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The HS40 ultrasound diagnostic system and probes are designed to obtain ultrasound images and analyze body fluids.

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

The HS40 is a general purpose, mobile, software controlled, diagnostic ultrasound system. Its function is to acquire ultrasound data and to display the data as 2D mode, M mode, Color Doppler imaging, Power Doppler imaging (including Directional Power Doppler mode; S-Flow), PW Spectral Doppler mode, CW Spectral Doppler mode, Harmonic imaging(S-Harmonic), Tissue Doppler imaging, Tissue Doppler Wave, Panoramic Imaging, Freehand 3D, 3D imaging mode (real-time 4D imaging mode), Elastoscan Mode or as a combination of these modes. The HS40 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 HS40 has real time acoustic output display with two basic indices, a mechanical index and a thermal index, which are both automatically displayed.

The provided text is a 510(k) Premarket Notification for the Samsung Medison HS40 Diagnostic Ultrasound System. This type of submission is for demonstrating substantial equivalence to a legally marketed predicate device, rather than proving the device meets specific performance acceptance criteria for a novel AI/ML algorithm.

Therefore, the document does not contain the acceptance criteria and the detailed study results as would be presented for a device demonstrating novel AI/ML performance. It focuses on showing the HS40 ultrasound system, including new software features and transducers, is equivalent to existing cleared ultrasound systems.

Here's an analysis based on the information provided, highlighting why the requested details are not present for an AI/ML algorithm in this specific document:

Missing Information (as expected for a 510(k) that is not for a novel AI/ML algorithm):

• Acceptance Criteria Table and Reported Device Performance: This document describes a general diagnostic ultrasound system. It outlines the intended clinical applications (Fetal/Obstetrics, Abdominal, Gynecology, Pediatric, Small Organ, Neonatal Cephalic, Adult Cephalic, Trans-rectal, Trans-vaginal, Muscular-Skeletal, Urology, Cardiac Adult, Cardiac Pediatric and Peripheral vessel) and modes of operation (B-mode, M-mode, PWD, CWD, Color Doppler, etc.). There are no specific performance metrics (e.g., sensitivity, specificity, AUC) or acceptance criteria for an AI/ML algorithm.
• Sample Size for Test Set and Data Provenance: Not applicable, as no specific AI/ML performance test set is described.
• Number of Experts and Qualifications for Ground Truth Establishment (Test Set): Not applicable.
• Adjudication Method for Test Set: Not applicable.
• Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study: Not applicable. The document states, "The subject of this premarket submission, HS40, is not required clinical studies to support substantial equivalence." This means a formal comparative effectiveness study like an MRMC study was not deemed necessary for this 510(k). The "summary of non-clinical tests" focuses on acoustic output, biocompatibility, software function, and safety standards.
• Standalone Performance (Algorithm Only): Not applicable. This is a system submission, not a standalone AI/ML algorithm. While the device mentions "added some of SW features (2DNT, BiometryAssist, E-Strain, NeedleMate, AutoIMT, etc.) that migrated from predicate HS50/60," it does not present performance data for these specific features in the way an AI/ML algorithm's performance would be evaluated. These features are likely automated measurement or image processing tools that were already cleared on other predicate devices.
• Type of Ground Truth Used: Not applicable, as there's no specific AI/ML performance outcome being measured against ground truth.
• Sample Size for Training Set & How Ground Truth for Training Set was Established: Not applicable, as this document does not describe the development or training of a novel AI/ML algorithm. It describes a general ultrasound system and its equivalence to pre-existing cleared devices.

Information from the document that is tangentially related (but not what was asked for directly related to AI/ML acceptance criteria):

• Device Name: HS40 Diagnostic Ultrasound System.
• Intended Use: "Diagnostic ultrasound imaging or fluid flow analysis of the human body." Clinical applications are listed above.
• Technology: "The HS40 employs the same fundamental scientific technology as its predicate device." This highlights the equivalence approach rather than novel technology.
• Predicate Devices: HS40 Diagnostic Ultrasound System (K180409) - Primary Predicate, HS50/60 Diagnostic Ultrasound System (K181336), SONOACE R7 Diagnostic Ultrasound System (K133505), HM70A Diagnostic Ultrasound System (K182894).
• Non-Clinical Tests: Evaluated for acoustic output, biocompatibility, software function, cleaning and disinfection effectiveness, and thermal, electrical, electromagnetic, and mechanical safety, conforming to applicable FDA guidance and medical device safety standards (e.g., IEC 60601-1, IEC 60601-1-2, IEC 60601-2-37, ISO10993-1, ISO14971, NEMA UD 2-2004, NEMA UD 3-2004). This speaks to the safety and fundamental performance of the ultrasound system itself, not the performance of an AI/ML algorithm.
• Regulatory Pathway: 510(k) Premarket Notification - Traditional. This pathway typically relies on substantial equivalence to a predicate, and for general ultrasound systems, does not usually require detailed clinical studies demonstrating AI/ML performance.

In summary, this 510(k) submission is for a general diagnostic ultrasound system and its associated transducers, demonstrating substantial equivalence to already-cleared predicate ultrasound systems. It does not provide the kind of performance data, acceptance criteria, or study details typically associated with the evaluation and clearance of novel AI/ML algorithms.

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