(85 days)
The diagnostic ultrasound system and probes are designed to obtain ultrasound images and analyze body fluids.
The clinical applications include: Fetal/Obstetrics, Abdominal, Gynecology, Intraoperative, Small Organ, Neonatal Cephalic, Adult Cephalic, Trans-vaginal, Muscular-Skeletal (Conventional, Superficial), Urology, Cardiac Adult, Cardiac Pediatric, Trans-esophageal(Cardiac) and Peripheral vessel.
It is intended for use by, or by the order of, and under the supervision of, an appropriately trained healthcare professional who is qualified for direct use of medical devices. It can be used in hospitals, private practices, clinics and similar care environment for clinical diagnosis of patients.
Modes of Operation: 2D mode, Color Doppler mode, Pulsed Wave (PW) Doppler mode, Continuous Wave (CW) Doppler mode, Tissue Doppler Imaging (TDI) mode, Tissue Doppler Wave (TDW) mode, Power Doppler (PD) mode, ElastoScan™ Mode, Multi-Image mode(Dual, Quad), Combined modes, 3D/4D mode
The RS85 / RS80 EVO are 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, Tissue Doppler imaging, Tissue Doppler Wave, 3D imaging mode (real-time 4D imaging mode), Elastoscan Mode, MV-Flow Mode or as a combination of these modes.
The RS85 / RS80 EVO also give the ability to measure anatomical structures and offers analysis packages that provide information that may aid in making a diagnosis by competent health care professionals.
The RS85 / RS80 EVO have real time acoustic output display with two basic indices, a mechanical index and a thermal index, which are both automatically displayed.
The provided document, a 510(k) summary for the Samsung Medison RS85 / RS80 EVO Diagnostic Ultrasound System, does not contain the detailed acceptance criteria and performance study information typically required for an AI/ML-based medical device.
The document explicitly states in section 12: "The subject of this premarket submission, RS85 / RS80 EVO, did not require clinical studies to support substantial equivalence." This indicates that the regulatory clearance was based on substantial equivalence to a predicate device (RS85 Diagnostic Ultrasound System K192903) through non-clinical testing, rather than a performance study involving AI/ML components with specific acceptance criteria.
The information provided pertains to general ultrasound system functions, safety standards, and technological equivalence to a previous model, not to the performance of an AI-driven feature.
Therefore, I cannot extract the requested information regarding acceptance criteria and performance study details for an AI component from this document.
To answer your request, if this were a document for an AI/ML-driven device, the following would typically be observed:
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A table of acceptance criteria and the reported device performance: This would be found in a dedicated section detailing the clinical or technical validation of the AI feature. It would list performance metrics (e.g., sensitivity, specificity, AUC, accuracy) with pre-defined thresholds and the actual values achieved by the device.
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Sample sized used for the test set and the data provenance: This information would be crucial for evaluating the robustness and generalizability of the AI model. It would include the number of cases/patients in the test set, their demographic breakdown, and where the data was collected (e.g., single-center, multi-center, specific countries), and whether it was collected retrospectively or prospectively.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts: For expert consensus ground truth, the number of readers, their specialty (e.g., board-certified radiologists), and years of experience would be detailed.
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Adjudication method (e.g., 2+1, 3+1, none) for the test set: If multiple experts were used to establish ground truth, the method for resolving discrepancies (e.g., majority vote, senior expert arbitration) would be described.
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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 would be a specific section demonstrating the human-AI team performance, often comparing reader performance with and without AI assistance using metrics like AUC, sensitivity, or specificity, and reporting the statistically significant improvement (effect size).
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If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Often, an AI model's standalone performance is evaluated first, before assessing its impact in a human-in-the-loop setting.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc): This would specify the "gold standard" against which the AI's performance was measured.
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The sample size for the training set: The number of cases/patients used to train the AI model would be an important detail to understand the model's learning capacity.
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How the ground truth for the training set was established: The method for labeling or annotating the data used for training the AI model would be described.
In summary, the provided document focuses on the substantial equivalence of a general diagnostic ultrasound system and its transducers, rather than the performance evaluation of an AI/ML-driven component within such a system.
§ 892.1550 Ultrasonic pulsed doppler imaging system.
(a)
Identification. An ultrasonic pulsed doppler imaging system is a device that combines the features of continuous wave doppler-effect technology with pulsed-echo effect technology and is intended to determine stationary body tissue characteristics, such as depth or location of tissue interfaces or dynamic tissue characteristics such as velocity of blood or tissue motion. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.