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The Spectrum Dynamics Medical's VERITON™ CT is intended for use by appropriately trained health care professionals to aid in detecting, localizing, diagnosing, staging of lesions, disease and organ function for the evaluation of diseases and disorders such as, but not limited to, cardiovascular disease, neurological disorders and trauma. The system output can be used for planning, guiding, and monitoring therapy.

SPECT: The SPECT component is intended to detect or image the distribution of radionuclides in the body or organ (physiology), using the following techniques; whole body and tomographic imaging.

CT: The CT component is intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data (anatomy) from either the same axial plane taken at different angles or spiral planes take at different angles. The CT part is indicated for pediatric and adult patients.

SPECT+CT: The SPECT and CT components used together acquire SPECT/CT images can be corrected for attenuation with the CT images, and can be combined (image registration) to merge the patient's physiological (SPECT) and anatomical (CT) images.

VERITON® CT consists of Single Photon Emission Computed Tomography (SPECT) scanners and integrated X-Ray Computed Tomography (CT). The SPECT subsystem images and measures the distribution of radiopharmaceuticals in humans for the purpose of determining various metabolic (molecular) and physiologic functions within the human body and integrates the CT's anatomical details for precise reference of the location of the metabolic activity. The CT component produces cross-sectional images of the body by computer reconstruction of X-Ray transmission data from either the same axial plane taken at different angles or spiral planes taken at different angles. The system can be used as an integrated SPECT and CT modality while also enabling independent functionality of SPECT and CT as stand- alone diagnostic imaqing devices.

The provided text describes the VERITON™ CT whole body SPECT/CT system, a medical imaging device. The document focuses on establishing substantial equivalence to a predicate device rather than detailing an AI/machine learning component. As such, information regarding AI-specific criteria, human reader improvement with AI, standalone algorithm performance, or ground truth establishment for AI training/testing is not present.

However, I can extract information related to the performance testing and clinical evaluation of the SPECT/CT system itself.

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

The document broadly states: "All testing has met the acceptance criteria for the proposed device." Specific quantitative acceptance criteria are not explicitly listed in a table format within the provided text. The areas evaluated included:

• Energy resolution
• Count rate linearity
• Uniformity
• System resolution
• Lesion detectability

2. Sample size used for the test set and the data provenance:

• Sample Size: Not specified for the clinical image evaluation. The text mentions "Sample clinical images."
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Number of Experts: One
• Qualifications: "a board-certified radiologist"

4. Adjudication method for the test set:

• Adjudication Method: Not applicable or not specified beyond a single expert's evaluation. There's no mention of a consensus or pluralistic adjudication process (e.g., 2+1, 3+1).

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:

• MRMC Study: No. The device is a SPECT/CT system, not an AI-powered diagnostic tool for aiding human readers. The clinical evaluation mentioned was to confirm diagnostic quality of the images produced by the device, not a comparative effectiveness study involving human readers with/without AI assistance.
• Effect Size: Not applicable.

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

• Standalone Performance: Not applicable in the context of an AI algorithm. The device itself is a standalone imaging system. The performance testing described (energy resolution, count rate linearity, etc.) assesses the standalone performance of the SPECT/CT system.

7. The type of ground truth used:

• Type of Ground Truth: For the clinical evaluation, the "diagnostic quality" of images was confirmed by a board-certified radiologist. This can be interpreted as expert opinion/consensus (from a single expert in this case) on the diagnostic utility of the images produced by the device, rather than a definitive "ground truth" for specific pathology or outcomes.
  For the non-clinical performance evaluations (e.g., energy resolution, uniformity), the ground truth would be based on established physics principles and industry standards, often using phantoms.

8. The sample size for the training set:

• Sample Size for Training Set: Not applicable. The document does not describe an AI/machine learning component that would require a distinct training set. The device is a traditional medical imaging system.

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

• Ground Truth for Training Set: Not applicable, as there is no described AI/machine learning training set.

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The Spectrum Dynamics Medical's VERITON™ CT is intended for use by appropriately trained health care professionals to aid in detecting, localizing, diagnosing, staging of lesions, disease and organ function for the evaluation of diseases and disorders such as, but not limited to, cardiovascular disease, neurological disorders and trauma. The system output can be used for planning, guiding, and monitoring therapy.

SPECT: The SPECT component is intended to detect or image the distribution of radionuclides in the body or organ (physiology), using the following techniques; whole body and tomographic imaging.

CT: The CT component is intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data (anatomy) from either the same axial plane taken at different angles or spiral planes take at different angles. The CT part is indicated for pediatric and adult patients.

SPECT+CT: The SPECT and CT components used together acquire SPECT/CT images can be corrected for attenuation with the CT images, and can be combined (image registration) to merge the patient's physiological (SPECT) and anatomical (CT) images.

VERITON™ CT consists of Single Photon Emission Computed Tomography (SPECT) scanners and integrated X-Ray Computed Tomography (CT). The SPECT subsystem images and measures the distribution of radiopharmaceuticals in humans for the purpose of determining various metabolic (molecular) and physiologic functions within the human body and integrates the CT's anatomical details for precise reference of the location of the metabolic activity. The CT component produces cross-sectional images of the body by computer reconstruction of X-Ray transmission data from either the same axial plane taken at different angles or spiral planes taken at different angles. The system can be used as an integrated SPECT and CT modality while also enabling independent functionality of SPECT and CT as stand- alone diagnostic imaging devices.

The provided text describes the VERITON™ CT whole body SPECT/CT system, a medical imaging device. However, it does not contain detailed information about specific acceptance criteria for a study, nor does it present a detailed study proving the device meets those criteria in the format requested.

The document discusses "Performance testing" and "Summary of Non-Clinical Testing" which state that "All testing has met the acceptance criteria for the proposed device." It also mentions a "Summary of Clinical Testing" where a radiologist evaluated sample images for diagnostic quality.

Based on the information provided, I can only generate a partial answer, focusing on the available details and explicitly stating when information is missing.

Here's the breakdown of what can be extracted:

1. A table of acceptance criteria and the reported device performance
   • The document states that "All testing has met the acceptance criteria for the proposed device" for areas like energy resolution, count rate linearity, uniformity, system resolution, and lesion detectability. However, the specific numerical or descriptive acceptance criteria and the 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)

   • Sample Size: Not specified for non-clinical testing. For clinical testing, it states "Sample clinical images" which implies an unspecified number, likely small, and not a statistically defined test set.
   • Data Provenance: Not specified.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

   • Number of Experts: "a board-certified radiologist" (one expert).
   • Qualifications: "board-certified radiologist." Specific experience (e.g., "10 years of experience") is not mentioned.

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

   • Adjudication Method: "None" or not applicable, as only one radiologist was used for evaluation.

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

   • MRMC Study: No, there is no indication of an MRMC comparative effectiveness study involving human readers with and without AI assistance. This device is an imaging system, not explicitly a standalone AI diagnostic tool.

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

   • Standalone Performance: The document describes the device (VERITON™ CT system) performance and capabilities. It doesn't detail an "algorithm only" performance separate from the integrated system. The clinical "evaluation" involved a human radiologist reviewing images produced by the system.

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

   • Ground Truth: For clinical evaluation, the ground truth was established by a "board-certified radiologist" confirming "diagnostic quality." This points to expert opinion/evaluation rather than a definitive "ground truth" like pathology for specific disease detection accuracy. For non-clinical testing, phantoms were used, but the specific "ground truth" for e.g. "lesion detectability" in phantoms is inherent to the phantom design.

8. The sample size for the training set

   • Training Set Sample Size: Not applicable/not specified. The document describes a medical imaging device, not a machine learning model that would typically have a separate training set.

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

   • Training Set Ground Truth Establishment: Not applicable, as there's no mention of a traditional "training set" for a machine learning model.

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