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The Discovery CT590 RT/Optima CT580 Computed Tomography Systems are intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data taken at different angles and planes, including Axial, Cine, Helical (Volumetric), and Gated (Respiratory and Cardiac) acquisitions for all ages. These images may be obtained either with or without contrast. This device may include signal analysis and display equipment, patient and equipment supports, components and accessories. This device may include data and image processing to produce images in a variety of trans-axial and reformatted planes. Further the images can be post processed to produce additional imaging planes or analysis results.

The GE Discovery CT590 RT/Optima CT580 Computed Tomography Systems are indicated for head, whole body, and vascular X-ray Computed Tomography applications in patients of all ages.

The device output is a valuable medical tool for the diagnosis of disease, trauma, or abnormality and for planning, guiding, and monitoring therapy.

The system is capable of assisting with minimally invasive procedures such as biopsy and ablation of tumors and pathology. The system allows imaging of Bariatrics patients, up to and including the morbidly obese population (BMI > 40).

The system can acquire CT anatomical images that are clinically useful in the simulation and planning of radiation therapy for the treatment of cancer.

If the Deviceless 4D option is included on the system, the system can be used to efficiently provide and display CT images of all phases of a breathing cycle for the evaluation of respiration-induced motion of the chest for use with therapy planning and simulation.

The software calculates the breathing period to determine the Cine acquisition duration and the cine time between images which is automatically imported to the 4D cine acquisition. The software then uses information derived from the imaging for the binning process.

Deviceless 4D is a software option used for capturing the respiratory cycle and binning the image such that the target motion may be obtained for treatment planning. Deviceless 4D has the same intended use and clinical output of Advantaae 4D (A4D,K032915) which was included in the cleared Discovery CT590 RT/ Optima CT580 (K093581) CT system. Deviceless 4D is an alternative and efficient solution for 4D imaging and virtual simulation without the need for an external respiratory monitoring device.

Deviceless 4D first uses a "scout-like" scan type called "SmartBreath" to determine the stable breathing cycle period that is then automatically implemented into the scan parameters for the Cine scans.

D4D incorporates a design that uses internal anatomic features of the respiratory cycle obtained during the Cine scans for image binning. These features are temporally registered to the actual breathing cycle.

The Discovery CT590 RT and Optima CT580 series of systems are composed of a gantry, patient table, operator console, computer, and power distribution unit (PDU), and interconnecting cables. The systems include image acquisition hardware, image acquisition, reconstruction software, associated accessories, and connections/interfaces to accessories.

The current systems are an evolutionary modification to the Discovery CT590 RT and Optima CT580 (K093581) by adding software features, quality fixes, IEC Ed. 3 compliance, and modifications in hardware for RoHS compliance and technology obsolesces.

The "16-slice", system generates cross-sectional images of the body by computer reconstruction transmission data taken at different angles and planes, including Axial. Cine, Helical (Volumetric) and gated acquisition modes. It has a maximum total collimation coverage of 20mm in the z direction.

The Discovery CT 590 RT and Optima CT 580 series of systems are designed to be a head and whole body CT systems incorporating the same basic fundamental operating principles and similar indications for use.

The provided submission focuses on establishing substantial equivalence for the GE Discovery CT590 RT/Optima CT580 with Deviceless 4D Option to its predicate devices, rather than presenting a detailed independent study with explicit acceptance criteria for the new feature or a comparative effectiveness study with human readers.

However, based on the information provided, here's a breakdown addressing the requested points:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria in terms of specific performance metrics (e.g., accuracy, sensitivity, specificity) for the Deviceless 4D option. Instead, the "acceptance criteria" are implied through the demonstration of equivalence to the predicate device.

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

• Sample Size for Test Set: The document mentions "clinical datasets from GE's internal development database" were used. However, the exact sample size (number of patients or scans) for this test set is not specified.
• Data Provenance: The data was sourced from "GE's internal development database," implying it's likely internal to GE and potentially collected from various regions where GE conducts development. The document does not specify the country of origin or whether it was retrospective or prospective. Given it's an "internal development database," it's highly likely to be retrospective data collected during prior development or clinical evaluations.

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

The submission does not mention using experts to establish ground truth for the test set or their qualifications. The comparison was primarily performed against the predicate device's output (Advantage 4D) as the reference.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method. The comparison was made directly between the D4D feature's output and the predicate A4D's output using statistical analysis.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done or reported in this submission. The focus was on demonstrating the technical equivalence of the D4D software to the existing A4D software using phantom and internal clinical data. There is no mention of human reader performance or improvements with or without AI assistance.

6. Standalone (Algorithm Only) Performance Study

Yes, a standalone performance evaluation of the Deviceless 4D (D4D) algorithm was performed.

• "Bench testing of and image review of the D4D feature provided additional data that this feature works both as intended and to provide the requisite data to substantiate performance claims, safety and efficacy, and ultimately substantial equivalence."
• "The testing performed included testing using a commercially available breathing phantom... and clinical datasets from GE's internal development database were used to compare the binning and measurement of D4D to the predicate feature A4D."
  This clearly indicates that the algorithm's output was directly compared against a reference (the predicate A4D and phantom measurements).

7. Type of Ground Truth Used

The ground truth used for the D4D evaluation was effectively the output of the predicate device's Advantage 4D (A4D) feature for binning and measurement from clinical datasets, and measurements from a commercially available breathing phantom for assessing breathing periodicity detection. This can be considered a form of "reference standard comparison" where the established performance of a cleared device or a known physical property (phantom) serves as the truth.

8. Sample Size for the Training Set

The document does not specify the sample size for the training set. It only mentions the use of "GE's internal development data base" for clinical datasets used in testing, but it's unclear if this same database was used for training or if other data was used, and if so, how much.

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

The document does not provide information on how the ground truth for the training set was established. Given the nature of a 510(k) submission focused on substantial equivalence to an existing device, a detailed account of training data ground truthing might not be deemed necessary if the underlying technology is largely similar and the primary claim is equivalence rather than novel performance.

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syngo TrueD is a medical diagnostic application for viewing, manipulation, 3D- visualization and comparison of medical images from multiple imaging modalities and/or multiple time-points. The application supports functional data, such as PET or SPECT as well as anatomical datasets, such as CT or MR. The images can be viewed in a number of output formats including MIP and volume rendering.

syngo TrueD enables visualization of information that would otherwise have to be visually compared disjointedly. syngo TrueD provides analytical tools to help the user assess, and document changes in morphological or functional activity at diagnostic and therapy follow-up examinations.

syngo TrueD is designed to support the oncological.workflow by helping the user to confirm the absence or presence of lesions, including evaluation, quantification, follow-up and documentation of any such lesions. The application allows to store and export volume of interest (VOI) structures in DICOM RT format for use in radiation therapy planning systems.

syngo TrueD allows visualization and analysis of respiratory gated studies to support accurate delineation of the target or treatment volume over a defined phase of the respiratory cycle and thus provide information for radiation therapy planning.

Note: The clinician retains the ultimate responsibility for making the pertinent diagnosis based on their standard practices and visual comparison of the separate unregistered images. syngo Truen in a complement to these standard procedures.

syngo TrueD is a medical diagnostic application for viewing, manipulation, 3D- visualization and comparison of medical images from multiple imaging modalities and/or multiple time-points. The application supports functional data, such as PET or SPECT as well as anatomical datasets, such as CT or MR. The images can be viewed in a number of output formats including MIP and volume rendering.

syngo TrueD enables visualization of information that would otherwise have to be visually compared disjointedly. syngo TrueD provides analytical tools to help the user assess, and document changes in morphological or functional activity at diagnostic and therapy follow-up examinations.

syngo TrueD is designed to support the oncological workflow by helping the user to confirm the absence or presence of lesions, including evaluation, quantification, follow-up and documentation of any such lesions. The application allows to store and export volume of interest (VOD structures in DICOM RT format for use in radiation therapy planning systems.

syngo TrueD allows visualization and analysis of respiratory gated studies to support accurate delineation of the target or treatment volume over a defined phase of the respiratory cycle and thus provide information for radiation therapy planning.

TrueD will be marketed as a software only solution for the end-user (with recommended hardware requirements) . It will be installed by Siemens service engineers. The TrueD described supports DICOM formatted images and information. It is based on the Windows XP operating system.

This 510(k) summary primarily focuses on the substantial equivalence of the "syngo™ TrueD Software" to existing predicate devices, rather than providing detailed acceptance criteria and a specific study demonstrating performance against those criteria. It's a submission for a software device used for image viewing, manipulation, and 3D visualization, targeting applications in oncology and radiation therapy planning.

Based on the provided text, the following information can be extracted:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not specify quantitative acceptance criteria or report specific performance metrics for the syngo™ TrueD Software that would typically be found in a performance study (e.g., sensitivity, specificity, accuracy for a diagnostic task). The submission emphasizes substantial equivalence to predicate devices for its intended use and technical characteristics.

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

The document does not mention a specific test set, its sample size, or data provenance (e.g., country of origin, retrospective/prospective). This type of information is usually associated with performance studies assessing diagnostic accuracy or similar metrics.

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

The document does not describe any specific ground truth establishment process for a test set, nor does it mention the number or qualifications of experts involved.

4. Adjudication Method for the Test Set:

No adjudication method is mentioned as there is no described test set or ground truth establishment process.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

No MRMC comparative effectiveness study is mentioned. The document primarily argues for substantial equivalence based on intended use and technological characteristics compared to predicate devices. The final "Note" states: "The clinician retains the ultimate responsibility for making the pertinent diagnosis based on their standard practices and visual comparison of the separate unregistered images. syngo TrueD in a complement to these standard procedures." This suggests the device is an assistive tool and not designed to replace human interpretation in diagnostic decision-making, which is often the focus of MRMC studies.

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

The document does not describe a standalone performance study. The device is presented as an image viewing and manipulation tool to support clinicians.

7. The Type of Ground Truth Used:

No specific ground truth type is mentioned as no performance study with a defined test set is described.

8. The Sample Size for the Training Set:

The document does not mention any training set or its sample size. This suggests the device may not heavily rely on machine learning models that require extensive training data in the same way as some contemporary AI diagnostics. Its function is primarily visualization and analysis, with tools to help users assess changes.

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

Not applicable, as no training set is mentioned.

Summary of what the document does provide regarding "proof":

The document argues for the device's substantial equivalence to existing legally marketed predicate devices, rather than providing direct "proof" of meeting novel acceptance criteria through a performance study. It emphasizes:

• Identical Intended Use: The device's intended use is described as viewing, manipulation, 3D visualization, and comparison of medical images, similar to the functions of its predicate devices. It supports oncology workflows (lesion evaluation, quantification, follow-up, documentation) and radiation therapy planning (visualization/analysis of respiratory gated studies).
• Similar Technological Characteristics: It is a software-only solution, supports DICOM images, and runs on Windows XP, implying comparable technology to its predicates.
• Safety Information: A hazard analysis was conducted, and appropriate preventive measures were taken, resulting in a determination of "minor level of concern." It highlights that the device has no patient-contacting materials, is used by trained professionals, and device output is subject to review by these professionals. It also states the device "does not impact the quality or status of the original acquired data."

Conclusion:

This 510(k) submission for syngo™ TrueD software in 2009 is a premarket notification for substantial equivalence. It does not include a detailed study with quantitative acceptance criteria and performance data like those commonly seen for AI/ML diagnostic algorithms today. Instead, its "proof" is centered on demonstrating that it is as safe and effective as, and performs as well as, already legally marketed predicate devices with similar intended uses and technological features.

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