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The Dilon 2000 Digital Gamma Camera is intended to be used to measure and image the distribution of selected single photon emitting radioisotopes in the human body. The resulting images are intended to be reviewed by qualified medical personnel.

To detect or image the distribution of radionuclides in the body or organ, using the following technique(s).
A. Planar Imaging
Energy Range (keV): 100 to 200

The Dilon 2000 is a high resolution, small field of view, portable gamma camera designed for general use in imaging radio pharmaceuticals.

The primary components of the Dilon 2000 are:

Detector Head: contains a two dimensional array of scintillation crystals, an array of position sensitive photomultiplier tubes and signal amplification and discrimination electronics.

Gantry Arm: safely supports the detector as positioned by the technologist operating the camera. Counter balancing is provided to hold the detector in its last position.

Mobile Cabinet: Contains data acquisition electronics, image development computer and Segami Pegasus™ imaging software. The cabinet also contains power filtering, isolation and a high voltage power supply.

This submission is for a Dilon 2000 Gamma Camera. The document does not describe a study to prove the device meets acceptance criteria, but rather states that "Images and performance testing data were collected on a prototype camera. Image quality and camera usage is equivalent to that of the predicate devices."

Therefore, I cannot provide all the requested information, particularly regarding specific acceptance criteria metrics, sample sizes, expert qualifications, or comparative effectiveness studies, as these details are not present in the provided text.

Here's a summary of what can be extracted from the document:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not specify quantitative acceptance criteria or provide a table of performance metrics. It generally states: "Image quality and camera usage is equivalent to that of the predicate devices."

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

Not specified in the provided text. The document only mentions "Images and performance testing data were collected on a prototype camera."

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

Not specified in the provided text.

4. Adjudication Method for the Test Set:

Not specified in the provided text.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

No, an MRMC comparative effectiveness study is not mentioned. The document focuses on demonstrating substantial equivalence to predicate devices, not on comparing human reader performance with and without AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done:

This device is a gamma camera, not an AI algorithm. Therefore, the concept of "standalone (algorithm only)" performance as typically understood for AI-powered devices does not apply in this context. The device's performance inherently involves image acquisition, processing (by the system's software, Segami Pegasus™ imaging software), and then review by qualified medical personnel.

7. The Type of Ground Truth Used:

Not explicitly stated. Given that the device images the distribution of radioisotopes, the ground truth would typically be established through the physical properties of the radioisotopes and their known distribution patterns, potentially corroborated by other diagnostic methods, but this is not detailed in the provided text.

8. The Sample Size for the Training Set:

Not applicable or specified. This device is a gamma camera, not a machine learning algorithm that requires a separate training set in the conventional sense. The "training" here would relate to the design and calibration of the camera's components and software based on established physics and medical imaging principles.

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

Not applicable or specified, as this is a hardware device with associated software, not a machine learning model requiring a ground-truthed training set.

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