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This device is indicated to acquire and display cross-sectional volumes of the whole body, to include the head.

The Aquilion Prime SP has the capability to provide volume sets. These volume sets can be used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician.

AiCE (Advanced Intelligent Clear-IQ Engine) is a noise reduction that improves image quality and reduces image noise by employing Deep Convolutional Network methods for abdomen, pelvis, lung, cardiac, extremities, head and inner ear applications.

The Aquilion Prime SP (TSX-303B/8) V10.2 with AiCE-i is a 40-row CT System that is intended to acquire and display cross-sectional volumes of the whole body, including the head. An optional Dynamic Volume CT Upgrade Kit can be installed in order to expand the active detector rows to 80. This system is based upon the technology and materials of previously marketed Canon CT systems. In addition, the subject device incorporates the latest reconstruction technology, AiCE-i (Advanced intelligent Clear-IQ Engine - integrated), intended to reduce image noise and improve image quality by utilizing Deep Convolutional Network methods. These methods can more fully explore the statistical properties of the signal and noise. By learning to differentiate structure from noise, the algorithm produces fast, high quality CT reconstruction.

The Aquilion Prime SP (TSX-303B/8) V10.2 with AiCE-i is a CT x-ray system, and information on its acceptance criteria and the study proving it is provided below:

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

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

  The document does not explicitly state a sample size for patient data. The testing primarily relies on phantom studies and a review of diagnostic images.

  • Data Provenance: Not specified for any potential patient data. The document mentions "Representative abdomen/pelvis, lung, cardiac, brain, extremities and inner ear diagnostic images" were obtained using the subject device, suggesting prospective acquisition for evaluation, but this is not definitively stated.

• 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)

  For the assessment of diagnostic image quality:

  • Number of experts: One
  • Qualifications of experts: An "American Board Certified Radiologist." Further details (e.g., years of experience) are not provided.

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

  Based on the mention of a single American Board Certified Radiologist reviewing images, there was no multi-reader adjudication method (e.g., 2+1, 3+1) described for the diagnostic quality assessment. It appears to be a solitary expert's assessment.

• 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 multi-reader multi-case (MRMC) comparative effectiveness study focusing on human reader improvement with or without AI assistance is described in the provided text. The study focused on assessing image quality and specific performance metrics of the AI-integrated system (AiCE-i) through phantom studies and expert review of diagnostic images.

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

  Yes, standalone testing of the algorithm (AiCE-i) was a primary component of the study. This is evidenced by:

  • The extensive use of phantom studies to assess various image quality metrics (CNR, CT Number Accuracy, Uniformity, SSP, MTF, SD, NPS, LCD, etc.). These are objective measurements of the algorithm's output.
  • Direct claims stating quantitative dose reduction (82%) and noise reduction (21.4%) by the AiCE algorithm relative to other reconstruction methods.
  • Claims about improved low contrast detectability (13.8%) by the algorithm.
  • Analysis of NPS and kurtosis values to assess noise appearance/texture of the algorithm's output.

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

  The ground truth used for the evaluation can be categorized as follows:

  • Objective Phantom Measurements: For quantitative image quality metrics like dose reduction, noise reduction, CNR, MTF, NPS, and LCD. The "ground truth" here is the physical properties of the phantom and the established measurement methodologies.
  • Expert Radiologist Review: For confirming the "diagnostic quality" of the AiCE reconstructed images. The "ground truth" in this context is the qualitative assessment and professional judgment of the American Board Certified Radiologist.

• 8. The sample size for the training set

  The document does not provide details on the sample size or characteristics of the training data used for the Deep Convolutional Network methods employed by AiCE-i.

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

  The document does not provide details on how the ground truth for the training set was established for the Deep Convolutional Network methods used by AiCE-i.

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This device is indicated to acquire and display cross-sectional volumes of the whole body, to include the head.

The Aquilion Prime SP has the capability to provide volume sets. These volume sets can be used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician.

The Aquilion Prime SP TSX-303B/1 is an 80-row CT System that is intended to acquire and display cross-sectional volumes of the whole body, including the head. This system is based upon the technology and materials of previously marketed Toshiba CT systems.

The provided text describes a 510(k) submission for the Toshiba Aquilion Prime SP, TSX-303B/1, v8.4. It outlines modifications to a previously cleared CT system. While the document mentions various performance evaluations and studies, it does not contain specific acceptance criteria tables nor detailed study designs that definitively "prove" the device meets acceptance criteria in the format of a typical peer-reviewed clinical study. Instead, it focuses on demonstrating substantial equivalence to a predicate device through engineering and performance testing.

However, I can extract and infer information about the testing and performance as described in the document.

Missing Information:

• A clear table of acceptance criteria for specific performance metrics. The document describes improvements but doesn't explicitly state "acceptance criteria" values met.
• Detailed sample sizes for all tests.
• Specific data provenance for all tests (e.g., country of origin, retrospective/prospective).
• Number and qualifications of experts for all ground truth establishment.
• Adjudication methods.
• MRMC comparative effectiveness study details (effect size of human reader improvement with AI).
• Standalone algorithm performance (the device is a CT system, not an algorithm in the AI sense).
• Sample size for the training set.
• How ground truth for the training set was established.

Based on the provided text, here's what can be extracted and inferred:

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

The document does not explicitly present a table of acceptance criteria. Instead, it describes performance improvements and that the modified system "demonstrates equivalent or slightly improved image quality characteristics." The performance evaluations are primarily focused on physical parameters and dose reduction, not diagnostic accuracy in the way an AI algorithm might be assessed against clinical endpoints.

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

• Sample Size for Physical Performance Tests: Not explicitly stated. The tests involved "model observer studies" using MITA-FDA LCD Head and MITA-FDA LCD Body phantoms, implying a phantom-based test set rather than patient data.
• Sample Size for Image Review: "Representative diagnostic images" were obtained. The exact number is not specified.
• Data Provenance: Not specified. Phantoms for performance tests. Clinical images for diagnostic quality assessment (implicitly from a clinical setting, but no country of origin or retrospective/prospective status is mentioned).

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 of Expert: An "American Board Certified Radiologist." Further details on experience (e.g., years) are not provided.
• Role: This radiologist "reviewed" the "representative diagnostic images" to confirm they were of "diagnostic quality."

4. Adjudication method for the test set

• Adjudication Method: Not applicable or not specified in detail. The document states a single American Board Certified Radiologist reviewed images. There is no mention of consensus or multi-reader adjudication for this informal review of diagnostic quality. For the quantitative performance metrics (dose reduction, LCD, noise), these were based on phantom studies and model observer analysis, not human reader adjudication.

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 document does not describe a MRMC comparative effectiveness study. This submission is for a CT system itself, not an AI-assisted diagnostic tool in the typical sense of showing improved human reader performance. The "AI" mentioned (AIDR 3D Enhanced, SEMAR) refers to image processing algorithms within the CT system to improve image quality or reduce artifacts, not a separate AI application for diagnosis or interpretation assistance that would warrant an MRMC study comparing human readers with and without its aid.

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

• Standalone Performance: Yes, in a way. The "performance testing" of the modified system, including spatial resolution, CT number, noise properties, LCD, and CNR, as well as the quantitative dose reduction and LCD/noise improvement studies using phantoms and model observers, represent a standalone evaluation of the system's technical image quality parameters. These are inherent algorithmic and hardware performance metrics of the CT scanner, not dependent on human interpretation for their measurement.

7. The type of ground truth used

• For Quantitative Performance: Model observer studies using MITA-FDA LCD Head and MITA-FDA LCD Body phantoms. These phantoms represent a controlled, objective ground truth for physical image quality parameters.
• For Diagnostic Quality: The subjective assessment of an "American Board Certified Radiologist" confirming images were of "diagnostic quality." This is expert opinion/consensus for a qualitative judgment rather than a definitive "ground truth" like pathology.

8. The sample size for the training set

• Training Set Sample Size: Not applicable / Not provided. This document describes a 510(k) submission for a CT scanner, not a machine learning algorithm that requires a "training set" in the conventional sense. While there might be internal development and validation data, it's not discussed as a distinct "training set" within this regulatory context.

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

• Ground Truth Establishment for Training Set: Not applicable / Not provided, as there is no described training set for an AI algorithm in the context of this submission.

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Optional movable gantry base unit for use with an Aquilion PRIME system to support longitudinal movement and allow acquisition of images in the z-direction (z-axis).

Note: When installed with the movable gantry base unit, Aquilion PRIME can be used with the INFX-8000C system in the same room.

The Infinix 4DCT is composed of the INFX-8000C interventional angiography system and the dynamic volume CT system, Aquilion PRIME. This combination enables patient access and efficient workflow for interventional procedures. CGBA-032A, Aquilion PRIME Self-Propelled Scan Base Kit for IVR-CT is an optional kit intended to be used in conjunction with an Aquilion PRIME / INFX-8000C based IVR-CT system. The subject device is attached to the CT gantry to support longitudinal movement and allow image acquisition in the z-direction (Z-axis), both axial and helical. When this option is selected, the standard CT patient couch is replaced with the patient handling system utilized by the interventional x-ray system, Toshiba INFX-8000C. The intended uses and technological characteristics of the Aquilion PRIME CT System and INFX-8000C Interventional X-Ray System with which this device is used, remain the same.

The provided document is a 510(k) premarket notification for a medical device called the "CGBA-032A, Aquilion™ PRIME Self-Propelled Scan Base Kit for IVR-CT." This notification is for a modification of an existing device and primarily addresses the physical and functional aspects of the new base unit for a CT system, rather than evaluating an AI-powered diagnostic or predictive algorithm.

Therefore, many of the requested criteria, such as acceptance criteria based on diagnostic performance metrics (e.g., sensitivity, specificity, AUC), sample sizes for test sets, ground truth establishment by experts, adjudication methods, MRMC studies, and standalone algorithm performance, are not applicable or not detailed in this type of submission. This submission focuses on engineering, safety, and functionality verification.

Here's an analysis based on the information available in the document:

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

The document does not provide a formal table of acceptance criteria and reported device performance in terms of diagnostic accuracy or clinical outcomes. The "acceptance criteria" discussed are related to the device's functional performance, safety, and substantial equivalence to a predicate device. The performance reported relates to the successful verification and validation of these functional aspects.

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

This submission does not involve clinical data or "test sets" in the context of diagnostic algorithm evaluation. The "testing" refers to non-clinical bench testing, verification, and validation of the hardware and software components. Therefore, information about sample size for test sets (e.g., number of patients/cases), data provenance, or retrospective/prospective studies is not relevant or provided.

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 applicable. As this is a submission for a mechanical/electronic device modification and not a diagnostic algorithm, there is no "ground truth" to be established by clinical experts for a test set.

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

Not applicable for the same reasons as above.

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. This device is an optional physical kit for a CT system, not an AI or diagnostic algorithm, so no MRMC study or AI-assistance evaluation was conducted.

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

Not applicable. This device is a hardware accessory (a self-propelled scan base kit), not a standalone algorithm.

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

Not applicable. The "ground truth" equivalent in this context would be the engineering specifications and safety standards against which the device's functional performance was verified.

8. The sample size for the training set

Not applicable. This document does not describe the development of a machine learning model, so there is no "training set."

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

Not applicable. As there is no training set, there's no ground truth for it.

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This device is indicated to acquire and display cross-sectional volumes of the whole body, to include the head.

The Aquilion Prime has the capability to provide volume sets. These volume sets can be used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician.

The Aquilion PRIME TSX-303A/A and /B, v6.00 are 80-row CT Systems and the TSX-303A/F, v6.00 is a 40-row CT system that is intended to produce axial scans of the whole body to include the head. These systems are based upon the technology and materials of previously marketed Toshiba CT systems.

This document is a 510(k) premarket notification for a Computed Tomography (CT) system, the Aquilion PRIME, v6.00. As such, it focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed study with specific acceptance criteria and performance metrics in the way one might expect for a novel AI-powered diagnostic device.

Therefore, the information regarding "acceptance criteria" and the "study that proves the device meets the acceptance criteria" is framed within the context of demonstrating equivalence and safety/effectiveness for a hardware/software update to an existing CT system, rather than a standalone performance study with clinical endpoints.

Here's an attempt to extract the closest available information based on your request, acknowledging that the format and detail for a conventional "acceptance criteria" study are not fully present in this type of submission.

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

Based on the document, the "acceptance criteria" are implied by demonstrating substantial equivalence to the predicate device and meeting regulatory standards for CT systems. The "reported device performance" is described in terms of improved imaging properties and diagnostic quality.

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

• Test Set Sample Size: Not explicitly stated as a number of patients or cases. The document mentions "representative diagnostic images" but does not quantify them.
• Data Provenance: Not specified. It's likely that the "representative clinical images" were obtained during internal testing or pilot sites, but no details on country or retrospective/prospective nature are provided.

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)

• Number of Experts: "an American Board Certified Radiologist" (singular).
• Qualifications of Experts: "American Board Certified Radiologist." No specific experience level (e.g., 10 years) is mentioned.

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

• Adjudication Method: "Reviewed by an American Board Certified Radiologist." This implies a single reader review, so no adjudication method (like 2+1 or 3+1) was used.

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. This submission is for a CT system itself, not an AI-assisted diagnostic tool designed to improve human reader performance. Its purpose is to demonstrate the fundamental image quality and safety of the CT scanner.

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

• This is not applicable in the context of this submission. The device is a CT scanner, which inherently produces images for human interpretation. The "software options" mentioned (SEMAR, SURESubtraction Ortho, Dual Energy System Package) are image processing algorithms that enhance the raw CT data, but the "performance" as described (image quality metrics, diagnostic quality) still relates to the final image presented for a human in the loop. There is no "algorithm only" performance study in the sense of an automated diagnostic algorithm.

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

• The "ground truth" for evaluating image quality appears to be based on:
  • Phantom measurements: For spatial resolution, CT number, contrast-to-noise ratio, and uniformity performance.
  • Expert opinion: The "American Board Certified Radiologist" reviewing representative diagnostic images for diagnostic quality. This functions as the human expert assessment indicating the images are fit for interpretation.

8. The sample size for the training set

• Not applicable/provided. This document describes a new version of an existing CT scanner, not a novel machine learning algorithm that requires a separate training set. The "software options" mentioned were previously cleared and their development (including any training data if applicable) would have been part of their original 510(k) submissions.

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

• Not applicable. See point 8.

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This device is indicated to acquire and display cross-sectional volumes of the whole body, to include the head.

The Aquilion PRIME has the capability to provide volume sets. These volume sets can be used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician.

The Aquilion PRIME TSX-303A/2, v5.00 is an 80-row CT System and the TSX-303A/6, v5.00 is a 40-row CT system that is intended to produce axial scans of the whole body to include the head. These systems are based upon the technology and materials of previously marketed Toshiba CT systems.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Toshiba Aquilion PRIME CT system (K130645):

The submission K130645 is for a modification of an already cleared device (Aquilion Prime CT System, K120710). As such, the purpose of the submission is to demonstrate that the modified device is substantially equivalent to the predicate device and that the modifications do not adversely affect its safety or effectiveness.

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't present a formal table of distinct "acceptance criteria" against which a device's performance is strictly measured in a pass/fail manner with specific quantitative thresholds. Instead, it describes a comparative evaluation against a predicate device and relies on an image quality metrics study using phantoms to show substantial equivalence.

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

• Sample Size: Not explicitly stated in terms of patient cases. The testing mentioned is an "image quality metrics study, utilizing phantoms." Phantoms are standardized test objects, not patient data.
• Data Provenance: Not applicable as patient data was not used for this specific substantial equivalence claim. The testing involved phantoms.

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

• Number of Experts/Qualifications: Not applicable. Ground truth for phantom-based image quality metrics is established by physical measurements and computed reference values within the phantom, not by expert interpretation.

4. Adjudication Method for the Test Set:

• Adjudication Method: Not applicable. The "image quality metrics study, utilizing phantoms" involves objective measurements (e.g., spatial resolution, CT number, CNR, noise properties) comparing the modified device to the predicate, rather than human interpretation requiring adjudication.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size:

• MRMC Study: No, an MRMC comparative effectiveness study was not performed or mentioned in this submission. This is not a study assessing human reader performance with or without AI assistance. It's a technical modification to a CT scanner.

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 algorithm's diagnostic performance. This submission is about a CT hardware and software system, not a diagnostic AI algorithm that operates standalone. The image quality metrics study is a technical performance assessment of the system.

7. The Type of Ground Truth Used:

• Ground Truth: For the "image quality metrics study, utilizing phantoms," the ground truth is established by the known physical properties of the phantoms and the expected scientific principles of CT imaging. This allows for objective quantification of spatial resolution, CT number accuracy, contrast-to-noise ratio, and noise properties.

8. The Sample Size for the Training Set:

• Training Set Sample Size: Not applicable. This document describes a modification to an existing CT system and its validation through phantom studies and adherence to standards. It does not refer to the development of a machine learning algorithm that would require a distinct "training set" of data.

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

• Ground Truth for Training Set: Not applicable, as there is no mention of a training set for a machine learning algorithm.

Summary of the Study:

The primary study mentioned to demonstrate that the device meets implied acceptance criteria is an "image quality metrics study, utilizing phantoms." This study compared the modified Aquilion PRIME CT system to its predicate device (TSX-302A/2, Aquilion Prime CT System) to demonstrate substantial equivalence in key image quality parameters.

Key Findings: The study validated that the subject device is substantially equivalent to the predicate device with regard to:

• Spatial resolution
• CT number
• Contrast-to-noise ratio (CNR)
• Noise properties

This type of technical study, along with bench testing, software validation, risk management, and compliance with applicable standards (IEC, NEMA, FDA), was considered sufficient by the manufacturer and the FDA to conclude that the modified device is safe and effective and substantially equivalent to the predicate.

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This device is indicated to acquire and display cross-sectional volumes of the whole body, to include the head.

The Aquilion PRIME has the capability to provide volume sets. These volume sets can be used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician.

The TSX-302A/2 is a 40-row CT System that is intended to produce axial scans of the whole body to include the head. The TSX-302A/2 is based upon the technology and materials of previously marketed Toshiba CT systems.

The provided text is a 510(k) premarket notification for a Computed Tomography (CT) system (TSX-302A/2, Aquilion Prime CT System). This document focuses on demonstrating substantial equivalence to a predicate device, rather than proving a device meets specific performance acceptance criteria through the kind of study described in the prompt.

Therefore, many of the requested details about acceptance criteria, specific performance metrics, sample sizes, expert involvement, and ground truth establishment are not present in this type of FDA submission.

Here's an explanation based on the available information:

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

• Not Applicable / Not Provided. A 510(k) submission for a CT scanner typically does not present quantitative performance acceptance criteria in the same way an AI/software device might. Instead, it focuses on demonstrating that the new device performs at least as safely and effectively as a legally marketed predicate device (substantial equivalence). Performance is generally shown through technical specifications (e.g., number of detector rows, dose reduction features, image reconstruction capabilities) being comparable to or improved over the predicate, rather than by meeting pre-defined statistical thresholds for diagnostic accuracy.

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. Since this is a hardware device focused on substantial equivalence to a predicate CT scanner, there's no mention of a "test set" of patient data for evaluating diagnostic performance in the way an AI algorithm would be tested. The review primarily concerns the technical design and capabilities of the scanner itself.

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. As there is no test set of patient cases described for diagnostic evaluation, there is no mention of experts or ground truth establishment.

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

• Not Provided. No test set, no adjudication method described.

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 CT scanner, not an AI software intended to assist human readers. Therefore, an MRMC study comparing human reader performance with and without AI assistance is not relevant to this submission.

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

• Not Applicable / Not Provided. This is a hardware CT scanner, not a standalone algorithm.

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

• Not Applicable / Not Provided. No ground truth is described for diagnostic performance evaluation. The "ground truth" for a CT scanner's performance would relate to its physical properties (e.g., spatial resolution, contrast resolution, noise, dose) which are assessed through engineering tests and phantom studies, not typically against medical "ground truth" in patient data for a 510(k) of this nature.

8. The sample size for the training set

• Not Applicable / Not Provided. This device is a CT scanner, not an AI model, so there is no "training set."

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

• Not Applicable / Not Provided. No training set, no ground truth establishment for it.

Summary from the document:

The 510(k) for the Toshiba TSX-302A/2, Aquilion Prime CT System (K120410, later corrected to K120710 in the FDA letter) demonstrates substantial equivalence to its predicate device, the Toshiba Aquilion PRIME TSX-302A (K110066, or potentially k10066 as referenced in one section).

• Device Description: The TSX-302A/2 is described as a 40-row CT System intended to produce axial scans of the whole body, including the head. It's based on the technology and materials of previously marketed Toshiba CT systems.
• Intended Use: "This device is indicated to acquire and display cross-sectional volumes of the whole body, to include the head. The Aquilion Prime has the capability to provide volume sets. These volume sets can then be used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician."
• Safety: The device is designed and manufactured under ISO-13485, meets 21 CFR 820 requirements, and conforms to appropriate IEC safety standards. Radiation safety adheres to 21 CFR 1020.

Conclusion:
This submission type (510(k) for a CT scanner) focuses on establishing "substantial equivalence" to a predicate device based on technical specifications, intended use, and safety standards, rather than proving performance against specific clinical acceptance criteria through a study involving patient data, expert reviews, and ground truth in the way an AI/software device would. Therefore, most of the detailed information requested about performance studies, sample sizes, and expert involvement is not present in this document.

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This device is indicated to acquire and display cross-sectional volumes of the whole body, to include the head.

The Aquilion PRIME has the capability to provide volume sets. These volume sets can be used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician.

The TSX-302A is an 80-row CT System that is intended to produce axial scans of the whole body to include the head. The TSX-302A is based upon the technology and materials of previously marketed Toshiba CT systems.

I am sorry, but the provided text does not contain any information about acceptance criteria, device performance, or any studies conducted to prove the device meets said criteria. The document is a 510(k) summary for the TSX-302A, Aquilion Prime CT System, detailing its intended use, substantial equivalence to a predicate device, and regulatory information from the FDA. It does not include any performance data or study details.

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