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Diagnostic imaging or fluid flow analysis of the human body as follows: Fetal, Abdominal, Intraoperative (abdominal, neurological), Pediatric, Small Organ, Neonatal Cephalic, Adult Cephalic, Cardiac, Transesophageal, Transrectal, Transvaginal, Transurethral, Intravascular, Peripheral vessel, Laparoscopic, Musculo-skeletal (Conventional, Superficial). The system also provides for the measurement of anatomical structures and for analysis packages that provide information that is used for clinical diagnosis purposes.

The Ebisu is a general purpose, mobile, software-controlled, diagnostic ultrasound system with an on-screen display for thermal and mechanical indices related to potential bioeffect mechanisms. Its function is to acquire primary or secondary harmonic ultrasound echo data and display it in: B-Mode, M-Mode, a combination of modes, 3D imaging or Harmonic Imaging on a CRT display.

The provided text is a 510(k) summary for the Ebisu Diagnostic Ultrasound System and subsequent FDA clearance letters for several transducers. It primarily focuses on demonstrating substantial equivalence to predicate devices and adherence to performance standards, rather than presenting a performance study with acceptance criteria and results for a new AI-powered diagnostic device.

Therefore, many of the requested categories in the prompt (e.g., sample size for test set, number of experts for ground truth, adjudication method, MRMC comparative effectiveness study, standalone performance, sample size for training set, ground truth for training set) are not applicable or not present in the provided document.

Instead, the document details safety and effectiveness standards the device is designed to meet and lists its intended uses by clinical application and mode of operation. The "acceptance criteria" here refer to these safety and performance standards and the "study" is the manufacturer's internal data and declarations that the device meets these standards, which are reviewed by the FDA for clearance.

Here's a breakdown of the relevant information:

1. Table of Acceptance Criteria and the Reported Device Performance

The acceptance criteria are generally adherence to recognized safety and performance standards for medical equipment and substantial equivalence to predicate devices. The "reported device performance" is the manufacturer's declaration and the FDA's concurrence that the device meets these standards and is substantially equivalent.

Intended Uses (Indications for Use for each transducer model listed in tables in pages 5-17):

The device performance is deemed acceptable if it can safely and effectively perform diagnostic imaging for the specified clinical applications (e.g., Fetal, Abdominal, Pediatric, Small Organ, Neonatal/Adult Cephalic, Cardiac, Vascular, Musculoskeletal) using the indicated modes of operation (e.g., B-Mode, M-Mode, PWD, CWD, Color Doppler, Combined BM, Tissue Harmonic Imaging, 3D imaging, B&W SieScape panoramic imaging).

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not provided in the document. As this is a 510(k) for an ultrasound system based on substantial equivalence, detailed clinical study data with specific test sets and data provenance for performance metrics (as would be typical for an AI-powered diagnostic device) are not part of this submission type.

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)

This information is not provided in the document. The substantial equivalence process relies on comparison to predicate devices, not on a new clinical study with expert-adjudicated ground truth.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

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

An MRMC study was not mentioned or indicated in the document. The Ebisu Ultrasound system itself is a diagnostic imaging device, not an AI-assisted diagnostic software.

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

A standalone algorithm performance study was not mentioned or indicated in the document. The device is an ultrasound system that produces images for human interpretation, not an automated diagnostic algorithm operating without human intervention.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

The concept of "ground truth" as it applies to performance studies for AI devices is not directly applicable here. The "truth" for this 510(k) submission relates to the device's ability to produce images and measurements comparable to predicate devices and to meet safety standards. Its effectiveness is based on its conventional diagnostic imaging capabilities, not on a specific diagnostic output requiring a ground truth for validation.

8. The sample size for the training set

This information is not applicable/not provided. The Ebisu is a hardware and software system for generating ultrasound images, not an AI model that undergoes a "training" phase in the described context. Its design and development would involve engineering benchmarks and testing, not machine learning training sets.

9. How the ground truth for the training set was established

This information is not applicable/not provided for the same reasons as point 8.

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