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Diagnostic ultrasound imaging or fluid flow analysis of the human body as follows: Fetal, Abdominal, Intraoperative (specify), Intraoperative Neurological, Pediatric, Small Organ (specify), Neonatal Cephalic, Adult Cephalic, Cardiac, Transesophageal, Transrectal, Transvaginal, Peripheral Vascular, Laparoscopic, Musculo-skeletal Conventional, Musculo-skeletal Superficial. Mixed mode operation includes B/M, B/PWD, B/Bflow/PWD. Intraoperative applications: include liver, pancreas and gall bladder. Small parts applications include breast, testes and thyroid.

The Aloka SSD-5500 V6.0 functions in the same manner as other diagnostic ultrasound devices. High frequency sound waves are transmitted into the body by a piezo-electric transducer. In the body, differences in the acoustic impedance of different tissues reflect a certain amount of the ultrasound energy back to the transducer, where it is transmitted via the probe cable to the system console and processed into an image. The Aloka SSD-5500 V6.0 transducer can also use the Doppler shift of sound reflected from moving tissues (blood) to detect and display flow.

The provided text is a 510(k) summary for the Aloka Model SSD-5500 V6.0 Diagnostic Ultrasound System. This type of submission focuses on demonstrating substantial equivalence to a predicate device, rather than providing detailed clinical study performance metrics or acceptance criteria for a novel AI algorithm. Therefore, much of the requested information regarding acceptance criteria and performance against those criteria, as well as specific details about test and training sets, expert qualifications, and comparative effectiveness studies, is not present in this document.

However, based on the provided text, here's what can be extracted and inferred:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't present specific quantitative acceptance criteria or detailed performance metrics. Instead, it relies on demonstrating substantial equivalence to predicate devices. The "reported device performance" is implicitly that it performs equivalently to the listed predicate devices across various clinical applications and operation modes.

2. Sample Size Used for the Test Set and Data Provenance:

• The document does not explicitly state a sample size for a test set or the provenance (country of origin, retrospective/prospective) of any specific data used to evaluate the substantial equivalence from a performance perspective.
• The evaluation appears to be based on a comparison to characteristics and performance of existing legally marketed predicate devices, rather than a new clinical study with a distinct test set for performance metrics. The tables listing "P" (previously cleared by FDA) and "E" (added under Appendix E) suggest that the current device is being compared to the established performance of the predicate device for those indications.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts:

• This information is not provided in the document.
• Given that this is a 510(k) submission asserting substantial equivalence to predicate devices, it's unlikely that new "ground truth" establishment by a panel of experts for a novel device performance study was required or conducted in the manner of a de novo submission. The "ground truth" for the predicate devices' performance would have been established during their initial clearances.

4. Adjudication Method:

• This information is not provided in the document.

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 information is not applicable and therefore not provided. The Aloka SSD-5500 V6.0 is a diagnostic ultrasound system, not an AI-powered image analysis or diagnostic aid designed to be used in conjunction with human readers to improve their performance. It's a standalone imaging device.

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

• The device being evaluated is a standalone diagnostic ultrasound system. Its performance is inherently "standalone" in the sense that it generates images and Doppler information directly. However, it is not an "algorithm only" device; it's a complete hardware and software system for image acquisition and display. The clinical performance is tied to the images and data it produces, which are then interpreted by a human operator/physician.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.):

• For the purpose of this 510(k) submission, the "ground truth" for the new device's performance is established by demonstrating its substantial equivalence to the performance and characteristics of its predicate devices. The predicate devices themselves would have had their performance (and hence their implicitly "ground-truth-validated" capabilities) established through various means at the time of their clearance, which could have included expert consensus, physical phantom testing, and clinical evaluations. The document does not specify how the predicate devices' performance was originally validated, only that the new device meets those established standards.

8. The Sample Size for the Training Set:

• This information is not applicable and therefore not provided. This device is a traditional diagnostic ultrasound system, not an AI/ML-driven device that requires a "training set" of data in the sense typically associated with machine learning algorithms.

9. How the Ground Truth for the Training Set was Established:

• This information is not applicable and therefore not provided, as the device doesn't rely on a "training set" in the AI/ML context.

In summary:

This 510(k) submission for the Aloka Model SSD-5500 V6.0 Diagnostic Ultrasound System primarily demonstrates its substantial equivalence to previously cleared predicate devices by comparing their technological characteristics, methods of use, safety, and indications. It does not contain details about specific clinical studies with distinct acceptance criteria, test/training sets, or expert ground truth establishment as would be expected for a novel AI/ML device. The "proof" the device meets acceptance criteria is fundamentally its demonstrated sameness in key aspects to already cleared devices and adherence to relevant safety standards.

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