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The device is a diagnostic imaging system that combines Positron Emission Tomography (PET) and X-ray Computed Tomography (CT) systems. The CT component produces cross-sectional images of the body by computer reconstruction of X-ray transmission data. The PET component images the distribution of PET radiopharmaceuticals in the patient body. The PET component utilizes CT images for attenuation correction and anatomical reference in the fused PET and CT images.

This device is to be used by a trained health care professional to gather metabolic and functional information from the distribution of the radiopharmaceutical in the body for the assessment of metabolic and physiologic functions. This information can assist in the evaluation, detection, diagnosis, therapeutic planning and therapeutic outcome assessment of (but not limited to) cancer, cardiovascular disease and brain dysfunction. Additionally, this device can be operated independently as a whole body multi-slice CT scanner.

Cartesion Prime, PCD-1000A, system combines a high-end CT and a high-throughput PET designed to acquire CT, PET and fusion images. The high-end CT system is a multi-slice helical CT scanner with a gantry aperture of 780 mm and a maximum scanning field of 700 mm. The high-throughput PET system has a digital PET detector utilizing SiPM sensors with temporal resolution of 280 ps. Cartesion Prime, PCD-1000A is intended to acquire PET images of any desired region of the whole body and CT images of the same region (to be used for attenuation correction or image fusion), to detect the location of positron emitting radiopharmaceuticals in the obtained images. This device is used to gather the metabolic and functional information from the distribution of radiopharmaceuticals in the body for the assessment of metabolic and physiologic functions. This information can assist research, diagnosis, therapeutic planning, and therapeutic outcome assessment. This device can also function independently as a whole body multi-slice CT scanner.

The provided text does not contain acceptance criteria or a study proving that an AI/algorithm-based device meets specific acceptance criteria.

The document is a 510(k) summary for a PET/CT imaging system (Cartesion Prime, PCD-1000A). It describes the device, its intended use, and argues for its substantial equivalence to previously cleared predicate devices. While it mentions "testing" and "risk analysis and verification/validation testing," this largely refers to standard engineering and performance testing of the imaging hardware and software, rather than a clinical study evaluating an AI/algorithm's diagnostic performance against established criteria and ground truth.

Specifically, the document focuses on:

• Device Description: The physical and functional characteristics of the PET/CT scanner.
• Indications for Use: What the device is intended for (diagnostic imaging, assessment of metabolic/physiologic functions, aiding in evaluation, diagnosis, therapeutic planning, outcome assessment for various diseases).
• Substantial Equivalence: A comparison of the subject device's technical specifications (PET sensitivity, timing resolution, CT detector, etc.) with predicate devices to demonstrate it performs similarly.
• Safety and Standards Compliance: Adherence to quality systems regulations (ISO 13485, 21 CFR 820) and various IEC/NEMA standards, as well as general statements about software documentation and cybersecurity.
• Testing (General): "Risk analysis and verification/validation testing conducted through bench testing" and that "PET image quality metrics were performed which validated that the subject device met established specifications for spatial resolution, sensitivity, NECR, energy/timing resolution and PET/CT alignment." This is about the scanner's image quality and technical performance, not an AI's diagnostic accuracy.

There is no mention of:

• An AI/algorithm component requiring specific performance evaluation.
• Diagnostic performance metrics (e.g., sensitivity, specificity, AUC) for an AI.
• Human readers, MRMC studies, or human-in-the-loop performance.
• Ground truth establishment methods for a diagnostic algorithm.
• Training or test sets for an AI.

Therefore, I cannot fulfill the request to describe the acceptance criteria and the study that proves an AI/algorithm device meets those criteria based on the provided text. The document is about a medical imaging device (a PET/CT scanner), not an AI/algorithm that performs diagnostic tasks.

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The Low Dose CT Lung Cancer Screening Option for Canon/Toshiba CT systems is indicated for using low dose CT for lung cancer screening. The screening must be conducted with the established program criteria and protocols that have been approved and published by a governmental body, a professional medical society and/or Canon.

Information from professional societies related to lung cancer screening can be found, but is not limited to: American College of Radiology® (ACR)-resources and technical specification; accreditation American Association of Physicists in Medicine (AAPM) - Lung Cancer Screening Protocols; radiation management.

The low dose lung cancer screening option is an indication being added to the following existing, previously FDA-cleared scanners: [List of Aquilion and Lightning CT scanner models and their corresponding 510(k) numbers]. No functional, performance, feature, or design changes are being made to the devices that will be indicated for low dose lung cancer screening. The devices already include low dose lung screening protocols, intended for use in the review of thoracic CT images within the established inclusion criteria of programs/protocols that have been approved and published by either a governmental body or professional medical society.

The provided text describes a 510(k) premarket notification for a "Low Dose CT Lung Cancer Screening Option" from Canon Medical Systems Corporation. The submission seeks to add this indication to existing, previously FDA-cleared CT scanners. The key claim is substantial equivalence to a predicate device (Aquilion RXL, K121553, which is a successor to the Aquilion 16 used in the National Lung Screening Trial - NLST). The device's performance is demonstrated through bench testing only, not a clinical study involving human subjects or AI-assisted readings.

Therefore, the following information can be extracted/inferred:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document states that performance was "substantially equivalent" to the predicate. Specific numerical values for the reported performance are not provided in this regulatory summary.

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

• Test Set Sample Size: Not applicable in the traditional sense of a clinical test set with patient data. The "test set" consists of bench testing data from representative scanners from different CT system families. One device from each of the three identified families (Aquilion 16/32/64/RXL, PRIME/PRIME SP, ONE/ViSION/Genesis, and Lightning) was used for bench testing.
• Data Provenance: The data is from bench testing performed by Canon Medical Systems Corporation. The document does not specify the country of origin for this bench testing data, but the manufacturer is Canon Medical Systems Corporation (Japan) with a U.S. agent. The original NLST data (which the predicate is compared against) was from a large-scale, prospective clinical trial conducted in the United States.

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

Not applicable. This submission relies on bench testing for substantial equivalence, not a clinical study requiring expert ground truth for image interpretation.

4. Adjudication Method for the Test Set

Not applicable, as no human readers or clinical image interpretation were part of the presented performance data.

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

No. This submission is for a CT scanner's indication for low-dose lung cancer screening, not an AI-powered diagnostic assist device. The performance demonstration is based on the physical imaging characteristics of the CT system.

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

Not applicable. This is for a CT imaging device, not a standalone algorithm.

7. The Type of Ground Truth Used

The "ground truth" for this substantial equivalence argument is the performance of the predicate device (Aquilion RXL), which is stated to have similar technological characteristics and performance equivalent to the Aquilion 16 used in the NLST. The "ground truth" for the benefit of low-dose CT lung cancer screening itself comes from clinical literature, specifically referencing the National Lung Screening Trial (NLST) results, which demonstrated reduced mortality from lung cancer with low-dose CT screening. However, the device's performance itself is measured against established phantom-based image quality metrics.

8. The Sample Size for the Training Set

Not applicable. This is a CT imaging device, not an AI/ML algorithm that requires a training set of data.

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

Not applicable.

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