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ADMIRE is a CT reconstruction software. The end user can choose to apply either ADMIRE or the weighted filter backprojection (WFBP) to the acquired raw data. Depending on the clinical task, patient size, anatomical location, and clinical practice, the use of ADMIRE can help to reduce radiation dose while maintaining pixel noise, low contrast detectability and high contrast resolution. Phantom measurements showed that high contrast resolution and pixel noise are equivalent between full dose WFBP images and reduced dose ADMIRE images. Additionally, ADMIRE can reduce spiral artifacts by using iterations going back and forth between image space and raw data space.

Images reconstructed with ADMIRE are not intended to be evaluated with syngo Osteo CT or syngo Calcium Scoring.

Siemens ADMIRE is an extension of the previously cleared Sinogram Affirmed Iterative Reconstruction (SAFIRE) reconstruction algorithm. ADMIRE is a software option for CT operating systems that provides an improved image quality or reciprocally can allow the physician to acquire scans with reduced radiation dose without reduction of image quality compared to today's standard.

ADMIRE is designed to improved reconstructed image quality through the integration of additional processing steps in image reconstruction. These additional steps result in the following improvements in image quality:

• . Higher pixel noise reduction
• A noise texture closer to filtered back projection (FBP) .
• . Improved resolution for high contrast edges

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

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly state quantitative acceptance criteria for device performance. Instead, it frames the performance improvements of ADMIRE relative to its predicate device (SAFIRE) and traditional methods (FBP/WFBP). The "acceptance criteria" are implied to be the successful demonstration of these improvements and equivalence for certain metrics.

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

• Sample Size: Not explicitly stated for specific tests. The document mentions "phantom testing" and "sample clinical images." No specific number of patients or imaging studies are provided.
• Data Provenance: The document does not specify the country of origin. It indicates that "phantom testing" was conducted and "sample clinical images were also provided within the submission." The general nature of the testing suggests it's likely internal Siemens data. The testing involves "simulated body and head phantoms."
• Retrospective/Prospective: The nature of the testing described (phantom testing, bench testing, verification/validation) suggests a controlled, likely retrospective analysis of specific data, or controlled prospective phantom acquisitions. There's no mention of a large-scale, prospective clinical trial with human subjects.

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 evaluation primarily relies on phantom measurements and technical performance metrics (noise reduction, resolution, noise texture) rather than a subjective human reader assessment against a clinical ground truth.

4. Adjudication Method for the Test Set

This information is not provided. Given the focus on objective phantom measurements and technical image quality metrics, a formal human reader adjudication method like 2+1 or 3+1 is unlikely to have been used in the primary performance evaluations 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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study with human readers assessing AI-assisted vs. non-AI-assisted images is not mentioned or described in this 510(k) summary. The document focuses on the technical improvements of the reconstruction algorithm itself.

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

Yes, the performance described is primarily that of the standalone algorithm (ADMIRE). The improvements in pixel noise reduction, noise texture, high contrast resolution, and artifact reduction are inherent to the algorithm's processing of raw data. The statement "The end user can choose to apply either ADMIRE or the weighted filter back-projection (WFBP) to the acquired raw data" further reinforces its standalone nature as a reconstruction option.

7. The Type of Ground Truth Used

The ground truth used appears to be:

• Physical Phantom Measurements: For metrics like pixel noise, high contrast resolution, and low contrast detectability. This involves comparing the device's output against known physical properties or reference measurements from phantoms.
• Reference Reconstruction Methods: Comparisons are made against established methods like Filtered Back Projection (FBP) and Weighted Filtered Back Projection (WFBP) to demonstrate improvements or equivalence.
• Compliance with Standards: Verification against various international standards for medical devices and software (ISO, IEC, NEMA).

There is no mention of "expert consensus," "pathology," or "outcomes data" being used as ground truth for the performance evaluation described in this summary.

8. The Sample Size for the Training Set

This information is not provided. The document outlines changes to a reconstruction algorithm rather than a machine learning model that would typically have a distinct training set. While reconstruction algorithms are developed and refined using data, the document doesn't use the terminology of "training set" in the context of deep learning.

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

This information is not provided, and the concept of a "training set" and associated ground truth, as typically understood in machine learning, is not discussed in this summary. The development process likely involved engineering and optimization against known physical models and existing reconstruction results, rather than labeled training data for a learning algorithm.

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The Siemens SOMATOM Definition AS Open systems are intended to produce 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.

(*spiral planes: the axial planes resulted from the continuous rotation of detectors and x-ray tube, and the simultaneous translation of the patient.)

The Siemens SOMATOM Definition AS Open is a whole body X-ray Computed Tomography System. The SOMATOM Definition AS Open produces CT images in DICOM format, which can be used by post-processing applications commercially distributed by Siemens and other vendors.

The system software is a command-based program used for patient management, data management, X-ray scan control, image reconstruction, and image archive/evaluation. The new version of system software, syngo® CT 2013B (SOMARIS/7 VA46A), supports the following features:

• MARIS (Metal Artifact Reduction in Image Space) A image . reconstruction mode designed to reduce image artifacts caused by metal
• HD FoV Pro (HD FoV 2.0) Designed to enable a more reliable . visualization of the skin line of human body parts located outside of the standard field of view
• t-MIP -- Image manipulation method for arithmetic operations which allows . the calculation of temporal Maximum or Minimum Intensity Projection (MIP) images from a set of series.

Here's a breakdown of the acceptance criteria and the study information for the SOMATOM Definition AS Open configured with software version syngo® CT 2013B (SOMARIS/7 VA46A), based on the provided text:

Important Note: The provided document is a 510(k) summary for a medical device which is largely about demonstrating "substantial equivalence" to a predicate device. This type of submission often focuses on verifying that new features don't introduce new safety or effectiveness concerns, rather than conducting a full-scale clinical trial to prove a specific level of diagnostic performance against a robust ground truth. As such, some of the requested information (especially about specific performance metrics tied to acceptance criteria, MRMC studies, and detailed ground truth establishment for clinical effect) might not be explicitly present or as detailed as in a typical in vitro diagnostic (IVD) or AI-only software submission.

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size (Clinical Tests): Not specified. The document states "Clinical tests were performed... to validate the performance of the MARIS algorithm" and "These tests include testing of the metal artifact reduction capabilities of MARIS in different clinical scenarios." However, the number of patients, scans, or images is not provided.
• Data Provenance: Not explicitly stated (e.g., country of origin). The tests were "clinical tests," implying real patient data. It is highly likely to be retrospective clinical data, as typical for 510(k) submissions focusing on software improvements, but this is not explicitly confirmed.

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

• The document mentions "clinical tests" for MARIS validation, but it does not specify how ground truth was established for these tests, nor does it mention the number or qualifications of experts involved in any ground truth assessment. In the context of metal artifact reduction, "ground truth" might be subjective visual assessment by radiologists if not compared to a gold standard imaging modality.

4. Adjudication Method for the Test Set:

• The document does not specify any adjudication method (e.g., 2+1, 3+1). Given the lack of detail on expert involvement, it's unlikely a formal adjudication process was described for the submission.

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

• No, an MRMC comparative effectiveness study was not done (or at least not described in this summary). The studies mentioned focus on validating the performance of features (MARIS, HD FoV Pro) in the device itself, not on comparing human reader performance with and without AI assistance from this specific device.

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

• The document describes "bench tests were performed to verify and validate the performance of the MARIS and HD FoV Pro (HD FoV 2.0) features," which are likely standalone algorithm evaluations using phantoms or controlled datasets.
• "Clinical tests" were also performed for the MARIS algorithm, which would involve the algorithm processing clinical data. While these involve physicians interpreting the output of the CT system, the focus of the "clinical tests" was on validating the algorithm's performance (e.g., artifact reduction), rather than a human reading study. So, in terms of the algorithm itself, yes, standalone performance was assessed.

7. The Type of Ground Truth Used:

• For the "bench tests" of MARIS and HD FoV Pro, the ground truth would likely be phantom-based measurements and technical specifications. Phantoms provide a known, controlled environment to assess image quality, artifact reduction, and field of view accuracy.
• For the "clinical tests" of MARIS, the document does not explicitly state the type of ground truth used. In the context of artifact reduction, it could involve visual assessment by clinicians comparing images with and without MARIS, or a comparison to an established 'gold standard' image if available (e.g., a non-metallic scan of the same area if feasible). However, no specifics are provided.

8. The Sample Size for the Training Set:

• The document does not specify the sample size for any training set. This is not uncommon for 510(k) submissions where the software updates are incremental and rely on established engineering practices, rather than a deep learning model requiring a distinct, large training dataset. The MARIS algorithm is described as an "image reconstruction mode," implying an algorithmic approach rather than a machine learning model that undergoes explicit "training" on a labeled dataset.

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

• As the document does not mention a training set in the context of machine learning, there is no information on how its ground truth was established. For algorithmic development, the "training" (design and tuning) is based on engineering principles, image science, and potentially smaller, internally-derived datasets with known properties or simulated artifacts.

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syngo.CT Pulmo 3D is an image analysis software for CT volume data sets. It analyses the lunq, either completely or in parts, identifying areas with lower or higher Hounsfield values in comparison to a predefined threshold. These areas are evaluated using statistical methods such as histograms and percentiles.

Using syngo.CT Pulmo 3D, you can examine the lung parenchyma and the airways of the lung.

The following evaluation tools are provided:

• Computation of lung volumes .
• Display of statistics related to the lung .
• Setting of markers .
• Airway measurements .

syngo.CT Pulmo 3D facilitates the reporting by using of appropriate reporting tools, for example, key image creation.

You can use syngo.CT Pulmo 3D to create a DiCOM Structured Report.

syngo.CT Pulmo 3D allows the evaluation of lung tissue and airways. In contrast to lung function tests, CT evaluations can show the effect of a disease on the parenchyma and the airways. The lungs as well as the airways are segmented in the preprocessing, and divisions of the lungs like thirds, core/peel, and lung lobes are calculated.

The provided text does not contain details of specific acceptance criteria or an explicit study that proves the device meets them in the format requested.

The document is a 510(k) Summary for the SIEMENS syngo.CT Pulmo 3D device, which focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed performance validation studies with specific acceptance criteria.

However, based on the non-clinical testing section, we can infer some general information:

Inferred Information from the Document:

• Acceptance Criteria & Device Performance: The document generally states that "The testing results supports that all the software specifications have met the acceptance criteria." However, it does not provide a table specifying these criteria or the reported performance metrics. The criteria are likely tied to the software's functional specifications, such as accurate lung and airway segmentation, volume computation, and statistical analysis as described in the "Device Description" and "Indications for Use" sections.
• Sample Size and Data Provenance: Not mentioned for any test sets.
• Number of Experts and Qualifications: Not mentioned.
• Adjudication Method: Not mentioned.
• Multi Reader Multi Case (MRMC) Comparative Effectiveness Study: Not mentioned. The focus is on the software's functionality, not comparative effectiveness with human readers.
• Standalone Performance: The testing mentioned in Section 6 ("Nonclinical Testing") refers to verification and validation of the device's software package to ensure it fulfills requirements and specifications. This implies standalone (algorithm-only) testing.
• Type of Ground Truth: Not explicitly stated, but for software functionality like segmentation and measurement, the ground truth would typically be established by expert radiologists or phantoms, though this is not confirmed in the document.
• Training Set Sample Size: Not mentioned.
• Ground Truth for Training Set: Not mentioned.

Summary of what is present and what is missing:

This 510(k) summary focuses more on demonstrating regulatory compliance and substantial equivalence through adherence to standards (IEC, ISO, DICOM) and risk management, rather than detailed clinical or performance validation studies with explicit quantitative acceptance criteria.

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The syngo.CT Neuro Perfusion software package is designed to evaluate areas of brain perfusion. The software processes images or volumes that were reconstructed from continuously acquired CT data after the injection of contrast media.

It generates the following result volumes:

• Cerebral blood flow (CBF)
• Cerebral blood volume (CBV)
• Local bolus timing (time to start (TTS), time to peak (TTP), time to drain (TTD))
• Mean transit time (MTT)
• Transit time to the center of the IRF (TMax)
• Flow extraction product (permeability)
• Temporal MIP
• Temporal Average
• Baseline Volume
• Modified dynamic input data

The software also allows the calculation of mirrored regions or volumes of interest and the visual inspection of time attenuation curves. One clinical application is to visualize the apparent blood perfusion and the parameter mismatch in brain tissue affected by acute stroke.

Areas of decreased perfusion appear as areas of changed signal intensity:

• Lower signal intensity for CBF and CBV
• Higher signal intensity for TTP, TTD, MTT, and TMax

A second application is to visualize blood brain barrier disturbances by modeling extra-vascular leakage of blood into the interstitial space. This additional capability may improve the differential diagnosis of brain tumors and be helpful in therapy monitoring.

The syngo. CT Neuro Perfusion software allows the quantitative evaluation of dynamic CT data of the brain acquired during the injection of a compact bolus of iodinated contrast material. It mainly aids in the early differential diagnosis of acute ischemic stroke. Blood-brain-barrier (BBB) imaging also supports the diagnostic assessment of brain tumors.

By providing images of e.g. cerebral blood flow (CBF), cerebral blood volume (CBV), time to peak (TTP), and Mean Transit Time (MTT) from one set of dynamic CT images or volumes, syngo.CT Neuro Perfusion allows a quick and reliable assessment of the type and extent of cerebral perfusion disturbances. The underlying approaches have been validated in extensive clinical studies and have been in routine clinical use for more than 10 vears.

The current syngo.CT Neuro Perfusion implementation allows simultaneous multi-slice processing and supports the workflow requirements in a stroke workflow. The availability of flow extraction product imaging extends the option to the diagnosis of brain tumors.

The provided text does not contain detailed information about the acceptance criteria or a specific study that proves the device meets those criteria. It mainly focuses on the device's substantial equivalence to a predicate device and its indications for use.

However, based on the information available, I can infer some aspects related to non-clinical testing and general acceptance.

Here’s an attempt to structure the answer based on the provided text, highlighting what is present and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not specify a sample size for a test set or provide details on data provenance (e.g., country of origin, retrospective/prospective study design). The discussion of testing is general and relates to non-clinical software verification and validation.

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. The filing describes non-clinical testing for software verification and validation rather than a clinical performance study with expert ground truth.

4. Adjudication method for the test set

This information is not provided.

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 is not mentioned in the provided text. The document describes a software package for post-processing CT data and does not detail studies on human reader performance with or without AI assistance.

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

The document discusses "non clinical tests" for software verification and validation, and states that "all the software specifications have met the acceptance criteria." This implies a form of standalone performance assessment against predefined specifications, but the specifics of how "standalone" this was (e.g., if it involved simulated data or real patient data processed without human intervention for evaluation) are not detailed. It's not a clinical standalone study in the sense of diagnostic accuracy against a ground truth.

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

The document refers to "non clinical tests" and "software specifications" rather than clinical ground truth like pathology or expert consensus from a clinical study. The "ground truth" for these non-clinical tests would likely be the expected output or behavior according to the software's functional requirements and design specifications.

8. The sample size for the training set

The document does not specify a sample size for a training set. The software likely relies on pre-established algorithms for generating perfusion maps, which would have been developed and "trained" (or validated) on various datasets over many years, as indicated by: "The underlying approaches have been validated in extensive clinical studies and have been in routine clinical use for more than 10 years." However, specifics about this device's training set are absent.

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

The document mentions that "The underlying approaches have been validated in extensive clinical studies and have been in routine clinical use for more than 10 years." This suggests that the ground truth for the "training" (or more accurately, the development and historical validation of the underlying algorithms) would have been established through clinical studies, but the specific methods (e.g., expert consensus, correlation with other imaging modalities, or patient outcomes) are not detailed in this 510(k) summary for this particular device.

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syngo® Single Source Dual Energy is designed to operate with CT images taken at the same anatomical region of a patient using two different kV levels. The differences in the energy dependence of the attenuation coefficient of the different materials provide information about the chemical composition of body materials. The images are combined to visualize and analyze information about anatomical and pathological structures.

The functionality of the syngo® Single Source Dual Energy applications are as follows:

• Gout Evaluation .
• Monoenergetic .

Dual Energy CT can be used to obtain intensity measurements with two different spectra and thus provides additional information when compared to single energy.

This additional information is analyzed in the post processing application syngo Single Source Dual Energy and can be used to improve the visualization of various materials in the human body.

After loading the reconstructed images corresponding to the two subsequent scans with different X-ray spectra into syngo® Single Source Dual Energy, the images are first registered to compensate for motion effects. They are then displayed using linear blending with selectable mixing ratio and color scale ("General Viewing"). Multiplanar reformations (MPR) of the volume are shown in 3 image segments, which are initialized as sagittal, coronal and axial view.

After arriving at an initial diagnosis on the basis of the CT-images, the user can choose between application classes Monoenergetic or Gout Evaluation.

These application classes are designed for specific clinical tasks, so that algorithms, additional tool buttons, the use of colored overlay images and image representation (for example MPR or maximum intensity projection) are optimized correspondingly. For Gout Evaluation a fourth image segment is used for volume rendering techniques (VRT). If is possible to adjust Gout Evaluation by using a configuration dialog. Special tools are available to remove the table or perform manual punching.

The provided text describes a special 510(k) submission for the Siemens syngo® Single Source Dual Energy software. It details the device's functionality, its claimed substantial equivalence to predicate devices, and the nonclinical testing performed. However, it does not contain information about specific acceptance criteria, a study proving the device meets those criteria, or details regarding sample sizes, expert involvement, ground truth establishment, or comparative effectiveness studies (MRMC).

The document primarily focuses on:

• Device Description and Intended Use: Explaining what the software does (post-processing dual-energy CT images to improve visualization of materials, with application classes like Monoenergetic and Gout Evaluation).
• Substantial Equivalence Claim: Stating that the device is substantially equivalent to previously cleared Siemens devices (syngo® Dual Energy with extended functionality (K083524) and syngo® Volume Perfusion - CT Body (K073373)) due to similar technology, intended use, and the unmodified use of an algorithm from a predicate device.
• Nonclinical Testing: Mentioning that verification and validation were performed for newly developed components and the complete software package according to standards like DICOM, IEC 60601-1-4, IEC 60601-1-6, IEC 62304, and IEC/ISO 14971. It states that "test results with the release acceptance criteria" were compared, but does not specify what those criteria were or what the results showed.

Therefore, I cannot populate the requested tables and information as the necessary details are not present in the provided text. The document is a 510(k) summary, which typically focuses on demonstrating substantial equivalence rather than presenting detailed performance study results with specific acceptance criteria and outcome metrics a device is held to.

To answer your request, I would need a different type of document, such as a full study report or a more detailed section of a regulatory submission that outlines the performance evaluation and acceptance criteria.

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The SOMATOM P45 is intended to produce 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. (*spiral planes: the axial planes resulting from the continuous rotation of detectors and x-ray tube, and the simultaneous translation of the patient.)

The Siemens SOMATOM P45 is a whole body X-ray Computed Tomography System, which features two continuously rotating tube-detector systems and functions according to the fan beam principle. The SOMATOM P45 produces CT images in DICOM format, which can be used by postprocessing applications commercially distributed by Siemens and other vendors. The system software is a command-based program used for patient management, data management, X-ray scan control, image reconstruction, and image archive/evaluation. The new version of system software, SOMARIS/7 VA44, supports a Windows 7 operating system, additional scanning and evaluation techniques CARE (Combined Application to Reduce Exposure) and FAST (Fully Assisted Scanner Technology), and single click 3D reconstruction of Dual Energy Scans. The computer system delivered with the CT scanner is able to run the post processing applications optionally.

Here's an analysis of the provided Siemens 510(k) submission for the SOMATOM P45 CT system, focusing on acceptance criteria and supporting studies:

This 510(k) submission is for a software update (SOMARIS/7 VA44) to an existing CT system (SOMATOM P45), not for a novel device. The primary argument for substantial equivalence relies on the fact that the changes are not significant in terms of materials, energy source, or technological characteristics compared to predicate devices. This means that extensive clinical studies with new acceptance criteria, as one might expect for a completely new AI algorithm or diagnostic device, are not detailed in this type of submission.

Therefore, many of the typical acceptance criteria and study details requested in your prompt (e.g., number of experts, adjudication methods, MRMC studies, standalone performance with novel AI) are not applicable to this specific 510(k) summary. The "acceptance criteria" here are primarily met through verification and validation of the software changes and phantom testing to ensure the updated system continues to perform as expected and safely.

Here's a breakdown based on your request, with an emphasis on what is and isn't present in this type of 510(k):

1. Table of Acceptance Criteria and Reported Device Performance

Given that this is a 510(k) for a software update to an existing CT system, the "acceptance criteria" revolve around ensuring the updated system maintains the safety and effectiveness of the predicate device and that the new software functions correctly. The submission states:

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

• Sample Size: Not specified in terms of patient data. The testing mentioned is "non clinical tests" and "phantom testing." This suggests that the "test set" primarily consisted of:
  • Software test cases for verification and validation.
  • Physical phantoms for image quality and performance assessment.
• Data Provenance: Not applicable in the context of clinical patient data for this submission. The tests are "non clinical" and involve "phantom testing" and internal "verification/validation." There's no mention of human subject data, country of origin, or retrospective/prospective clinical data for this specific 510(k) submission.

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

• Not Applicable: This submission does not describe a clinical study requiring human expert assessment for ground truth. Verification and validation of CT system software and phantom performance typically rely on engineering specifications, physical measurements, and image quality metrics, not expert consensus on diagnostic interpretations of patient data.

4. Adjudication Method for the Test Set

• Not Applicable: Since there's no expert-based ground truth establishment described for patient data, no adjudication method would be presented.

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, not specified and highly unlikely for this type of submission. This 510(k) is for a software update to an existing CT scanner, not a novel AI-driven diagnostic aid that would typically require an MRMC study to demonstrate clinical improvement. The new features (CARE, FAST, single-click 3D) are enhancements to existing CT capabilities, not AI for diagnostic interpretation.

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

• Not Applicable for a novel diagnostic algorithm. The "algorithm" here refers to the CT system's operating software for image acquisition, reconstruction, and basic post-processing. Its performance is always "standalone" in the sense that the system itself generates the images, but it's not a standalone diagnostic algorithm in the way a CAD system would be. The focus is on the system's ability to produce images according to specifications, not on its isolated diagnostic performance.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

• Engineering Specifications and Physical Measurements: For the software, the "ground truth" is adherence to its predefined functional and performance specifications. For phantom testing, the "ground truth" would be expected physical measurements, known phantom properties, and established image quality metrics (e.g., spatial resolution, contrast-to-noise ratio, dose efficiency). There is no mention of expert consensus, pathology, or outcomes data, as those are typically relevant for diagnostic interpretation, which is not the focus of this particular 510(k) update.

8. The Sample Size for the Training Set

• Not Applicable: This is not a machine learning or AI algorithm in the contemporary sense that requires a "training set" of data. It's an update to the operating software of a CT scanner. The software would have been developed and tested through traditional software engineering methods.

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

• Not Applicable: As there's no "training set" in the context of machine learning, there's no ground truth established for one. The "ground truth" for software development would be its design specifications and requirements.

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The Siemens SOMATOM Definition Flash system is intended to produce 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.

In addition the SOMATOM Definition Flash is able to produce additional image planes and analysis results by executing optional post processing features, which operate on DICOM images.

The images and results delivered by the system can be used by a trained physician as an aid in diagnosis.

(*spiral planes: the axial planes resulting from the continuous rotation of detectors and x-ray tube, and the simultaneous translation of the patient.)

The Siemens SOMATOM Definition Flash is a Computed Tomography X- ray System, which features two continuously rotating tube-detector systems and functions according to the fan beam principle. The SOMATOM Definition Flash produces CT images in DICOM format, which can be used by post-processing applications commercially distributed by Siemens and other vendors.

The system software is a command-based program used for patient management. data management, X-ray scan control, image reconstruction, and image archive/evaluation. The new version of system software, SOMARIS/7 VA44, allows the reconstruction of images with a slice thickness of 0.5mm for SOMATOM Definition Flash systems equipped with Stellar Detector.

The computer system delivered with the CT scanner is able to run the post processing applications optionally. The Stellar Detector will be offered as an optional upgrade to the cleared SOMATOM Definition Flash CT systems.

The provided text describes a 510(k) submission for the SOMATOM Definition Flash CT System, focusing on modifications introduced with software version SOMARIS/7 VA44. The key modification is the ability to reconstruct 0.5mm slices for systems equipped with Stellar Detector and SAFIRE, providing a z-axis resolution of 0.3mm. The submission details non-clinical testing to support these modifications.

However, the provided text does not contain a table of acceptance criteria or reported device performance metrics in the way typically expected for a detailed study report. Instead, it focuses on demonstrating substantial equivalence to predicate devices through technical characteristic comparisons and non-clinical testing.

Here's an attempt to answer the questions based on the available information, highlighting what is missing or not explicitly stated:

Acceptance Criteria and Device Performance

The document does not explicitly state quantitative acceptance criteria or a table of reported device performance values in the context of a clinical study or a formal validation report against specific performance targets (e.g., sensitivity, specificity for a diagnostic task).

Instead, the "acceptance criteria" appear to be implicit in the non-clinical testing performed, which aimed to confirm the technical capabilities of the new software feature (0.5mm slice reconstruction).

Implicit Acceptance Criteria and Reported Performance (from non-clinical testing):

Study Details

1. Sample size used for the test set and the data provenance:

   • The document describes "phantom tests" to evaluate the 0.5mm slice width.
   • Test Set Sample Size: Not specified. Phantom studies typically involve multiple acquisitions or varying phantom configurations, but the number of "samples" or "cases" is not quantified.
   • Data Provenance: Phantom data (simulated/controlled environment). No human patient data is mentioned for this specific testing.

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

   • Not applicable/Not mentioned. Phantom studies typically do not involve human experts establishing ground truth in the same way clinical studies do. The "ground truth" for phantom measurements is based on the known physical properties and geometry of the phantom and the expected output based on theoretical understanding or established measurement techniques.

3. Adjudication method for the test set:

   • Not applicable. As this was non-clinical phantom testing, no expert adjudication was involved. The measurements are objective physical assessments.

4. 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. The document explicitly states "Nonclinical Testing" and describes phantom tests. There is no mention of an MRMC study or any assessment of human reader performance or AI assistance. This device is a CT scanner, and the modifications are about image reconstruction capabilities, not an AI-assisted diagnostic tool for human readers.

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

   • Yes, to some extent. The non-clinical testing of the software focuses on the performance of the image reconstruction algorithm itself using phantom data (e.g., measuring MTF, spatial resolution). This can be considered a standalone assessment of the algorithm's capability to produce specific image characteristics.

6. The type of ground truth used:

   • For the non-clinical phantom testing, the ground truth is derived from known physical properties and characteristics of the phantoms used (e.g., precisely manufactured structures for resolution assessment) and established measurement methodologies for CT performance.

7. The sample size for the training set:

   • Not applicable/Not mentioned. This submission does not describe an AI model that requires a training set. The software update is for image reconstruction logic and hardware capabilities, not a machine learning algorithm in the typical sense that would necessitate a trained model.

8. How the ground truth for the training set was established:

   • Not applicable. As no training set for an AI model is mentioned, there's no ground truth establishment for such a set.

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The Siemens SOMATOM Definition Flash (with Stellar Detector) system is intended to produce 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.

In addition the SOMATOM Definition Flash (with Stellar Detector) is able to produce additional image planes and analysis results by executing optional post processing features, which operate on DICOM images.

The images and results delivered by the system can be used by a trained physician as an aid in diagnosis.

(*spiral planes: the axial planes resulting from the continuous rotation of detectors and x-ray tube, and the simultaneous translation of the patient.)

The Siemens SOMATOM Definition Flash (with Stellar Detector) is a Computed Tomography X- ray System, which features two continuously rotating tube-detector systems and functions according to the fan beam principle. The system software is a command-based program used for patient management, data management, X-ray scan control, image reconstruction, and image archive/evaluation.

The SOMATOM Definition Flash (with Stellar Detector) produces CT images in DICOM format, which can be used by post-processing applications commercially distributed by Siemens and other vendors.

The computer system delivered with the CT scanner is able to run such post processing applications optionally.

The provided text is a 510(k) summary for the Siemens SOMATOM Definition Flash (with Stellar Detector) Computed Tomography X-ray system. This document focuses on demonstrating substantial equivalence to a predicate device (Siemens SOMATOM FLASH DS, K082220) rather than presenting a study with specific acceptance criteria and detailed performance data of the new device against those criteria in the context of clinical accuracy or diagnostic efficacy.

Therefore, the information requested in your prompt regarding acceptance criteria, device performance, sample sizes, expert qualifications, and detailed study methodologies for assessing the device's diagnostic capabilities is not contained within the provided text.

The document discusses the device's technical specifications, intended use, and general safety and effectiveness concerns related to its design and manufacturing practices, which are typically part of a 510(k) submission for demonstrating substantial equivalence for a medical imaging device. It does not include clinical performance studies with specific metrics like sensitivity, specificity, or reader agreement that would typically be associated with answering the questions you've posed.

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The SOMATOM P45 is intended to produce cross-sectional images of the body by The SOMA TOM 145 13 medical vary ... at different angels or spiral planes* taken at different angles.

(*spiral planes: the axial planes resulting from the continuous rotation of detectors and xray tube, and the simultaneous translation of the patient.)

The Siemens SOMATOM P45 is a whole body X-ray computed tomography system, which I he Slement SOMATON 1-19-15 a wills and functions and functions according to the fan assisted teatures two continuously routing is a command-based program used for paint management, data management, X-ray scan control, image reconstruction, and image archive/evaluation.

The provided document is a 510(k) summary for the Siemens SOMATOM Project P45, a computed tomography (CT) system. The document focuses on establishing substantial equivalence to predicate devices and outlines general safety and effectiveness concerns.

Crucially, this document does NOT contain information about specific acceptance criteria or a study that proves the device meets such criteria in terms of performance metrics like sensitivity, specificity, accuracy, or reader improvement with AI.

The information provided describes the device's classification, general indications for use (producing cross-sectional images), and adherence to safety standards and risk management. It confirms FDA clearance based on substantial equivalence to existing CT systems.

Therefore, I cannot extract the requested information from the provided text. To answer your questions, I would need a different type of document, such as a clinical study report, a performance validation study, or a more detailed technical specification.

Here's a breakdown of why each requested point cannot be answered:

1. A table of acceptance criteria and the reported device performance: Not present. The document focuses on substantial equivalence to predicate devices for its classification as a CT system, not on specific performance metrics.
2. Sample size used for the test set and the data provenance: Not present. No specific test set for performance evaluation is mentioned.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not present. No performance study is described.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not present. No performance study is 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 present. This device is a CT scanner, not an AI-assisted diagnostic tool. An AI component is not mentioned.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not present. This device is a CT scanner, not an algorithm, and no standalone performance testing for diagnostic capabilities is described.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not present. No performance study is described.
8. The sample size for the training set: Not present. This device is a CT scanner, not an AI model requiring a training set in the conventional sense.
9. How the ground truth for the training set was established: Not present. This device is a CT scanner, not an AI model, and no training set is relevant in this context.

In summary, the provided 510(k) document for the SOMATOM Project P45 focuses on regulatory clearance for a general-purpose CT system based on substantial equivalence, and does not contain the detailed performance study information you are seeking.

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