Search Results

The Siemens Symbia series is intended for use by appropriately trained health care professionals to aid in detecting, localizing, diagnosing, staging and restaging of lesions, tumors, disease and organ function for the evaluation of diseases and disorders such as, but not limited to, cardiovascular disease, neurological disorders and cancer. The images produced by the system can also be used by the physician to aid in radiotherapy treatment planning and interventional radiology procedures.

SPECT: The SPECT component is intended to detect or image the distribution of radionuclides in the body or organ (physiology), using the following techniques; Planar imaging, and tomographic imaging for isotopes with energies up to 588 keV.

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.

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.

Software: the syngo MI Applications software is a display and analysis package intended to aid the clinician in the assessment and quantification of pathologies in images produced from SPECT, PET, CT and other imaging modalities.

The following statement applies only to Siemens Symbia T16, and Symbia Intevo Bold systems: This CT system can be used for low dose lung cancer screening in high risk populations.* * As defined by professional medical societies. Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information.

The Siemens Symbia systems consist of Sinqle Photon Emission Computed Tomography (SPECT) scanners and integrated hybrid X-Ray Computed Tomography (CT) and SPECT scanners. 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 CT's anatomical detail 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 standalone diagnostic imaging devices.

The provided text describes a 510(k) premarket notification for the Siemens Symbia 6.7 system, primarily focusing on its substantial equivalence to predicate devices and adherence to performance and safety standards. However, it does not contain information about a study proving the device meets specific acceptance criteria for AI/algorithm performance, nor does it specify detailed acceptance criteria for a new software feature.

The document states:

• Modifications: The Symbia 6.7 includes software updates, specifically enhancements to "xSPECT features" (gastric retention addition, renal processing improvement), addition of SPECT Dose Reporting, and expansion of xSPECT Quantification to support Absolute Quantification of I-131. It also notes an upgrade to Windows 10 and updated third-party applications.
• Performance Testing: It mentions "Performance testing for the CT subsystem was included in the original premarket notification for the CT subsystems and there have been no changes affecting this testing." For the SPECT component, it states: "Collimator performance testing is conducted according to NEMA NU-1:2018. All Performance testing met the predetermined acceptance values." It also shows "Quantitative accuracy specifications" (Figure 3) which demonstrate "accurate quantification in phantoms for all the isotopes and collimators tested" and state that "The quantitative error for all isotopes with the collimators is smaller or equal to 10%, and met the predefined acceptance criteria."

Based on the provided text, I cannot describe acceptance criteria and a study that proves the device meets the acceptance criteria in the context of AI/algorithm performance, because no specific AI/algorithm performance study or associated acceptance criteria are detailed. The document primarily focuses on hardware specifications and general system performance as per NEMA standards, and software updates for existing features, rather than the introduction or validation of a new AI-driven diagnostic algorithm.

Therefore, I am unable to provide the requested information fully, particularly points 2-7, 9, as the document does not describe a study that validates new AI/algorithmic features against specific, measurable acceptance criteria in a clinical or reader study setting.

However, I can extract the general quantitative acceptance criteria mentioned for existing features:

1. Table of acceptance criteria and reported device performance (for quantitative accuracy, as stated):

Note: These criteria and performance metrics are stated to be "In phantoms for objects with negligible partial volume effect" and "incident count rate" for the Lu177 metric.

2. Sample size used for the test set and the data provenance: Not specified in the provided text for the quantitative accuracy tests beyond "in phantoms." No clinical test sets are described.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not specified. Ground truth appears to be based on physical phantom measurements, not expert consensus on clinical images for a new AI feature.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not specified.

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 MRMC study or AI assistance evaluation is mentioned.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: The "Quantitative accuracy specifications" appear to be standalone phantom tests, but not for a new AI algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): For the quantitative accuracy, the ground truth appears to be established through physical phantom measurements and known isotope properties, rather than clinical consensus, pathology, or outcomes data.

8. The sample size for the training set: Not applicable, as no new AI model training is described.

9. How the ground truth for the training set was established: Not applicable, as no new AI model training is described.

In summary, the provided document is a 510(k) summary for a SPECT/CT system, focusing on its substantial equivalence to predicates and adherence to established performance standards (like NEMA NU-1:2018) for its existing imaging capabilities. It does not detail the validation of a new AI/algorithmic feature with the type of acceptance criteria and study design requested in the prompt.

Ask a specific question about this device

The Siemens Symbia Intevo Bold is intended for use by appropriately trained health care professionals to aid in detecting, localizing, diagnosing, staging of lesions, tumors, disease and organ function for the evaluation of diseases and disorders such as, but not limited to, cardiovascular disease, neurological disorders and cancer. The images produced by the system can also be used by the physician to aid in radiotherapy treatment planning and interventional radiology procedures.

SPECT: The SPECT component is intended to detect or image the distribution of radionuclides in the body or organ (physiology), using the following techniques; Planar imaging, and tomographic imaging for isotopes with energies up to 588 keV.

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.

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.

Software: the syngo MI Applications software is a display and analysis package intended to aid the clinician in the assessment and quantification of pathologies in images produced from SPECT, PET, CT and other imaging modalities.

The Siemens Symbia Intevo Bold consist of Sinqle Photon Emission Computed Tomography (SPECT) scanners and integrated hybrid X-Ray Computed Tomography (CT) and SPECT scanners. 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 CT's anatomical detail 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 standalone diagnostic imaging devices.

Symbia Intevo Bold implements software version syngo MI Applications VB20A.

Modifications include:

1. Incorporation of the commercially available SOMATOM Scope Power CT system (K151749)

2. Software updates include modifications to support features available with the CT and SPECT subsystems

3. Expansion of commercially available xSPECT Quant (Symbia 5.0 K131634) to support quantification of additional isotopes

4. Four additional touchpad sensors to cover detectors' light rails and L-arms.

Commercially available xSPECT Quant (Symbia 5.0 K131634) was expanded to support quantification of additional isotopes such as I-123 and In-111. For these isotopes, dose calibrator independent quantification is enabled by a NIST traceable sensitivity calibration method. In addition, cross calibration capabilities were added to remove dose calibrator biases and variations in SUV calculations. The system allows cross calibration of multiple dose calibrators and, once calibrated, automatically adjusts for their biases.

In addition to dose calibrator independent quantification for I-123 and In-111 support for dose calibrator dependent quantification was added for a broad range of SPECT isotopes and collimators (Broad Quantification). Dose calibrator dependent quantification neither supports NIST traceable calibration nor cross calibration and relies on sensitivity measurements based on a local dose calibrator.

Here's an analysis of the acceptance criteria and study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The documents primarily focus on the quantitative accuracy and reproducibility of the "xSPECT Quant" and "Broad Quantification" features within the Symbia Intevo Bold system.

Additionally, general performance testing against NEMA NU-1:2012 standards was conducted, with a general statement that "All Performance testing met the predetermined acceptance values." This includes intrinsic spatial resolution, intrinsic spatial linearity, intrinsic energy resolution, intrinsic flood field uniformity, multiple window spatial registration, intrinsic count rate performance in air, system spatial resolution with LEHR, SPECT reconstructed spatial resolution, intrinsic flood field uniformity at 25% busy time, intrinsic energy resolution at 25% busy time, energy peak position stability, and system spatial resolution at 25% busy time.

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

The documents do not specify the sample size in terms of number of patient cases or images for either the quantitative accuracy or reproducibility testing. The tests utilized "phantoms."

• Data Provenance: The data provenance is not explicitly stated as retrospective or prospective patient data. Given the use of "phantoms," the data is synthetically generated and controlled within a laboratory setting, not derived from human subjects. The testing was conducted by Siemens Medical Solutions USA, Inc.

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

This information is not provided in the document. The ground truth for xSPECT Quant accuracy was established directly through reference to the National Institute of Standards and Technology (NIST) traceable precision sources for calibration. For reproducibility, the ground truth standard is the initial measurement against which subsequent measurements are compared. These methods do not inherently require human expert consensus for establishing the ground truth values.

4. Adjudication Method for the Test Set

An adjudication method is not applicable and not mentioned. The quantitative accuracy and reproducibility tests rely on direct comparison to known physical standards (NIST traceable sources) or statistical measures of variance, rather than human interpretation that would require 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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not mentioned and likely not performed for this submission. The device (Symbia Intevo Bold) is an imaging system, not an AI-assisted diagnostic software in the sense of providing automated interpretations or workflow enhancements to human readers that would be typically evaluated in an MRMC study focused on reader performance improvement. The "MI Applications software" is a "display and analysis package" to aid clinicians in assessment and quantification, but the provided studies do not evaluate its impact on human reader performance.

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

Yes, the performance testing described for "xSPECT Quant" and "Broad Quantification" falls under a standalone (algorithm only) evaluation. The system's ability to accurately quantify isotopes or reproduce measurements is tested intrinsically, separate from a human operator's diagnostic interpretation. The NEMA NU-1:2012 testing also evaluates the physical performance of the SPECT and CT components in a standalone manner.

7. The Type of Ground Truth Used

• xSPECT Quant (Quantitative Accuracy): The ground truth was based on a NIST traceable precision source. This is a highly accurate, objectively verifiable physical standard.
• Broad Quantification (Reproducibility): The ground truth for reproducibility essentially refers to the initial, measured values from dose calibrator dependent quantification against which subsequent measurements are compared for consistency.

8. The Sample Size for the Training Set

The documents do not provide information regarding a "training set" sample size. The Symbia Intevo Bold is a medical imaging system, and while it includes software (syngo MI Applications), the described testing focuses on the physical and quantitative performance of the imaging hardware and associated quantification algorithms, not a machine learning model that would typically have a distinct training set. The software updates mention "modifications to support features" and "software improvements," suggesting iterative development rather than a single distinct training phase for a new AI model.

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

Since no specific "training set" for a machine learning model is mentioned, details on how its ground truth was established are not provided. If the software components involve internal calibration or optimization routines, these would use internal reference data, but it's not discussed in the context of an external, validated training set as one might find for a deep learning algorithm.

Ask a specific question about this device