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The iuStar300 Medical Ultrasound Diagnostic System is intended for visualization of internal organs by ultrasound images for medical diagnostic purposes only. It must be operated by qualified and trained Physician or Sonographer. It can be used in following applications: General application, Abdominal, Vascular, OB/GYN, Urology, Breast, Small Parts (breast, thyroid, testes), Musculoskeletal, Superficial Musculoskeletal and Cardiology. Each application includes a set of exams, including the specific measurements, reports, pictograms, annotations and system presets.

The iuStar300 Medical Ultrasound Diagnostic System is a general purpose, mobile, software-controlled, diagnostic ultrasound system with an on-screen display for thermal and mechanical indices related to potential bioeffect mechanisms. iuStar300 Medical Ultrasound Diagnostic System is intended for visualization of internal organs and for medical diagnostic purposes only. It supports 2D, M Mode, CFM and Pulse and Continuous Wave Spectral Doppler, Color Doppler Energy and Directional Color Doppler Energy modes.

Here's a breakdown of the acceptance criteria and study information based on the provided text, recognizing that this document is a 510(k) summary for an ultrasound device without any AI/ML components:

Important Note: The provided text is a 510(k) Premarket Notification for a general-purpose medical ultrasound diagnostic system, the iuStar300. This document details the substantial equivalence of the device to predicate ultrasound devices, meaning it does not contain information about the performance or validation of an Artificial Intelligence (AI) or Machine Learning (ML) component. Therefore, many of the requested fields related to AI performance, ground truth establishment, expert review, and training/test sets are not applicable to this specific document.

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a traditional medical device submission and not an AI/ML component, the "acceptance criteria" discussed are primarily related to safety, performance against established standards, and substantial equivalence to legally marketed predicate devices. The "reported device performance" is implicitly that it meets these standards and is equivalent to the predicates.

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

• Sample Size for Test Set: Not applicable. This document does not describe a test set for validating an AI/ML algorithm. The evaluation is based on meeting engineering standards and comparison to predicate devices, not on a clinical test set of patient data to assess diagnostic accuracy for an AI.
• Data Provenance (e.g., country of origin of the data, retrospective or prospective): Not applicable.

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

• Number of experts: Not applicable.
• Qualifications of experts: Not applicable.

4. Adjudication Method (e.g., 2+1, 3+1, none) for the Test Set

• Adjudication method: Not applicable.

5. If a Multi Reader Multi Case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• MRMC study: No, this document does not describe an MRMC study.
• Effect size: Not applicable as no AI component is discussed.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Standalone performance: No, this document does not describe a standalone AI algorithm performance study.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• Type of ground truth: Not applicable. The "ground truth" in this context refers to compliance with established medical device standards and the functionality observed during non-clinical testing, rather than a clinical ground truth for diagnostic accuracy.

8. The sample size for the training set

• Sample size for training set: Not applicable. This document describes a traditional ultrasound system, not an AI/ML algorithm, and therefore has no training set in the AI sense.

9. How the ground truth for the training set was established

• How ground truth was established: Not applicable.

Summary of the Study:

The document describes a 510(k) Premarket Notification for the iuStar300 Medical Ultrasound Diagnostic System. The "study" referenced is a non-clinical assessment and comparison study to demonstrate substantial equivalence to legally marketed predicate devices (ACUSON X300 DIAGNOSTIC ULTRASOUND SYSTEM and S20 DIGITAL COLOR DOPPLER ULTRASOUND SYSTEM).

The study involved:

• Verification of technical specifications: Comparing the operating principles, basic design, technological characteristics, and modes of operation (B-Mode, M-Mode, CWD, PWD, Color Doppler, Power Doppler, 3D/4D Imaging, Tissue Harmonic Imaging) of the iuStar300 to those of the predicate devices.
• Compliance with recognized standards: Testing the device against international standards for medical electrical equipment safety (IEC 60601-1, EN 60601-1-2, IEC 60601-2-37), acoustic output (NEMA UD 2-2004), and biocompatibility (ISO 10993 series).
• Software validation: Confirming that the system's software has been validated.

The conclusion is that based on this non-clinical testing and comparison, the device is considered safe and effective and substantially equivalent to the predicate devices. No AI/ML components are mentioned or evaluated in this submission.

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