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510(k) Data Aggregation

    K Number
    K251322
    Date Cleared
    2025-07-25

    (87 days)

    Product Code
    Regulation Number
    892.1550
    Reference & Predicate Devices
    N/A
    Why did this record match?
    Reference Devices :

    K231966 LOGIQ E10, K223832 Vivid S70N, K161588 Vscan Extend, K220940 EchoPAC Software Only/EchoPAC Plug-in

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Venue, Venue Go, Venue Fit and Venue Sprint are general purpose diagnostic ultrasound systems for use by qualified and trained healthcare professionals or practitioners that are legally authorized or licensed by law in the country, state or other local municipality in which he or she practices, for ultrasound imaging, measurement, display and analysis of the human body and fluid. The users may or may not be working under supervision or authority of a physician. Users may also include medical students working under the supervision or authority of a physician during their education / training.

    Venue, Venue Go and Venue Fit are intended to be used in a hospital or medical clinic. Venue, Venue Go and Venue Fit clinical applications include: abdominal (GYN and Urology), thoracic/pleural, ophthalmic, Fetal/OB, Small Organ (including breast, testes, thyroid), Vascular/Peripheral vascular, neonatal and adult cephalic, pediatric, musculoskeletal (conventional and superficial), cardiac (adults and pediatric), Transrectal, Transvaginal, Transesophageal, Intraoperative (vascular) and interventional guidance (includes tissue biopsy, fluid drainage, vascular and non-vascular access). Modes of operation include: B, M, PW Doppler, CW Doppler, Color Doppler, Color M Doppler, Power Doppler, Harmonic Imaging, Coded Pulse and Combined modes: B/M, B/Color M, B/PWD, B/Color/PWD, B/Power/PWD, B/CWD, B/Color/CWD.

    The Venue Sprint is intended to be used in a hospital, medical clinic, home environment and road/air ambulance. Venue Sprint clinical applications include: abdominal (GYN and Urology), thoracic/pleural, ophthalmic, Fetal/OB, Small Organ (including breast, testes, thyroid), Vascular/Peripheral vascular, neonatal and adult cephalic, pediatric, musculoskeletal (conventional and superficial), cardiac (adults and pediatric, 40 kg and above) and interventional guidance (includes free hand tissue biopsy, fluid drainage, vascular and non-vascular access). Modes of operation include: B, M, PW Doppler, Color Doppler and Harmonic Imaging.

    Device Description

    Venue, Venue Go, Venue Fit and Venue Sprint are general-purpose diagnostic ultrasound systems intended for use by qualified and trained healthcare professionals to evaluate the body by ultrasound imaging and fluid flow analysis.

    The systems utilize a variety of linear, convex, and phased array transducers which provide high imaging capability, supporting all standard acquisition modes.

    The systems have a small footprint that easily fits into tight spaces and positioned to accommodate the sometimes-awkward work settings of the point of care user.

    The Venue is a mobile system, the Venue Go and Venue Fit are compact, portable systems that can be hand carried using an integrated handle, placed on a horizontal surface, attached to a mobile cart or mounted on the wall. Venue, Venue Go and Venue Fit have a high-resolution color LCD monitor, with a simple, multi-touch user interface that makes the systems intuitive.

    The Venue Sprint is used together with the Vscan Air probes and provides the user interface for control of the probes and the needed software functionality for analysis of the ultrasound images and saving/storage of the related images and videos.

    The Venue, Venue Go, Venue Fit and Venue Sprint systems can be powered through an electrical wall outlet for long term use or from an internal battery for a short time with full functionality and scanning. A barcode reader and RFID scanner are available as additional input devices. The systems meet DICOM requirements to support users image storage and archiving needs and allows for output to printing devices.

    The Venue, Venue Go and Venue Fit systems are capable of displaying the patient's ECG trace synchronized to the scanned image. This allows the user to view an image from a specific time of the ECG signal which is used as an input for gating during scanning. The ECG signal can be input directly from the patient or as an output from an ECG monitoring device. ECG information is not intended for monitoring or diagnosis. Compatible biopsy kits can be used for needle-guidance procedures.

    AI/ML Overview

    The provided document, a 510(k) Clearance Letter and Submission Summary, primarily focuses on the substantial equivalence of the GE Healthcare Venue series of diagnostic ultrasound systems to previously cleared predicate devices. It specifically details the "Auto Bladder Volume (ABV)" feature as an AI-powered component and provides a summary of its testing.

    Here's a breakdown of the acceptance criteria and the study proving the device meets them, based only on the provided text:

    1. Table of Acceptance Criteria and Reported Device Performance (for Auto Bladder Volume - ABV)

    Acceptance CriteriaReported Device Performance
    At least 90% success rate in automatic caliper placement for bladder volume measurements when bladder wall is entirely visualized.Automatic caliper placement success rate: 95.09% (with a 95% confidence level)
    Performance demonstrated consistent across key subgroups including subjects with known BMI (healthy weight, obese, overweight).Healthy weight (18.5-24.9): 95.64%
    Obese (25-29.9): 95.59%
    Overweight (Over 30): 92.6%

    2. Sample Size Used for the Test Set and Data Provenance

    • Test Set (Verification Dataset) Sample Size: 1874 images from 101 individuals.
    • Data Provenance:
      • Country of Origin: USA and Israel.
      • Retrospective or Prospective: Not explicitly stated as either retrospective or prospective. However, the description of "data collected from several different Console variants" for training and verification suggests pre-existing data, which often leans towards a retrospective collection.

    3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

    • Number of Experts: Not explicitly stated. The document refers to "annotators" who performed manual annotation.
    • Qualifications of Experts: Not explicitly stated. The annotators are described as performing "manual annotation," implying they are skilled in this task, but specific qualifications (e.g., radiologists, sonographers, years of experience) are not provided.

    4. Adjudication Method for the Test Set

    • Adjudication Method: Not explicitly stated. The document mentions "annotators performed manual annotation," but does not detail if multiple annotators were used for each case or any specific adjudication process (e.g., 2+1, 3+1 consensus).

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

    • Was an MRMC study done? No. The document states: "The subjects of this premarket submission, Venue, Venue Go, Venue Fit and Venue Sprint, did not require clinical studies to support substantial equivalence." The testing described for ABV is a standalone algorithm performance validation against established ground truth, not a comparative human-AI study.
    • Effect Size of Human Readers Improvement: Not applicable, as no MRMC study was performed.

    6. Standalone (Algorithm Only) Performance Study

    • Was a standalone study done? Yes. The "AI Summary of Testing" section describes a study for the Auto Bladder Volume (ABV) feature, which assesses the algorithm's "automatic caliper placement success rate" against manually established ground truth. This is a standalone performance evaluation of the algorithm.

    7. Type of Ground Truth Used (for ABV Test Set)

    • Ground Truth Type: Expert consensus/manual annotation. The document states: "Ground truth annotations of the verification dataset were obtained as follows: In all Training/Validation and Verification datasets, annotators performed manual annotation on images converted from DICOM files." They identified "landmarks, which represent the bladder edges," corresponding to standard measurement locations.

    8. Sample Size for the Training Set (for ABV)

    • Training Set Sample Size: Total dataset included 8,392 images from 496 individuals. Of these, 1,874 were used for the verification dataset, and "the rest" were used for training/validation. This implies the training/validation set would be 8392 - 1874 = 6518 images from the remaining individuals not included in the verification set.

    9. How the Ground Truth for the Training Set Was Established (for ABV)

    • Ground Truth Establishment: Similar to the verification dataset, "annotators performed manual annotation on images converted from DICOM files" for both Training/Validation and Verification datasets. They chose "4-6 images that represent different bladder volume status" for each individual and annotated "4 different landmarks" per view (transverse and longitudinal) representing bladder edges.
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    K Number
    K240645
    Device Name
    Sonix Health
    Date Cleared
    2024-11-27

    (265 days)

    Product Code
    Regulation Number
    892.2050
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K220940, K213544

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended 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.

    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.

    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.

    AI/ML Overview

    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

    FeatureAcceptance CriteriaReported Device Performance
    View RecognitionAverage accuracy ≥ 84%96.25% average accuracy for additional views.
    Auto MeasureAverage correlation coefficient ≥ 0.80 (compared to manual measurements)0.918 average correlation coefficient (compared to manual measurements).
    Auto Strain
    LVGLS, LARS, LACtsAverage correlation coefficient ≥ 0.80 (compared to manual measurements)0.88 average correlation coefficient.
    RV Free Wall StrainAverage correlation coefficient ≥ 0.60 (compared to manual measurements)0.69 correlation coefficient.
    Average GLSRMSE ≤ 3.00% (compared to manual measurements)2.16% RMSE.
    Segmental Longitudinal StrainRMSE ≤ 7.50% (compared to manual measurements)6.32% RMSE.

    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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