and validation test results established that the device meets its design requirements and intended use. A description for the patient characteristics within the training data is provided in the table below.

Software verification was conducted at unit, module, and system integration levels. Risk management analysis generated multiple risk mitigation measures and verification activities. Regression- and numerical stability testing were conducted to ensure the device meets algorithmic specifications. Formative and summative usability assessments were conducted to validate labelling and mitigate against the device outputs being misinterpreted by the clinical user. Cybersecurity and data security testing were conducted to verify that data and patient protected health…