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The device is general purpose diagnostic ultrasound system for use by qualified healthcare professionals. It is applicable for adults, pregnant women, pediatric patients and neonates.

The device is intended for ultrasound imaging, measurement and analysis of human body and fluid for multiple clinical applications including: abdominal (GYN and Urology), Thoracic/Pleural, Fetal/Ob, small organ (including breast, thyroid, testes), peripheral vessel, neonatal cephalic, pediatric, musculo-skeletal (conventional, superficial), transrectal, trans-vaginal, cardiac adult, cardiatric, magnetic Needle guidance and imaging guidance of interventional procedures (e.g. biopsy).

This device is intended to use by, or by the order of, and under the supervision of a licensed physician qualified to direct the use of the device.

The device is used in hospital, clinics and clinical point-of-care for diagnosis of patients.

The VINNO 8, VINNO 6, VINNO 5 ultrasound devices are laptop digital color ultrasonic diagnostic devices which transmit ultrasound waves into the body tissues and display the echo images of the tissues and blood flow accordingly. The devices are capable of digital acquisition, processing and display and operate from an integrated battery or separate power supply/charger.

The systems also provide for the measurement of anatomical structures and for analysis packages that provide information that is used for clinical diagnosis purposes.

The VGuide NGS function detects the position and orientation of magnetized needles in the presence of the probe and displays this information relative to the ultrasound image. Spatial positioning of the needle, with respect to the ultrasound image, is then updated in real time. This guides the operator to better visualize the needle in the ultrasound image during ultrasound guided needling procedures.

The provided text describes a 510(k) premarket notification for the VINNO 8, VINNO 6, and VINNO 5 ultrasound devices. This document focuses on demonstrating substantial equivalence to predicate devices rather than proving a novel device's performance against specific clinical acceptance criteria through a clinical study.

Therefore, the response will focus on the information relevant to the substantial equivalence determination and the general performance testing mentioned, noting where specific information requested in the prompt (e.g., sample size for test/training sets, number of experts for ground truth, MRMC study, effect size) is not applicable or not provided within this type of regulatory submission.

Here's a breakdown based on the provided text:

1. A table of acceptance criteria and the reported device performance

For a 510(k) submission intending to demonstrate substantial equivalence, the "acceptance criteria" are generally that the new device performs "as safely and as effectively" as the legally marketed predicate device. This is primarily shown through a comparison of technological characteristics and compliance with recognized standards, rather than specific clinical performance metrics for a novel AI algorithm.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This document describes a submission for a general diagnostic ultrasound system, not an AI/ML device that requires a test set of patient data to evaluate algorithmic performance. The "performance data" provided relates to engineering and regulatory compliance testing.

Therefore, information on "sample size used for the test set" and "data provenance" (country of origin, retrospective/prospective) for a performance study of an AI algorithm is not applicable and not provided in this submission.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Since there is no "test set" of patient data for evaluating an AI algorithm's performance described, information on experts for establishing ground truth is not applicable and not provided.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Similarly, as no patient-data-based test set evaluation is described, adjudication methods are not applicable and not provided.

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

This document describes a general diagnostic ultrasound system. It does not appear to incorporate an AI component for interpretation or assistance to human readers that would necessitate an MRMC study. Therefore, an MRMC comparative effectiveness study is not applicable and not mentioned.

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

This is for a diagnostic ultrasound machine, not a standalone AI algorithm. Therefore, "standalone algorithm performance" is not applicable.

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

As this submission is for an ultrasound system itself, not an AI diagnostic algorithm, the concept of "ground truth" concerning patient diagnoses is not applicable to the performance data presented (which covers electrical safety, biocompatibility, software verification, and acoustic output).

8. The sample size for the training set

This document does not describe the development or testing of an AI/ML algorithm that would involve a training set. Therefore, "sample size for the training set" is not applicable and not provided.

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

As there is no AI/ML training set mentioned, this information is not applicable and not provided.

In summary: The provided 510(k) submission demonstrates the substantial equivalence of the VINNO ultrasound systems to predicate devices through technical specifications, compliance with relevant industry standards (e.g., IEC, ISO, NEMA), and standard safety and performance testing for general medical imaging equipment. It does not involve the clinical study methodologies (e.g., test sets, training sets, expert review, MRMC studies) typically associated with the evaluation of AI/ML-driven diagnostic devices.

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