(265 days)
Yes
The document explicitly states that Sonix Health utilizes artificial intelligence and deep learning algorithms for image classification, measurement, and segmentation.
No.
The device is intended for quantifying and reporting echocardiography for diagnosis and analysis, not for treatment.
Yes
Explanation: The "Intended Use / Indications for Use" states that Sonix Health is "intended for quantifying and reporting echocardiography for use by or on the order of a licensed physician," and it is used for "diagnostic purposes in actual clinical settings" as per the test set description.
Yes
The device description explicitly states "Sonix Health will be offered as SW only, to be installed directly on customer PC hardware." This confirms it is a software-only medical device.
No, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostic devices are used to examine specimens (like blood, urine, or tissue) taken from the human body to provide information for diagnosis, monitoring, or screening. This testing is performed outside of the body (in vitro).
- Sonix Health's Function: Sonix Health processes and analyzes medical images (specifically ultrasound images) acquired from within the patient's body. It quantifies and reports echocardiography data based on these images.
- No Specimen Analysis: The description of Sonix Health does not mention the analysis of any biological specimens. Its input is image data, not biological samples.
Therefore, based on the provided information, Sonix Health falls under the category of medical image processing software, not an In Vitro Diagnostic device.
No
The clearance letter does not explicitly state that the FDA has reviewed and approved or cleared a PCCP for this specific device. The provided text mentions "Control Plan Authorized (PCCP) and relevant text" but states "Not Found" under it, indicating no such information is present in the document.
Intended Use / Indications for Use
Sonix Health is intended for quantifying and reporting echocardiography for use by or on the order of a licensed physician. Sonix Health accepts DICOM-compliant medical images acquired from ultrasound imaging devices. Sonix Health is indicated for use in adult populations.
Ultrasound images are acquired via the B (2D), M, Pulsed-wave Doppler, and Continuous-wave Doppler modes.
Product codes (comma separated list FDA assigned to the subject device)
QIH, LLZ
Device Description
Sonix Health comes with the following functions:
- Checking ultrasound multiframe DICOM
- Echocardiography multiframe DICOM classification and automatic measurement.
- Verification of the results and making adjustments manually.
- Providing the report for analysis
Sonix Health will be offered as SW only, to be installed directly on customer PC hardware. Sonix Health is DICOM compliant and is used within a local network.
Mentions image processing
Not Found
Mentions AI, DNN, or ML
Sonix Health utilizes a two-step algorithm. A single identification model identifies a view in the first step. The second step performs the deep learning according to the view. The deep learning algorithms for the second step are categorized as B-mode, and Doppler algorithms. The main algorithm of Sonix Health is to identify the view and segment the anatomy in the image.
Sonix Health utilizes artificial intelligence to automate previous manual quantification tasks, resulting in increased efficiency for users.
Input Imaging Modality
Ultrasound images are acquired via the B (2D), M, Pulsed-wave Doppler, and Continuous-wave Doppler modes.
Anatomical Site
heart
Indicated Patient Age Range
adult populations
Intended User / Care Setting
licensed physician / Inside of hospitals, clinics, and physician's offices.
Description of the training set, sample size, data source, and annotation protocol
The training data and validation data are distinct and independent.
Description of the test set, sample size, data source, and annotation protocol
We utilized heart images from 335 patients, taken for diagnostic purposes in actual clinical settings, to evaluate the performance of Sonix Health's Auto-Measure and Auto-Strain software. The test images were acquired following the IRB procedures. 303 patients were American, with images source from Mayo Clinic in Arizona, and Severance Hospital, Seoul, South Korea. Among them, 30% (93 patients) of the patients originated from U.S. hospitals, while 70% (200 patients) came from Korean hospital. 32 (32/335 = 10%) patients were obtained from South Korean with images source from Severance Hospital, Seoul, South Korea.
The ground truth annotation for the test was performed by two experienced sonographers with a Registered Diagnostic Cardiac Sonographer (RDCS) certification. The annotation was supervised by two experienced cardiologists and the consensus annotation was used as the final ground truth for strain measurements was established by the experts with the help of the reference devices (EchoPAC for global longitudinal, segmental and RV free wall strain and TOMTEC Arena for LA reservoir and contraction strain.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Non-Clinical and Standalone Test Summary. A total of 335 patients' images were used for performance validation, of which 303 (90%) were American and 32 (10%) were Korean. The test showed that Sonix Health software successfully passed all categories of evaluation: it achieved an average accuracy of 96.25% for the additional views in the 'View Recognition' software (exceeding the acceptable threshold of 84%) and an average correlation coefficient of 0.918 (above the acceptable threshold of 0.80) when compared to manual measurements made by participating experts for 'Auto Measure'. For the 'Auto Strain', it attained an average correlation coefficient of 0.88 for LVGLS values and LARS and LACts, surpassing the acceptable threshold of 0.80 when compared to manual measurements made by participating experts. And it attained a correlation coefficient 0.69 of RV Free wall strain surpassing the acceptable threshold of 0.60 when compared to manual measurements made by participating experts. Additionally, it demonstrated an RMSE of 2.16 % for average GLS, staying below the acceptable threshold of 3.00. For Segmental Longitudinal Strain, the RMSE was 6.32%, also below the acceptable threshold of 7.50%, compared to manual measurements. We also demonstrated the statistical agreement between Sonix Health's automated measurements and the experts' manual measurements by providing Bland-Altman plots for all measurements. This report also includes additional measurements (A4C RV-focused RV FAC; A4C: LVEF, SV; PLAX LV: EF, RWT; and M-mode: EF, SV, EDV, ESV, %FS, LVd Mass) derived as simple arithmetic statistics of measurements which are provided by Sonix Health. Detailed performance testing results and the associated subgroup analysis are reported in the labeling.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Accuracy: 96.25% for 'View Recognition' (threshold: 84%).
Correlation Coefficient: 0.918 for 'Auto Measure' (threshold: 0.80).
Correlation Coefficient: 0.88 for LVGLS values, LARS, and LACts (threshold: 0.80).
Correlation Coefficient: 0.69 of RV Free wall strain (threshold: 0.60).
RMSE: 2.16 % for average GLS (threshold: 3.00).
RMSE: 6.32% for Segmental Longitudinal Strain (threshold: 7.50%).
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.
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
November 27, 2024
Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo is in blue and includes the letters "FDA" in a square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION".
Ontact Health Co., Ltd Seungah Lee Division Director 5F Erom Tower, 50-5, Ewhayeodae-gil, Seodaemun-gu Seoul, 03764 Korea, South
Re: K240645
Trade/Device Name: Sonix Health Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH, LLZ Dated: March 7, 2024 Received: November 1, 2024
Dear Seungah Lee:
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 (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.
1
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 Rue"). 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.
2
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device (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,
Samul for
Jessica Lamb, Ph.D. Assistant Director Imaging Software Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
3
Indications for Use
Form Approved: OMB No. 0910-0120 Expiration Date: 07/31/2026 See PRA Statement below.
Submission Number (if known)
Device Name
Sonix Health
Indications for Use (Describe)
Sonix Health is intended for quantifying and reporting echocardiography for use by or on the order of a licensed physician. Sonix Health accepts DICOM-compliant medical images acquired from ultrasound imaging devices. Sonix Health is indicated for use in adult populations.
Ultrasound images are acquired via the B (2D), M, Pulsed-wave Doppler, and Continuous-wave Doppler modes.
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)
CONTINUE ON A SEPARATE PAGE IF NEEDED.
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4
K240645
510(k) Summary
[As required by 21 CFR 807.92]
1. Date
November 26, 2024
Submitter's Information & Contact Person 2.
- Submitter: ONTACT HEALTH Co., Ltd.
- Address: 5F Erom Tower, 50-5, Ewhayeodae-gil, Seodaemun-gu, Seoul, 03764, Republic of Korea
- Contact Person: Seungah Lee / Division Director
- Telephone No.: +82-2- 362-9610
- Email Address: ontact01@ontacthealth.com
Trade Name, Common Name, Classification 3.
- Common name: Workstation Software for cardiac ultrasound image review, analysis, and reporting
- Trade name: Sonix Health
| Classification Description | 21 CFR Section | Product Code |
|---|---|---|
| Automated Radiological Image Processing Software | 21 CFR 892.2050 | QIH, LLZ |
As stated in 21 CFR parts 892.2050 of the device has been classified as Class II.
Identification of Predicate Device(s) 4.
The identified predicate devices within this submission are shown as follow:
Predicate Device
- 510(k) Number: K230209 .
- Trade/Device name: Sonix Health .
- Manufacturer: ONTACT HEALTH Co., Ltd .
Reference Devices
5
Description of the Device 5.
Sonix Health comes with the following functions:
- Checking ultrasound multiframe DICOM
- Echocardiography multiframe DICOM classification and automatic measurement.
- Verification of the results and making adjustments manually.
- Providing the report for analysis
Sonix Health will be offered as SW only, to be installed directly on customer PC hardware. Sonix Health is DICOM compliant and is used within a local network.
Image /page/5/Figure/7 description: The image shows a diagram of a local area network. Within the network is a personal computer that contains Sonix Health. Sonix Health contains a Windows application and an AI Engine. The Windows application and AI Engine are connected to each other, and the NAS is connected to the Windows application.
Sonix Health utilizes a two-step algorithm. A single identification model identifies a view in the first step. The second step performs the deep learning according to the view. The deep learning algorithms for the second step are categorized as B-mode, and Doppler algorithms. The main algorithm of Sonix Health is to identify the view and segment the anatomy in the image.
6. Indications for Use
Sonix Health is intended for quantifying and reporting echocardiography for use by or on the order of a licensed physician. Sonix Health accepts DICOM-compliant medical images acquired from ultrasound imaging devices. Sonix Health is indicated for use in adult populations.
Ultrasound images are acquired via the B (2D), M, Pulsed-wave Doppler, and Continuous-wave Doppler modes.
6
7. Determination of Substantial Equivalence
| Description | Decision | Subject Device (K240645) | Predicate Device (K230209) |
|---|---|---|---|
| Trade/Device name | Sonix Health | Sonix Health | |
| Product Code | QIH (Subsequent Product Code: LLZ) | QIH (Subsequent Product Code: LLZ) | |
| Regulatory Class | SE | 2 | 2 |
| Regulation Number | - | 21 CFR 892.2050 | 21 CFR 892.2050 |
| Intended use | SE | Same | Sonix Health is intended for |
| quantifying and reporting | |||
| echocardiography for use by or on the | |||
| order of a licensed physician. Sonix | |||
| Health accepts DICOM-compliant | |||
| medical images acquired from | |||
| ultrasound imaging devices. Sonix | |||
| Health is indicated for use in adult | |||
| populations. | |||
| Indications for use | SE | Same | Ultrasound images are acquired via the |
| B (2D), M, Pulsed-wave Doppler, and | |||
| Continuous-wave Doppler modes. | |||
| Where used | |||
| (hospital, home, | |||
| ambulance, etc.) | SE | Same | Inside of hospitals, clinics, and |
| physician's offices. | |||
| Application description | SE | Same | Sonix Health utilizes artificial |
| intelligence to automate previous | |||
| manual quantification tasks, resulting | |||
| in increased efficiency for users. Our | |||
| system performs view classification | |||
| and measurements according to the US | |||
| ASE guidelines, and the users can | |||
| review and modify the results if | |||
| necessary. | |||
| Semi-automated | |||
| view classification | SE | PLAX LV, PLAX zoomed AV, PLAX | |
| zoomed MV, PLAX zoomed AV&MV, | |||
| PLAX zoomed Aorta, A4C, A4C zoomed | |||
| LV, A4C RV-focused, A2C, A2C zoomed | |||
| LV, A3C, A3C zoomed LV, A5C, PSAX | |||
| level of great vessels, PSAX level of MV, | |||
| PSAX level of papillary muscles, PSAX | |||
| level of apex, SC4C, SC long axis IVC, M- | |||
| mode LA/Ao, M-mode LV, M-mode | |||
| TAPSE, CW Doppler MS, CW Doppler | |||
| MR, PW Doppler MV, CW Doppler AV in | |||
| parasternal, CW Doppler AV, CW Doppler | |||
| AR, CW Doppler TR, CW Doppler PV, | |||
| CW Doppler PR, PW Doppler RVOT, PW | |||
| Doppler LVOT, Mitral annulus TDI septal, | |||
| Mitral annulus TDI lateral, Tricuspid | |||
| annulus TDI lateral | PLAX LV, A4C, A4C Zoomed LV, | ||
| A2C, A2C zoomed LV, M-mode | |||
| LA/Ao, M-mode LV, CW Doppler | |||
| MS, CW Doppler MR, PW Doppler | |||
| MV, CW Doppler AV, CW Doppler | |||
| AR, CW Doppler TR, CW Doppler | |||
| PV, CW Doppler PR, PW Doppler | |||
| RVOT, PW Doppler LVOT, DTI MV | |||
| annulus | |||
| Description | Decision | Subject Device (K240645) | Predicate Device (K230209) |
| Semi-automated | |||
| measurements | SE | [B-Mode] | |
| IVSd (2D), IVSs (2D), LVIDd (2D), | |||
| LVIDs (2D), LVPWd (2D), LVPWs (2D), | |||
| Ao (2D), LA (2D), RVOT (2D), LVOT | |||
| (2D), EF(Teich) (2D), RWT (2D), | |||
| LAESV A4C, LVESV A4C, LVEDV | |||
| A4C, LVGLS / LVSLS A4C, LARS A4C, | |||
| LACTs A4C, LVEF MOD A4C, SV MOD | |||
| A4C, RVEDA A4C, RVESA A4C, RVD1 | |||
| A4C, RVD2 A4C, RVD3 A4C, RVFWS | |||
| A4C, RV FAC A4C, LVESV A2C, | |||
| LVEDV A2C, LAESV A2C, LVGLS/ | |||
| LVSLS A2C, LVEF MOD A2C, LVGLS / | |||
| LVSLS A3C | |||
| [M-Mode] | |||
| LAd (M), LAs (M), Aod (M), Aos (M), | |||
| IVSd (M), IVSs (M), LVIDd (M), LVIDs | |||
| (M), LVPWd (M), LVPWs (M), EF(Teich) | |||
| (M), RWT(Teich) (M), SV(Teich) (M), | |||
| LVEDV(Teich) (M), LVESV(Teich) (M), | |||
| LVd Mass(ASE) (M), %FS (M), RV | |||
| TAPSE (M) | |||
| [Doppler] | |||
| MS Vmax, MS VTI, MS PHT, MR Vmax, | |||
| MR VTI, MV Peak E Vel, MV Peak A | |||
| Vel, MV DecT, AV Vmax, AV VTI, PV | |||
| Vmax, PV VTI, AR Vmax, AR PHT, PR | |||
| Vmax, PR EDV, TR Vmax, TR VTI, | |||
| RVOT Vmax, RVOT VTI, RVOT AccT, | |||
| LVOT Vmax, LVOT VTI, E' Sept, A' Sept, | |||
| S' Sept, E' Lat, A' Lat, S' Lat(Mitral), S' Lat | |||
| (Tricuspid) | IVSd (2D), IVSs (2D), LVIDd (2D), | ||
| LVIDs (2D), LVPWd (2D), LVPWs | |||
| (2D), LA (2D), Ao (2D), LVESV / | |||
| LVEDV (4CH), LAV (4CH), LVESV / | |||
| LVEDV (4CH), LVESV / LVEDV | |||
| (2CH), LAV (2CH), LVESV / LVEDV | |||
| (2CH), LA (mmode), Aorta (m-mode), | |||
| IVSd (m-mode), LVIDd (m-mode), | |||
| LVPWd (m-mode), IVSs (m-mode), | |||
| LVIIDs (m-mode), LVPWs (m-mode), | |||
| MV Peak E Vel, MV Peak A Vel, MV | |||
| Decel Time, e' (med), a' (med), s' | |||
| (med), LVOT Vmax, LVOT VTI, | |||
| RVOT Vmax, RVOT VTI, PV AccT, | |||
| TR Vmax, TR VTI, AR Vmax / PR | |||
| Vmax, AR PHT / PR PHT, MV Vmax, | |||
| MV VTI, MV, PHT, MR Vmax, MR | |||
| VTI, AV Vmax / PV Vmax, AV VTI / | |||
| PV VTI, RVOT (2D) | |||
| Semi-automation | |||
| technology | SE | Same | After the automated analysis is |
| complete, users can review and modify | |||
| the results as needed. The product is | |||
| intended for use by, or on the order of a | |||
| licensed physician. |
The identified predicate device within this submission is shown in the following table:
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- It is necessary for the end user to verify, confirm, and modify the analysis results when using Subject Device.
8
Non-Clinical and Standalone Test Summary 8.
Performance Standards
Digital Imaging and Communications in Medicine (DICOM) Set (Ps3.1 - .20) IEC 62304:2006, Medical Device Software - Software Life Cycle Processes. ISO 14971 Second edition 2007-03-01. Medical devices - Application of risk management to medical devices. IEC 62366-1 Edition 1.1 2020-06, Medical devices-Part 1 Application of usability engineering to medical devices.
Sonix Health is classified as a Basic Level of Documentation. The software verification and validation were conducted in compliance with IEC 62304 "Medical device software life cycle process" and the internal design control process to ensure safety and effectiveness. Throughout the verification and validation process, traceability was maintained, encompassing risk management (including Cyber Security and Usability).
The training data and validation data are distinct and independent. The ground truth annotation for the test was performed by two experienced sonographers with a Registered Diagnostic Cardiac Sonographer (RDCS) certification. The annotation was supervised by two experienced cardiologists and the consensus annotation was used as the final ground truth for strain measurements was established by the experts with the help of the reference devices (EchoPAC for global longitudinal, segmental and RV free wall strain and TOMTEC Arena for LA reservoir and contraction strain.
The results of the performance testing for additional features in this version are as follows. All other conditions are the same as the existing version (K230209).
We utilized heart images from 335 patients, taken for diagnostic purposes in actual clinical settings, to evaluate the performance of Sonix Health's Auto-Measure and Auto-Strain software. The test images were acquired following the IRB procedures. 303 patients were American, with images source from Mayo Clinic in Arizona, and Severance Hospital, Seoul, South Korea. Among them, 30% (93 patients) of the patients originated from U.S. hospitals, while 70% (200 patients) came from Korean hospital. 32 (32/335 = 10%) patients were obtained from South Korean with images source from Severance Hospital, Seoul, South Korea.
Sonix Health software was designed to assist clinicians in adult heart measurements. Three functions of Sonix Health software are the 'View Recognition', 'Auto Measure' and 'Auto Strain'. This performance test report has described the performance test procedures and the result for novel features of the 'Auto Measure' and 'Auto Strain' software. Additionally, the previously approved 'View Recognition' feature demonstrates that it works well for additional views as well. A total of 335 patients' images were used for performance validation, of which 303 (90%) were American and 32 (10%) were Korean.
The test showed that Sonix Health software successfully passed all categories of evaluation: it achieved an average accuracy of 96.25% for the additional views in the 'View Recognition' software (exceeding the acceptable threshold of 84%) and an average correlation coefficient of 0.918 (above the acceptable threshold of 0.80) when compared to manual measurements made by participating experts for 'Auto Measure'. For the 'Auto Strain', it attained an average correlation coefficient of 0.88 for LVGLS values and LARS and LACts, surpassing the acceptable threshold of 0.80 when compared to manual measurements made by participating experts. And it
9
attained a correlation coefficient 0.69 of RV Free wall strain surpassing the acceptable threshold of 0.60 when compared to manual measurements made by participating experts. Additionally, it demonstrated an RMSE of 2.16 % for average GLS, staying below the acceptable threshold of 3.00. For Segmental Longitudinal Strain, the RMSE was 6.32%, also below the acceptable threshold of 7.50%, compared to manual measurements. We also demonstrated the statistical agreement between Sonix Health's automated measurements and the experts' manual measurements by providing Bland-Altman plots for all measurements. This report also includes additional measurements (A4C RV-focused RV FAC; A4C: LVEF, SV; PLAX LV: EF, RWT; and M-mode: EF, SV, EDV, ESV, %FS, LVd Mass) derived as simple arithmetic statistics of measurements which are provided by Sonix Health. Detailed performance testing results and the associated subgroup analysis are reported in the labeling
Through verification and validation activities, Sonix Health is confirmed to meet all the design and performance requirements, demonstrating substantial equivalence to the predicate device.
9. Conclusion
In conclusion, all verification and validation activities demonstrated that the design specifications and technological characteristics of Sonix Health meet the applicable requirements and Sonix Health is substantially equivalent to the currently marketed predicate device.