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The Dilon 6800 Acella 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.

The Dilon 6800 Acella (Acella) is a modification to the Dilon 2000 (now known as the Dilon 6800), a high resolution, small field of view, portable gamma camera designed for general use in imaging radio pharmaceuticals. The Acella has a larger field-of-view than the Dilon 6800 and replaces photomultiplier tubes with photodiodes. Both technologies convert visible light photons generated by scintillation crystals into electronic signals.

The provided text describes the Dilon 6800 Acella Scintillation Camera, a modification of the Dilon 6800. It focuses on the device's substantial equivalence to its predicate device and the new detector technology. However, the document does not contain the detailed information necessary to fully answer all aspects of your request regarding acceptance criteria and a study proving those criteria.

Specifically, the document lacks:

• A table of acceptance criteria and reported device performance.
• Sample sizes for test sets and data provenance.
• Number and qualifications of experts for ground truth.
• Adjudication methods.
• Any mention of a multi-reader multi-case (MRMC) comparative effectiveness study or human-in-the-loop performance.
• Details on standalone algorithm performance.
• Specific types of ground truth used (beyond general "performance requirements").
• Sample size for the training set.
• How ground truth for the training set was established.

The text primarily summarizes the technical changes and confirms that the device meets "predetermined success criteria according to established protocols" without providing the specific criteria or study details.

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

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

• Acceptance Criteria: Not explicitly detailed in a table. The document states that "Testing has demonstrated that the Acella, with the larger Field of View, has met predetermined success criteria according to established protocols." This implies criteria exist but are not presented.
• Reported Device Performance: Not explicitly detailed in a table. The document states the performance is "equivalent to the Dilon 6800 camera and the predicate detector technology." No specific metrics (e.g., sensitivity, specificity, resolution, image quality scores) are provided.

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

• Not provided. The text only mentions "Performance testing" and "Verification testing" without specifying sample sizes or data provenance.

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 provided. The text states that "The resulting images are intended to be reviewed by qualified medical personnel," but this refers to clinical use, not the ground truth establishment for testing.

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 applicable / Not provided. This device is a gamma camera (hardware), not an AI-assisted diagnostic software. There is no mention of AI or human-in-the-loop performance.

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

• Applicability is limited by device type. As a hardware device (gamma camera), "standalone algorithm performance" in the context of AI is not relevant. The performance refers to the imaging capabilities of the camera itself. While tests were done as a "standalone" device (without human interpretation being part of the device's direct output), no specific metrics are given.

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

• Not explicitly stated. The document refers to "predetermined success criteria" and "established protocols" for performance, which likely involve phantom studies or comparison with existing validated imaging systems for physical performance metrics (like resolution, uniformity, sensitivity). It does not suggest ground truth based on pathology or clinical outcomes for diagnostic accuracy validation in the way an AI diagnostic tool might.

8. The sample size for the training set

• Not applicable / Not provided. This device is a gamma camera. The concept of a "training set" is generally associated with machine learning or AI models, which is not what this device is. Its development involves engineering and hardware performance testing, not algorithmic training on data.

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

• Not applicable / Not provided. (See point 8)

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