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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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This device is indicated to acquire and display cross sectional volumes of the whole body, to include the head, with the capability to image whole organs in a single rotation. Whole organs include but are not limited to brain, heart, pancreas, etc.

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

Aquilion ONE (TSX-305A/3) V7.3 is a whole body multi-slice helical CT scanner, consisting of a gantry, couch and a console used for data processing and display. This device captures cross sectional volume data sets used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician. This system is 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 Aquilion ONE (TSX-305A/3) V7.3:

1. Table of Acceptance Criteria and Reported Device Performance:

The document is a 510(k) summary for a premarket notification for a Computed Tomography X-ray System. It is not a clinical study report with specific acceptance criteria directly tied to a diagnostic performance metric (like sensitivity or specificity) of a disease-detecting AI algorithm. Instead, it demonstrates substantial equivalence to a predicate device, focusing on technical specifications and image quality for general diagnostic use.

Therefore, the "acceptance criteria" here relate to demonstrating that the new device performs acceptably for its intended use and is equivalent to the predicate. The "performance" is primarily a comparison of technical specifications and image quality metrics against the predicate.

• View rate: Maximum 2910 views/s (same)
• Detector: 896 channels x 320 rows (same)
• Pitch factor: Range 0.555 to 1.575 / 0.555 to 1.5 (very similar)
• FOV: 240/320/500mm / 180/240/320/400/500mm (subject has slightly reduced range, but still within typical diagnostic needs)
• Metal artifact reduction: SEMAR (Volume, Helical, ECG gated) / SEMAR (Volume, Helical) (subject has added ECG gated capability)
• Gantry opening size: 780 mm (same)
• All previously cleared software options are listed as "no change" in functionality, with some having "workflow improvements" (e.g., Lung Volume Analysis, surESubtraction Lung, MyoPerfusion, Dual Energy System Package, 4D Airways Analysis) which are enhancements rather than regressions.

Differences (addressed through testing or not raising new concerns):

• Gantry Rotation Speed: 0.35s (Optional max 0.275s) for subject vs. 0.275s (Standard or optional) for predicate. This indicates a minor hardware difference, likely addressed by showing image quality is maintained.
• Wedge filter types: Two types for subject vs. Three for predicate. This is a minor design change.
• X-ray tube voltage/current: Max 72kW (Optional Max 90kW) for subject vs. Max 90kW (for one model) or Max 72kW (for others) for predicate. Comparable.
• Image reconstruction time: Up to 80 images/s for subject vs. Up to 50 images/s for predicate. Improvement in subject device.
• Helical reconstruction method: 20 rows or more: TCOT+ for subject vs. 16 rows or more: TCOT+ for predicate. Improvement in subject device (more rows).
• Patient Couch Type and related dimensions/weights: Various configurations/differences between subject and predicate models, indicating design variations but within expected functional range.
• Gantry tilt angle: ±30° for subject vs. ±22° for predicate. Improvement in subject device.
• Minimum area for installation: Smaller for subject (27m² vs 37.2m²). Improvement in subject device.
• Area finder: Optional for subject vs. NA for predicate. New feature on subject device. |
  | Image Quality | Image quality metrics (spatial resolution, CT number magnitude/uniformity, noise properties, low contrast detectability/CNR performance) should meet established specifications and be comparable to the predicate device. Images obtained should be of diagnostic quality. | CT image quality metrics performed using phantoms demonstrated that the subject device is substantially equivalent to the predicate device with regard to: spatial resolution, CT number magnitude/uniformity, noise properties, and low contrast detectability/CNR performance. Representative diagnostic images (head, chest, abdomen/pelvis, extremity, cardiac) were also reviewed and demonstrated diagnostic quality. |
  | Safety and Standards Compliance | The device must be designed and manufactured under Quality System Regulations (21 CFR 820, ISO 13485) and conform to applicable performance standards for ionizing radiation-emitting products (21 CFR, Subchapter J, Part 1020). It must also comply with various IEC, NEMA, and other relevant standards. | The device is designed and manufactured under QSR and ISO 13485. It conforms to applicable performance standards for Ionizing Radiation Emitting Products [21 CFR, Subchapter J, Part 1020] and numerous international standards including IEC60601-1 series, IEC60601-2 series, IEC60825-1, IEC62304, IEC62366, NEMA PS 3.1-3.18, NEMA XR-25 and NEMA XR-26. |
  | Software Validation | Software documentation must comply with FDA guidance for a Moderate Level of Concern, and validation testing should be successfully completed. | Software Documentation for a Moderate Level of Concern was included. Successful completion of software validation is cited in the conclusion. |
  | Risk Management | Risk analysis should be conducted. | Risk analysis was conducted. |

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

• Test Set Description: The "test set" for this submission primarily consists of:
  • Phantoms: Used for evaluating CT image quality metrics (spatial resolution, CT number, noise, low contrast detectability). The number and specific types of phantoms are not explicitly stated but are typically standard phantoms used in CT performance testing.
  • Representative Diagnostic Images: Clinical images covering various body regions (head, chest, abdomen/pelvis, extremity, cardiac). The number of cases/patients is not specified.
• Data Provenance: The document does not explicitly state the country of origin for the diagnostic images. Given Toshiba Medical Systems Corporation is based in Japan and Toshiba America Medical Systems, Inc. is in the US, the data could originate from either region or a combination. The document also does not specify if the data was retrospective or prospective. However, for a 510(k) clearance based on substantial equivalence, particularly for a hardware/software update to a CT scanner, diagnostic images are often retrospectively collected or acquired as part of internal validation.

3. Number of Experts Used to Establish Ground Truth and Qualifications:

• Number of Experts: One (1) expert is explicitly mentioned.
• Qualifications of Experts: An "American Board Certified Radiologist." No specific years of experience are stated. This expert reviewed the representative diagnostic images to confirm diagnostic quality.

4. Adjudication Method for the Test Set:

• The document describes a single American Board Certified Radiologist reviewing images to confirm diagnostic quality. This indicates no formal adjudication method involving multiple readers (like 2+1 or 3+1) was used for this specific part of the evaluation. The assessment of image quality from phantoms would not typically involve expert adjudication.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

• No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This document is for a general-purpose CT scanner system, not an AI-specific diagnostic tool that assists human readers. Therefore, there is no mention of an effect size for human reader improvement with or without AI assistance.

6. If a Standalone (Algorithm-Only Without Human-in-the-Loop Performance) Study Was Done:

• No, a standalone performance study in the context of an AI algorithm was not done. The Aquilion ONE (TSX-305A/3) V7.3 is a complete CT system where the "algorithm" refers to the image reconstruction and processing capabilities, which are inherent to the device's function. The study validates the overall system's ability to produce diagnostic images, not a separate AI algorithm's diagnostic accuracy. The performance is assessed on the system output.

7. The Type of Ground Truth Used:

• For the phantom studies, the "ground truth" is typically known physical properties of the phantoms (e.g., known dimensions, densities, contrast levels).
• For the representative diagnostic images, the "ground truth" for confirming "diagnostic quality" is based on the expert opinion/consensus of an American Board Certified Radiologist. This is a form of expert consensus, albeit from a single expert in this stated context. There is no mention of pathology or outcomes data being used as ground truth for this submission.

8. The Sample Size for the Training Set:

• The document does not specify a separate "training set" sample size. This submission is for a medical imaging device (CT scanner) rather than a deep learning AI algorithm that undergoes distinct training. The underlying algorithms for image reconstruction and processing (e.g., TCOT+, SEMAR) are developed and refined through engineering and iterative testing, but not typically in the same "training set" paradigm as AI for diagnostic interpretation. The software validation is mentioned, which refers to standard software development lifecycle testing.

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

• As a "training set" in the context of AI development is not explicitly mentioned as relevant to this submission, the establishment of ground truth for a training set is not applicable/described. The "ground truth" during the development of a CT scanner's image reconstruction algorithms would typically involve engineering specifications, physical models, and potentially early clinical data used for empirical tuning and validation, but not a formally labeled training set in the AI sense.

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SURESubtraction Lung software is intended to aid in the visualization of lung parenchyma enhancement by subtracting a non-contrast enhanced volume from a contrast enhanced volume.

The SURESubtraction Lung, CSSL-001A, is a post-processing software that subtracts image information by comparison of two data sets, one of which is contrast enhanced. Registration software is used to match the two independent studies.

The provided text does not contain detailed information about specific acceptance criteria, a study proving the device meets those criteria, or most of the requested quantitative metrics (sample sizes, number of experts, adjudication methods, MRMC study, standalone performance, training set data).

However, it does offer some general insights into the device validation process. Here's a breakdown of what can be extracted and where information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

Missing Information: Specific quantitative or qualitative metrics for acceptance criteria (e.g., sensitivity, specificity, image quality scores, agreement rates) are not provided in the document.

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

• Sample Size for Test Set: Not specified. The document only states "clinical data."
• Data Provenance: "clinical data at an investigational site." Country of origin is not specified, but it's likely related to either the submitting company (Toshiba Medical Systems Corporation, Japan) or Toshiba America Medical Systems, Inc., which has an office in Tustin, CA. The document does not explicitly state if the data was retrospective or prospective.

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

• 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

• An MRMC study is not explicitly mentioned. The document states that the software "aids in the visualization," implying assistance to a human reader, but does not quantify the improvement or describe a comparative effectiveness study design.

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

• The document describes the device as "intended to aid in the visualization" and for "post-processing," which suggests it's designed to be used with a human-in-the-loop. A standalone performance evaluation is not mentioned.

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

• The document implies that the ground truth for "abnormal blood perfusion correlating to thromboembolic disease such as pulmonary embolism" was used to assess the visualization capability. However, the specific method for establishing this ground truth (e.g., expert consensus on other imaging modalities, pathology, follow-up outcomes) is not detailed.

8. The sample size for the training set

• This information is not provided in the document. The document refers to "testing was conducted" but doesn't differentiate between training and test sets.

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

• This information is not provided in the document, as the training set itself is not mentioned.

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suRESubtraction Ortho software is intended to generate subtraction images and improve the visualization of contrast enhancement. The system can load two or more CT images with and without contrast enhancement. When used by a qualified physician, a potential application is to determine the course of treatment.

The SURESubtraction Ortho, CSSO-001A is a post-processing software that subtracts image information by comparison of two data sets, one of which is contrast enhanced. Registration software is used to match the two independent studies. This registration software has been used on Toshiba CT systems for a number of years with no adverse events reported.

Here is an analysis of the provided text regarding the acceptance criteria and study for the SURESubtraction Ortho, CSSO-001A device:

This 510(k) submission primarily focuses on demonstrating substantial equivalence to a predicate device after a modification to an accessory software (SURESubtraction Ortho) to extend its anatomical region of use. The provided text does not explicitly state specific quantitative acceptance criteria or detailed performance metrics used in a formal clinical study. Instead, it describes general improvements and performance validation.

1. Table of Acceptance Criteria and Reported Device Performance

As specific quantitative acceptance criteria are not presented in the provided document, the table below reflects what can be inferred from the "Testing" section and the claims of improvement.

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

The document states "performance studies" were conducted but does not specify the sample size for any test set (number of images, cases, or patients).

The data provenance is not explicitly stated (e.g., country of origin, retrospective or prospective). The nature of the testing described ("bench testing" and "resultant subtraction images produced by the software") suggests it could be based on existing or simulated data, rather than a prospective clinical trial.

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

The document does not specify the number of experts used to establish ground truth, nor does it provide their qualifications. The mention of "qualified physician" in the Indications For Use suggests clinical relevance, but details about expert review for the study are absent.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method (e.g., 2+1, 3+1, none) for the test set.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs Without AI Assistance

The document does not describe an MRMC comparative effectiveness study. The study described focuses on the device's ability to produce subtraction images and improve visualization, not on human reader performance with or without the device. Therefore, no effect size of human improvement with AI assistance is provided.

6. If a Standalone Study (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, the testing described appears to be a standalone (algorithm only) performance assessment. The text mentions "resultant subtraction images produced by the software" and an "improved visualization" when comparing software outputs to each other, without involving human interpretation performance as the primary endpoint. "Bench testing" is also mentioned, reinforcing this.

7. The Type of Ground Truth Used

The document implies a form of expert assessment or comparison to a gold standard, but it does not explicitly state the specific type of ground truth used. The claims of "successful visualization of calculated enhanced edema" and "improved visualization" suggest that there was a reference or standard against which the software's output was judged, likely by human observers or against a known expectation. However, it's not explicitly stated if this was pathology, outcomes data, or a formal expert consensus.

8. The Sample Size for the Training Set

The document does not provide any information regarding a training set sample size. This submission is for a modification of existing software, and the discussion focuses on performance studies, not the original development or training of the core algorithm.

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

Since no training set details are provided, there is no information on how its ground truth was established.

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