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The SMV TAC-2 accessory to the SMV dual head emission tomographic systems produces images of a patient's anatomy when performing cardiac or wholebody imaging in single photon mode, or in coincidence mode (using the VCR option) . The system is intended to provide an enhancement to the emission images by compensating for the attenuation effects of the human body.

The TAC-2 hardware consists of two sliding line source emitter block/collimator assemblies. Each assembly extends transaxially across the face of each detector of a DST series gamma camera oriented 180°. When in use each emitter block exposes the collimator in the opposing assembly. The assemblies travel in synchrony along the axial direction.

The provided text primarily focuses on the 510(k) summary for the TAC-2 device, its intended use, and substantial equivalence to predicate devices. It lacks the detailed information required to comprehensively answer the request regarding acceptance criteria and a study proving device performance against those criteria.

However, based on the limited information, here's what can be extracted and what is missing:

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

The document does not explicitly state formal acceptance criteria in a quantitative manner. The closest statement regarding performance is:

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. The text only mentions "Clinical and non-clinical studies were conducted."

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.

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

This information is not provided in the document. Given the device's nature (attenuation correction for gamma cameras), it's unlikely to be an AI-driven device in the modern sense that would involve human reader improvement with AI assistance. The focus is on image quality enhancement.

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

The device is an accessory that produces images to enhance emission images by correcting for photon attenuation. Its performance would be evaluated on the quality of these corrected images, which is a form of standalone performance in terms of image processing. The statement "Images produced were of the same quality as those provided by the manufacturers of the predicate devices" implies a standalone evaluation against a benchmark. However, details of this evaluation are not provided.

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

The document mentions "images that depict the anatomy of a patient" and "correcting for photon attenuation effects of the patient's anatomy." The implied ground truth would be the actual patient anatomy and the actual amount of attenuation, likely assessed through comparison with established imaging techniques or potentially physical phantoms for quantifiable metrics. However, the specific method for establishing ground truth is not provided.

8. The sample size for the training set

This information is not provided in the document.

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

This information is not provided in the document.

Summary of Study Information Provided:

• Study Purpose: Clinical and non-clinical studies were conducted to evaluate the TAC-2 attenuation correction device.
• Device Under Test: TAC-2 attenuation correction device with a DST-XL with the VCAR option.
• Outcome: "Images produced were of the same quality as those provided by the manufacturers of the predicate devices."
• Comparison: Implied comparison to images from predicate devices (90KPS TAC SMV and 90 KPS MCD-AC ADAC Laboratories).

Key Missing Information for a Comprehensive Answer:

The document is a 510(k) summary, which often provides high-level information for substantial equivalence. It lacks the detailed methodology, specific quantitative acceptance criteria, statistical analysis, and comprehensive results typically found in full study reports required to answer all sections of your request comprehensively.

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The VCAR option to the DST Gamma Camera and VISION Powerstation and the VCAR option to the DST-XL Gamma Camera and VISION Powerstation produce images which depict the anatomical distribution of radioisotopes within the human body, for interpretation by medical clinicians. The systems are intended for whole body nuclear imaging.

Indication for use of the VCR option to the DST and DST-XL cameras and Powerstation computer is an accessory upgrade intended to perform electronic collimation for coincidence imaging of positron-emitting radioisotopes in the human body.

The VCAR option to the DST Gamma Camera and VISION Powerstation (DST&PS/VCAR) and the VCAR option to the DST-XL Gamma Camera and VISION Powerstation are accessory upgrades to the previously cleared DST an DST-XL Gamma Cameras (K921008) and VISION Powerstation (K912753). This accessory enables the system to perform electronic collimation for positron emission tomography (PET) of radioisotopes within the human body. The DST&PS/VCAR and DST-XL&PS/VCAR utilize proprietary coincidence detection electronics and software to perform electronic collimation of gamma rays emitted from decay of radioisotopes in a patient. This process is called "Coincident Photon Emission Computed Tomography" (CPECT).

The DST&PS/VCAR and DST-XL&PS/VCAR allows the operator to acquire coincidence imaging data by controlling the detector electronics and processors in coincidence imaging mode. The DST&PS/VCAR and DST-XL&PS/VCAR also includes the control, processing, and display screens to perform reconstruction of 2-D and 3-D information from the acquired datasets.

Here's a breakdown of the acceptance criteria and study information for the SMV America DS-VCAR Option 510(k) (K972686), based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly state specific pass/fail acceptance criteria values for performance metrics. Instead, it makes a general claim of meeting "system performance specifications" and performing "in a fashion similar to predicate devices."

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

The document does not specify a sample size for a test set in terms of cases or patients. It mentions that "Major performance parameters have been measured using industry-standard test methods," implying laboratory or phantom-based testing rather than studies involving patient data for a test set. There is no information provided regarding data provenance (e.g., country of origin, retrospective or prospective) for any clinical or test data.

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

This information is not provided in the document. The testing described appears to be technical performance validation against specifications and predicate devices, not a clinical study requiring expert ground truth for interpretation.

4. Adjudication Method for the Test Set

This information is not applicable/not provided. The document describes technical performance testing rather than a study requiring adjudication of clinical interpretations.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

The document does not describe an MRMC comparative effectiveness study. There is no mention of human readers, AI assistance, or an effect size for human improvement. The focus is on the device's technical performance and equivalence to predicate devices.

6. Standalone Performance Study (Algorithm Only Without Human-in-the-Loop)

The document describes standalone performance in terms of the device's technical capabilities. The "Summary of Testing" states that "Major performance parameters have been measured using industry-standard test methods to determine that the device meets its system performance specifications and performs in a fashion similar to predicate devices." This implies testing of the device's imaging and reconstruction capabilities in isolation.

7. Type of Ground Truth Used

The ground truth for the testing appears to be based on industry-standard test methods and published specifications for gamma cameras and PET systems, rather than clinical ground truth like pathology or outcomes data. The comparisons are made against established performance characteristics of predicate devices.

8. Sample Size for the Training Set

The document does not mention a training set sample size. This is expected as the device predates modern machine learning/AI paradigms that typically rely on large training datasets. The "VCAR option" appears to be an accessory upgrade that utilizes coincidence detection electronics and software for image processing and reconstruction, not a deep learning model.

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

Since there is no mention of a training set in the context of machine learning, the question of how its ground truth was established is not applicable. The development and validation were "in accordance with the Company's product and software development procedures," implying traditional engineering and software verification methods.

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The VCAR option to the VISION FX-80 Gamma Camera and Power Station produces images which depict the anatomical distribution of radioisotopes within the human body, for interpretation by medical clinicians. The system is intended for whole body nuclear imaging.

The VCAR option to the VISION FX-80 Gamma Camera and Power Station (FX-80&PS/VCAR) is an accessory upgrade to the previously cleared FX-80 Gamma Camera (K935273) and Power Station (K912753). This accessory enables the system to perform electronic collimation for positron emission tomography (PET) of radioisotopes within the human body. The FX-80&PS/VCAR utilizes proprietary coincidence detection electronics and software to perform electronic collimation of gamma rays emitted from decay of radioisotopes in a patient. This process is called "Coincident Photon Emission Computed Tomography" (CPECT). The FX-80&PS/VCAR allows the operator to acquire coincidence imaging data by controlling the detector electronics and processors in coincidence imaging mode. The FX-80&PS/VCAR also includes the control, processing, and display screens to perform reconstruction of 2-D and 3-D information from the acquired datasets.

The provided text does not contain detailed acceptance criteria or a specific study proving the device meets those criteria with the requested level of detail. The information is a 510(k) summary, which focuses on demonstrating substantial equivalence to predicate devices rather than providing a comprehensive clinical study report with specific performance metrics and patient data.

Here's a breakdown of what can be extracted and what information is missing based on your questions:

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

The document states: "Major performance parameters have been measured using industry-standard test methods to determine that the device meets its system performance specifications and performs in a fashion similar to predicate devices." However, specific numerical acceptance criteria and the corresponding reported device performance values are not provided.

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 available in the provided text. The summary mentions "system testing and validation" but does not detail the nature of the test set (e.g., patient data, phantom data), its size, or its origin.

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 available. The document mentions that the device "produces images which depict the anatomical distribution of radioisotopes within the human body, for interpretation by medical clinicians," but does not describe any human expert review process for a test set.

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

This information is not available.

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

This type of study is not mentioned in the provided text. The device is described as an "accessory upgrade" that enables CPECT, not an AI-powered diagnostic aid that assists human readers.

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

The document suggests that the device performs imaging and reconstruction as a standalone system (algorithm only without human-in-the-loop performance for image generation), given that it "produces images which depict the anatomical distribution of radioisotopes." However, specific standalone performance metrics (e.g., sensitivity, specificity, accuracy) are not provided. The focus is on the technical capability to generate images for interpretation.

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

Given the nature of the device (a gamma camera system for nuclear imaging), the "ground truth" for its basic performance would likely involve phantom studies (measuring spatial resolution, sensitivity, uniformity with known radioisotope distributions) and comparison to established imaging techniques (e.g., existing PET systems). However, the document does not explicitly state the type of ground truth used. It implies that the images are for "interpretation by medical clinicians," suggesting a reliance on clinical judgment as the ultimate benchmark, but doesn't detail how this was assessed during testing.

8. The sample size for the training set

This information is not available. The document describes "proprietary coincidence detection electronics and software" and states the device was "developed and validated in accordance with the Company's product and software development procedures," but does not mention specific training sets or machine learning aspects that would typically involve a training set as understood today.

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

This information is not available, as a "training set" in the context of modern AI/ML development is not explicitly discussed.

In summary: The provided 510(k) summary focuses on the technological characteristics and substantial equivalence to predicate devices for a gamma camera accessory enabling CPECT. It asserts that "system testing and validation demonstrates that the system meets its published specifications, performs as well as or better than the currently marketed product, and is safe and effective for its intended use." However, it does not provide the granular details of acceptance criteria, specific performance metrics, study designs, sample sizes, or ground truth methodologies that your questions are looking for. This is typical for a 510(k) summary from 1997, where the emphasis was on engineering validation and equivalence rather than extensive clinical efficacy trials with detailed statistical reporting of performance against ground truth as might be expected for an AI/ML-driven device today.

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