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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.

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.

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.

The provided text describes the performance evaluation of a medical device named "Sonix Health" for quantifying and reporting echocardiography. Here's a breakdown of the requested information:

Device: Sonix Health (K240645)

Software 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.
• Utilizes a two-step algorithm: single identification model for view recognition, followed by deep learning for B-mode and Doppler algorithms. Main algorithm identifies view and segments anatomy.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance

• Total Patients: 335
• Data Provenance:
  • 303 patients (90%) originated from the U.S. (Mayo Clinic in Arizona) and South Korea (Severance Hospital, Seoul).
    • Specifically, 30% (93 patients) of these 303 were from U.S. hospitals.
    • 70% (200 patients) of these 303 were from Korean hospitals.
  • An additional 32 patients (10%) were obtained from South Korea (Severance Hospital, Seoul).
• Recruitment Type: Images were "taken for diagnostic purposes in actual clinical settings" and "acquired following the IRB procedures," suggesting a retrospective collection of existing patient data.

3. Number and Qualifications of Experts for Ground Truth

• Experts for Annotation: Two experienced sonographers with Registered Diagnostic Cardiac Sonographer (RDCS) certification.
• Supervising Experts: Two experienced cardiologists.

4. Adjudication Method for the Test Set

• The text states, "The annotation was supervised by two experienced cardiologists and the consensus annotation was used as the final ground truth." This implies a form of consensus-based adjudication, but the exact process (e.g., if initial annotations were independent, how disagreements were resolved, etc.) is not detailed beyond "consensus annotation."

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study to evaluate "human readers improve with AI vs without AI assistance." The study focuses on evaluating the standalone performance of the AI model against expert manual measurements, and the device is intended for human-in-the-loop use where users review and modify results.

6. Standalone (Algorithm Only) Performance

• Yes, a standalone performance evaluation was primarily done. The metrics presented (accuracy, correlation coefficients, RMSE) directly assess the algorithm's output compared to ground truth, which was established by experts' manual measurements or reference devices. Although the device is designed for human review, the reported performance metrics quantify the automated capabilities of the software.

7. Type of Ground Truth Used

• The ground truth for the test set was established through expert consensus annotation.
• For strain measurements, the ground truth 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)." This means the ground truth combines expert interpretation with measurements derived from established medical software.

8. Sample Size for the Training Set

• The document states, "The training data and validation data are distinct and independent." However, the sample size for the training set is not provided in the given text.

9. How Ground Truth for the Training Set Was Established

• The document explicitly states how the ground truth for the test set was established (expert consensus, aided by reference devices).
• However, the text does not describe how the ground truth for the training set was established.

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The intended use of EPIQ Ultrasound Diagnostic System is diagnostic ultrasound imaging and fluid flow analysis of the human body, with the following indications for use:

Abdominal, Cardiac Adult, Cardiac other (Fetal), Cardiac Pediatric, Cerebral Vascular, Cephalic (Adult), Cephalic (Neonatal), Fetal/Obstetric, Gynecological, Intraoperative (Vascular), Intraoperative (Cardiac), intra-luminal, intra-cardiac echo, Musculoskeletal (Conventional), Musculoskeletal (Superficial), Ophthalmic, Other: Urology, Pediatric, Peripheral Vessel, Small Organ (Breast, Thyroid, Testicle), Transesophageal (Cardiac), Transrectal, Transvaginal, Lung.

The clinical environments where EPIQ Series Diagnostic Ultrasound Systems can be used include clinics, hospitals, and clinical point-of-care for diagnosis of patients.

When integrated with Philips EchoNavigator, the systems can assist the interventionalist and surgeon with image guidance during treatment of cardiovascular disease in which the procedure uses both live X-ray and live echo guidance.

The systems are intended to be installed, used, and operated only in accordance with the safety procedures and operating instructions given in the product user information. Systems are to be operated only by appropriately trained healthcare professionals for the purposes for which they were designed. However, nothing stated in the user information reduces your responsibility for sound clinical judgement and best clinical procedure.

The purpose of this Traditional 510(k) Pre-Market Notification is to introduce the 3D Auto TV (Tricuspid Valve) software application onto the EPIQ Series Diagnostic Ultrasound Systems.

The 3D Auto TV software enables semi-automated quantification of the tricuspid valve. At a high level, this is accomplished through automatically derived measurements from a segmented model of the tricuspid valve annulus formed by the software through model-based segmentation of the acquired ultrasound images.

The 3D Auto CFQ software provides semi-automated quantification of Mitral Requrgitation (MR) volume and peak flow rate based on 3D color flow images. This application uses a known fluid dynamic model of flow that is adapted to the acquired color information. This allows quantitative assessment of mitral valve leakage during systole. The derived result supports the assessment of mitral regurgitation volume and peak flow rate.

No hardware changes to the EPIQ systems are required when using the 3D Auto TV and 3D Auto CFQ, and existing, cleared Philips transducer(s) are used for the software applications.

The software applications are supported by all EPIQ models running software version 11.0 or higher including EPIQ CVx/CVxi. EPIQ Elite Advanced. EPIQ 7. EPIQ 5. The software applications are both associated with the cardiac adult indication.

The provided text describes two software applications, 3D Auto TV and 3D Auto CFQ, for the Philips EPIQ Series Diagnostic Ultrasound System. It details their acceptance criteria and the studies conducted to demonstrate their performance.

1. Table of Acceptance Criteria & Reported Device Performance

2. Sample Size for Test Set and Data Provenance

• 3D Auto TV: The text does not explicitly state the numerical sample size (number of patients or clips) used for the test set. It mentions "cardiac clips were used" and "Subjects whose clips contributed to the study represented a broad range of demographics, body habitus, and their severity of tricuspid regurgitation." The provenance is not explicitly stated (e.g., country of origin), but it is implied to be clinical data (transthoracic and transesophageal echocardiography (TTE, TEE) cardiac clips). It is a retrospective study, as pre-recorded clips were used.
• 3D Auto CFQ: The text does not explicitly state the numerical sample size (number of patients or clips) used for the test set. The provenance is not explicitly stated (e.g., country of origin), but it is implied to be clinical data. It is a retrospective study, as the results were compared to pre-existing Cardiac Magnetic Resonance Imaging (CMR) data.

3. Number of Experts and Qualifications for Ground Truth

• 3D Auto TV:
  • Number of Experts: 3 clinical experts (reviewers).
  • Qualifications: Not explicitly stated beyond "clinical experts (reviewers)."
• 3D Auto CFQ: The ground truth was Cardiac Magnetic Resonance Imaging (CMR) regurgitant volume (RVol). No human experts were used to establish this specific ground truth; rather, it's considered a gold standard imaging modality. For comparison to the PISA method, clinical experts would have performed the PISA measurements, but their number and qualifications are not specified here.

4. Adjudication Method for the Test Set

• The text does not explicitly state an adjudication method (e.g., 2+1, 3+1). For 3D Auto TV, the ground truth was established by "manual measurements by the same reviewers performed within 4D Cardio-View application (K213544)." This implies individual or consensus measurements, but a formal adjudication process is not detailed.
• For 3D Auto CFQ, the primary ground truth was CMR, which does not involve human adjudication in the same way. When compared to the 2D PISA methodology, it's implied that such measurements were collected.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No explicit MRMC comparative effectiveness study demonstrating improved human reader performance with AI assistance vs. without AI assistance was reported. The studies described focus on the standalone performance of the AI software against human manual measurements or other gold standard modalities.

6. Standalone (Algorithm Only) Performance Study

• Yes, standalone performance studies were conducted for both 3D Auto TV and 3D Auto CFQ.
  • 3D Auto TV: The study evaluated the "automation performance of the 3D Auto TV software" and compared its results to manual measurements (ground truth) performed within the 4D Cardio-View application.
  • 3D Auto CFQ: The study evaluated the "performance of the 3D Auto CFQ software" by comparing its regurgitant volume output to CMR (ground truth) and its peak flow rate output to the 2D PISA methodology.

7. Type of Ground Truth Used

• 3D Auto TV: Manual measurements performed by 3 clinical experts using the 4D Cardio-View application (K213544). This can be classified as expert consensus/manual measurement from a predicate device.
• 3D Auto CFQ:
  • For regurgitant volume: Cardiac Magnetic Resonance Imaging (CMR) regurgitant volume (RVol), which is considered a clinical gold standard.
  • For peak flow rate: 2D PISA methodology. While PISA is a widely accepted method in echocardiography, it is also a measurement based on assumptions and manual input, thus falling under a blend of accepted clinical methodology and manual measurement.

8. Sample Size for the Training Set

• The provided document does not specify the sample size for the training set for either 3D Auto TV or 3D Auto CFQ. It only states that the algorithms use "machine learning algorithm without user interaction" for contour generation.

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

• The provided document does not explicitly describe how the ground truth for the training set was established for either 3D Auto TV or 3D Auto CFQ. It mentions "model-based segmentation" for 3D Auto TV and "machine learning algorithm" for both, implying a supervised learning approach where annotated data would have been used for training, but the specifics of that annotation process are not detailed.

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