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The RS80A Diagnostic Ultrasound System and transducers are intended for diagnostic ultrasound imaging and fluid analysis of the human body.

The clinical applications include: Fetal/Obstetrics, Abdominal, Gynecology, Pediatric, Small Organ, Neonatal Cephalic, Adult Cephalic, Trans-rectal, Muscular-Skeletal (Conventional, Superficial), Urology, Cardiac Adult, Cardiac Pediatric and Peripheral vessel.

The RS80A is a general purpose, mobile, software controlled, diagnostic ultrasound system. Its function is to acquire ultrasound data and to display the data as B mode, Color Doppler imaging, Power Doppler imaging (including Directional Power Doppler mode; S-Flow), PW Spectral Doppler mode, CW Spectral Doppler mode, Harmonic imaging, Tissue Doppler imaging, Tissue Doppler Wave, 3D imaging mode (real time 4D imaging mode), Elastoscan Mode or as a combination of these modes. The RS80A also gives the operator the ability to measure anatomical structures and offers analysis packages that provide information that is used to make a diagnosis by competent health care professionals. The RS80A has real time acoustic output display with two basic indices, a mechanical index and a thermal index, which are both automatically displayed.

This is a 510(k) premarket notification for the SAMSUNG MEDISON RS80A Diagnostic Ultrasound System. The purpose of this document is to demonstrate "substantial equivalence" to previously cleared predicate devices, meaning that the new device is as safe and effective as the predicate devices and does not raise new questions of safety or effectiveness. As such, the document primarily focuses on comparing the new device's features and performance to those of predicate devices, rather than establishing acceptance criteria and proving the device meets them through a dedicated standalone study with defined ground truth and expert adjudication.

However, based on the provided text, I can infer information related to acceptance criteria through the comparison tables and the declaration of "substantial equivalence."

Inferred Acceptance Criteria and Reported Device Performance

The acceptance criteria for the RS80A Diagnostic Ultrasound System are implicitly met by demonstrating that its technical characteristics, intended uses, and safety compliance are "substantially equivalent" to predicate devices. This means the device's performance is expected to be on par with or better than the established performance of the cleared predicate devices.

Here's an inferred table of key "acceptance criteria" (represented by the features and standards the device claims to meet as being equivalent to predicate devices) and the "reported device performance" (which is the device possessing these features and meeting these standards):

Study Information:

The document describes a bench and non-clinical test approach rather than a specific clinical trial with a test set.

1. Sample size used for the test set and the data provenance: Not explicitly stated as a distinct "test set" for performance evaluation in the described manner. The submission relies on "substantial equivalence" to predicate devices, implying that the performance of the RS80A is within acceptable ranges demonstrated by already cleared devices. The testing mentioned (acoustic output, biocompatibility effectiveness, thermal, electrical, electromagnetic, and mechanical safety) are likely internal engineering and quality assurance tests, not a clinical "test set" with patient data.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. The document does not describe a clinical study with a "ground truth" established by experts in the context of image interpretation or diagnosis. The "ground truth" for compliance testing is against engineering specifications and international standards.

3. Adjudication method for the test set: Not applicable. No clinical adjudication method is described.

4. 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: Not applicable. This document is for a diagnostic ultrasound system and its transducers, not explicitly an AI-assisted diagnostic device requiring an MRMC study for improved human reader performance. The listed "SW Features" like S-Detect, E-Thyroid, E-Breast, and S-Fusion hint at advanced functionalities that might involve AI-like processing, but the document does not present them as standalone AI clinical decision support systems requiring MRMC studies to show reader improvement. Instead, they are presented as equivalent or improved features compared to predicates.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable for the overall device clearance. The device is a diagnostic ultrasound system, intended for use with a human operator. Even advanced features like S-Detect for lesion analysis are presented as aids within the larger diagnostic system, not as standalone algorithms performing a diagnosis without human interaction.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): For the safety and effectiveness mentioned, the "ground truth" would be the adherence to established international and national standards (e.g., IEC, NEMA, ISO standards) for medical devices. For functional comparisons, the "ground truth" is the established performance and features of the predicate devices. There is no mention of specific clinical ground truth (like pathology or outcomes data) being used for this 510(k) submission, as it relies on substantial equivalence.

7. The sample size for the training set: Not applicable. The document describes a general diagnostic ultrasound system and its transducers, not a machine learning model that would typically have a "training set." While "SW Features" like S-Detect might leverage machine learning, the details of their development (including training sets) are not provided in this 510(k) summary, which focuses on device safety and substantial equivalence rather than algorithm validation.

8. How the ground truth for the training set was established: Not applicable, as no training set is described. If features like S-Detect or E-Thyroid involve machine learning, their internal ground truth establishment would be part of their individual validation, but it's not detailed in this high-level regulatory summary.

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