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This device will be used in the same manner as all three dimensional imaging products are used, similar to the Voxel System. It can be used with any two and three dimensional spinning image data such as MRI, CAT, DSA and Ultrasound produced data and converts the data into complete and real 3D images in rotational and static fashion. The device is used to aid the surgeon by permitting more precise morphological diagnosis especially in aneurysms, atherosclerotic lesions and bile duct carcinomas due to the improved view of the data. It may also facilitate highly selective interventional procedures for vascular surgeons because of the improved three dimensional vascular road map the physician sees. The system does not take the place of the physician it is only a tool to assist the physician in his diagnosis, it does not make the diagnosis for him. Decisions about what to do and how to perform a procedure rest firmly with the physician.

This device is used primarily by physicians and health care workers, and is normally used under the supervision of a physician. It is not sold to the general public but may be available as a teaching tool to Universities and Medical Schools without the requirement of physician supervision.

The DynaVue 3-D spinning Image System (3DSI) takes a stream of images (frames) and as a first step duplicates the frames and pairs them by delaying one image stream. This is done by the 3DSI's field delay circuit.

With the paired image, the system has now created a parallax. In order to see a 3-D image the system creates a sequence of left or right images on the screen. After the paired images are coded " left" and " right", the information is transmitted to a controller/emitter. The emitter converts the left and right information into an infrared signal which activates the left or right shutter. The emitted 3-D images are observed with active viewing eyewear.

The system can also be used with an active monitor screen, the same principles as above apply except that the infrared emitter is not needed and the active viewing evewear is replaced by passive polarizing glasses.

The provided text is a 510(k) summary for the Dyna Vue 3-D spinning Image System (3DSI). It describes the device's function, intended use, and claims of substantial equivalence to predicate devices (Voxel System and Zeiss Endolive System). However, it does not contain information about specific acceptance criteria, performance studies, sample sizes, expert qualifications, or ground truth establishment.

Therefore, I cannot populate the table or answer most of the questions as the information is not present in the provided document.

Here's a breakdown of what can be extracted and what is missing:

Information Present:

• Device Name: Dyna Vue 3-D spinning Image System (3DSI)
• Intended Use: As an accessory to MRI's, CAT Scans, DSA and Ultrasound systems to permit viewing of data in true 3-D. It is used to aid the physician in visualizing anatomy, seeing tumors and other ailments, facilitating morphological diagnosis (aneurysms, atherosclerotic lesions, bile duct carcinomas), and guiding interventional procedures. It is a tool to assist, not replace, the physician in diagnosis and procedure decisions.
• Predicate Devices: Voxel System (Voxel Digital Holography), Zeiss Endolive System (3 D Endoscope).
• Claimed Substantial Equivalence: To Voxel System for 3-D imaging and aid to visualization; to Zeiss Endolive for the method of producing the 3-D image (shuttering effect and glasses).
• Mechanism of Action: Takes a stream of images, duplicates and pairs them by delaying one image stream to create parallax. Codes images as "left" and "right" and transmits to a controller/emitter (infrared signal for active eyewear) or directly to an active monitor for passive polarizing glasses.

Missing Information (Crucial for Acceptance Criteria and Study Details):

• Specific Acceptance Criteria: The document discusses the functionality and intended use of the device but does not define quantifiable performance metrics (e.g., accuracy, resolution, speed, error rates) that would constitute "acceptance criteria."
• Reported Device Performance: No performance data, test results, or metrics are provided.
• Sample Size for Test Set: Not mentioned.
• Data Provenance (Test Set): Not mentioned.
• Number of Experts for Ground Truth (Test Set): Not mentioned.
• Qualifications of Experts: Not mentioned.
• Adjudication Method (Test Set): Not mentioned.
• Multi Reader Multi Case (MRMC) Comparative Effectiveness Study: Not mentioned.
• Effect size of human readers with AI vs. without AI assistance: Not applicable as no MRMC study or AI-assistance is described. The device is a 3D visualization tool, not an AI diagnostic aid in the modern sense.
• Standalone Performance (Algorithm Only): Not applicable as the device is for human visualization.
• Type of Ground Truth Used: Not mentioned.
• Sample Size for Training Set: Not mentioned (likely not applicable as it's a visualization system, not a machine learning model that requires a training set in the typical sense).
• How Ground Truth for Training Set was Established: Not applicable.

Summary of Available Information:

Given the limitations, here's what can be provided:

1. A table of acceptance criteria and the reported device performance:

   Acceptance Criteria	Reported Device Performance
   Not explicitly stated in the document.	No performance data reported.
   The general implication is that the device must be able to:
   - Convert 2D/3D imaging data (MRI, CAT, DSA, Ultrasound) into true 3D images.	This functionality is described as how it works, but no performance metrics are given.
   - Display these 3D images in rotational and static fashion.	This functionality is described, but no performance metrics are given.
   - Aid physicians in visualizing anatomy, tumors, and ailments.	Implied by substantial equivalence to predicate device, but no study results are presented.
   - Be substantially equivalent to the Voxel System and the method of 3D projection of the Zeiss Endolive system.	A qualitative claim of substantial equivalence is made based on intended use and projection method.

2. Sample size used for the test set and the data provenance: Not provided.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not provided.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not provided.

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 mentioned. The device is a 3D visualization tool, not an AI-assisted diagnostic system.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The device is a visualization system for human use, so "standalone" performance in the sense of an AI algorithm is not applicable. Its entire purpose is to be "human-in-the-loop."

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not provided. This document claims substantial equivalence based on intended use and mechanism, not on direct performance validation against a ground truth.

8. The sample size for the training set: Not applicable based on the description of the device's technology. It's a hardware-based visualization tool, not a machine learning algorithm requiring a training set in the modern sense.

9. How the ground truth for the training set was established: Not applicable.

Conclusion:

The provided 510(k) summary focuses on describing the device's technology, its intended use, and its similarities to legally marketed predicate devices to establish substantial equivalence. It does not include the detailed performance study results, acceptance criteria, or ground truth methodology that might be expected for newer, AI-driven medical devices. The regulatory framework for this device (1997-1998) would have placed more emphasis on demonstrating safety and efficacy through substantial equivalence to existing technologies, rather than requiring extensive quantitative performance studies as might be seen today for novel diagnostic algorithms.

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