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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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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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