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510(k) Data Aggregation
(28 days)
The ACUSON Sequoia ultrasound imaging system is intended to provide images of, or signals from, inside the body by an appropriately trained healthcare professional in a clinical setting for the following applications: Fetal, Abdominal, Pediatric, Neonatal Cephalic, Small Parts, OB/GYN (useful for visualization of the ovaries, follicles, uterus and other pelvic structures), Cardiac, Pelvic, Vascular, Adult Cephalic, Musculoskeletal and Peripheral Vascular applications.
The system also provides the ability to measure anatomical structures for fetal, abdominal, pediatric, small organ, cardiac, transvaqinal, peripheral vessel, musculoskeletal and calculation packages that provide information to the clinician that may be used adjunctively with other medical data obtained by a physician for clinical diagnosis purposes.
The ACUSON Sequoia Diagnostic Ultrasound System is a multi-purpose mobile, software controlled, diagnostic ultrasound system with an on-screen display of thermal and mechanical indices related to potential bio-effect mechanisms. Its function is to transmit and receive ultrasound echo data and display it in B-Mode, M-Mode, Pulsed (PW) Doppler Mode, Continuous (CW) Doppler Mode, Color Doppler Mode, Color M Mode, Doppler Tissue Mode, Amplitude Doppler Mode, a combination of modes and Harmonic Imaging on a Display.
Based on the provided text, here's an analysis of the acceptance criteria and the study that proves the device meets them:
1. A table of acceptance criteria and the reported device performance
The provided document is a 510(k) Pre-Market Notification, which focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than presenting specific acceptance criteria and performance data for new functions. The "acceptance criteria" here are implicitly the existing performance and safety characteristics of the predicate devices. The "reported device performance" is essentially that the ACUSON Sequoia Diagnostic Ultrasound System performs equivalently to its predicate devices.
| Acceptance Criteria Category | Reported Device Performance |
|---|---|
| Intended Use Equivalence | The ACUSON Sequoia system's intended uses are consistent with its predicate devices (ACUSON Sequoia K193257 and ACUSON S family K172162). It provides images/signals for Fetal, Abdominal, Pediatric, Neonatal Cephalic, Small Parts, OB/GYN, Cardiac, Pelvic, Vascular, Adult Cephalic, Musculoskeletal and Peripheral Vascular applications, and measures anatomical structures for various clinical diagnosis purposes, matching the predicates. It also adds new indications for Adult Cephalic and incorporates specific transducers (CW5, 11M3, 7L2) and a needle guide for 7L2, which were cleared under previous 510(k)s. |
| Technological Characteristics Equivalence | The modified ACUSON Sequoia Ultrasound System shares the same technology and principles as the predicate ACUSON Sequoia (K193257) and the ACUSON S family (K172162). It transmits and receives ultrasound echo data and displays it in various modes (B-Mode, M-Mode, PW Doppler, CW Doppler, Color Doppler, Color M Mode, Doppler Tissue Mode, Amplitude Doppler Mode, combined modes, and Harmonic Imaging). Features, frequencies, modes, and other characteristics (e.g., monitor, touch screen, patient contact materials, safety certifications) are either identical to or within the established range of the predicate devices. |
| Safety and Effectiveness | The device has been evaluated for acoustic output, biocompatibility, cleaning and disinfection effectiveness, and thermal, electrical, electromagnetic, and mechanical safety. It conforms to applicable medical device safety standards (IEC 62359:2010, AAMI ES60601-1:2005, IEC 60601-1-2 Ed 4.0 2014-02, IEC 60601-2-18: Ed 3.0 2009-08, IEC 60601-2-37 Ed 2.1 2015, ISO 10993-1). The manufacturer's design and development process conforms to 21 CFR 820 Quality System Regulation and ISO 13485:2016 quality system standards. |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
The document explicitly states: "Since the ACUSON Sequoia Diagnostic Ultrasound System uses the same technology and principles as existing devices, clinical studies were not required to support substantial equivalence." This indicates that no specific "test set" of clinical data was used for this 510(k) submission to demonstrate performance with acceptance criteria, as the claim is based on equivalence to previously cleared devices.
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)
Not applicable, as no new clinical studies or test sets with ground truth establishment by experts were conducted for this submission.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable, as no new clinical studies or test sets requiring adjudication were conducted for this submission.
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
Not applicable. This is a submission for a diagnostic ultrasound system, not an AI-enabled device requiring a comparative effectiveness study in the context of human reader improvement.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. This is not an AI algorithm. The submission is for a diagnostic ultrasound system.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Not applicable, as no new clinical studies were conducted to establish ground truth for new performance claims. The "ground truth" for the device's capabilities relies on the established safety and efficacy of its predicate devices, which would have undergone their own validation processes during their initial clearance.
8. The sample size for the training set
Not applicable, as there is no mention of a training set, indicating that this is not an AI/machine learning device that performs a diagnostic task for which it needs to be "trained."
9. How the ground truth for the training set was established
Not applicable, as there is no training set mentioned.
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