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This computed tomography system is intended to generate and process cross-sectional images of patients by computer reconstruction of x-ray transmission data.

The images delivered by the system can be used by a trained physician as an aid in diagnosis. The images delivered by the system can be used by trained staff as an aid in diagnosis, treatment preparation and radiation therapy planning.

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.

Scan&GO:

The in-room scan application is a planning and information system designed to perform the necessary functions required for planning and controlling scans of supported SIEMENS CT scanners. It allows users to work in close proximity to the scanner.

The in-room scan application runs on standard information technology hardware and software, utilizing the standard information technology operating systems and user interface. Communication and data exchange are done using special protocols.

The subject device SOMATOM go.Platform with SOMARIS/10 syngo CT VA30 are Computed Tomography Xray Systems which feature one continuously rotating tube-detector system and function according to the fan beam principle. The SOMATOM go.Platform with Software SOMARIS/10 syngo CT VA30 produces CT images in DICOM format, which can be used by trained staff for post-processing applications commercially distributed by Siemens Healthcare and other vendors as an aid in diagnosis, treatment preparation and therapy planning support (including, but not limited to, Brachytherapy, Particle including Proton Therapy, External Beam Radiation Therapy, Surgery). The computer system delivered with the CT scanner is able to run optional post processing applications.

The Scan&GO mobile workflow is an optional planning and information software designed to perform the necessary functions required for planning and controlling of the SOMATOM go.Platform CT scanners. Scan&GO can be operated on a Siemens provided tablet or a commercially available tablet that meets certain minimum technical requirements. It allows users to work in the scanner and the patient.

The provided text describes acceptance criteria and testing for the Siemens SOMATOM go.Platform CT Scanners with software version SOMARIS/10 syngo CT VA30, and the Scan&GO mobile medical application.

Here's the breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document details various non-clinical tests conducted, with statements of the test results meeting the acceptance criteria. However, it does not present a single consolidated table of specific, quantifiable acceptance criteria alongside reported performance values for those criteria. Instead, it offers narrative summaries of the testing and its outcomes, indicating successful verification and validation.

Below is a table constructed from the provided text, outlining the features tested and the reported performance (which is generally stated as "met acceptance criteria" or "similar/improved performance").

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

• Check&GO Testing:

  • Sample size: 500 CT-series from 100 patients.
  • Data provenance: Not explicitly stated, but clinical datasets were used ("clinical datasets from 100 patients"). It's specified as a "bench test," which implies it was likely retrospective from an existing data archive. Country of origin is not mentioned.

• Other Non-Clinical Testing (Phantom, Integration, Functional): The document frequently refers to "phantom images," "test levels," "development activities," and "bench tests." No specific sample sizes for these tests (e.g., number of phantom scans) or data provenance are provided beyond the general descriptions of the tests themselves, which are stated as having been conducted "during product development."

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

• Check&GO Testing:

  • Ground Truth Establishment: The datasets were "manually annotated with a detailed GT contrast-state (None, Low, InhomogeneousLow, Standard, InhomogeneousHigh, High)."
  • Number & Qualifications of Experts: Not specified.

• Other Tests: For other tests, such as those involving image quality or physical measurements (e.g., Flex 4D Spiral, DirectDensity), the ground truth is typically derived from physical measurements, reference standards (e.g., known phantom properties), or established technical specifications, rather than expert consensus on clinical interpretation. The document does not mention the use of experts to establish ground truth for these tests. The indication for the new "Kidney Stones" feature notes: "Only a well-trained radiologist can make the final diagnosis under consideration of all available information," suggesting the involvement of radiologists in the clinical context, but not for ground truth establishment specifically for the device's technical validation.

4. Adjudication method for the test set

• The document does not explicitly describe an adjudication method (like 2+1, 3+1, etc.) for any of the test sets. For the Check&GO test, ground truth was "manually annotated," implying a single process for ground truth establishment rather than a consensus/adjudication method among multiple experts.

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 comparative effectiveness study involving human readers and AI assistance is reported for this device in the provided text. The device itself is a CT scanner system and its associated software, not explicitly an AI-assisted diagnostic tool for interpretation in collaboration with human readers. The Check&GO feature is described as "aiding the user," but no study on human performance improvement is included.

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

• The non-clinical performance testing, particularly phantom studies and specific feature evaluations like "kV and Filter independent CaScore," "Recon&GO - Spectral Recon," "TwinSpiral Dual Energy," "Flex 4D Spiral," "DirectDensity," "HD FoV," and "InjectorCoupling," can be considered standalone algorithm/device performance evaluations. These tests assess the technical output and accuracy of the device and its software features independent of human interpretation or interaction during the measurement process. The Check&GO feature's "Bench Test" also evaluates the algorithm's performance against annotated ground truth.

7. The type of ground truth used

• Check&GO: Expert annotation of "detailed GT contrast-state" (None, Low, InhomogeneousLow, Standard, InhomogeneousHigh, High) for 500 CT series.
• Other Feature Tests (e.g., CaScore, Spectral Recon, Flex 4D Spiral, DirectDensity, HD FoV, TwinSpiral DE): Primarily derived from physical phantom measurements, comparison to established technical specifications, or reference images/algorithms (e.g., comparing to initial release versions or conventional 120kV images).
• National Lung Screening Trial (NLST): Outcomes data from a large clinical trial (N Engl J Med 2011; 365:395-409) is cited to support the "low dose lung cancer screening" indication for use, not for direct ground truth establishment during this device's specific validation, but rather as supportive clinical literature for the screening concept itself.

8. The sample size for the training set

• The document does not specify any training set sizes. The studies described are primarily for verification and validation, not for training machine learning models. The Check&GO feature describes "500 CT-series from 100 patients were used for the testing of the algorithm," but this is explicitly called "testing," not training.

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

• Since no training set details (size or establishment method) are provided, this information is not available in the document.

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uWS-CT is a software solution intended to be used for viewing, manipulation, communication, and storage of medical images. It supports interpretation and evaluation of examinations within healthcare institutions. It has the following additional indications:

The CT Oncology application is intended to support fast-tracking routine diagnostic oncology, staging, and follow-up, by providing a tool for the user to perform the segmentation and volumetric evaluation of suspicious lesions in lung or liver.
The CT Colon Analysis application is intended to provide the user a tool to enable easy visualization and efficient evaluation of CT volume data sets of the colon.
The CT Dental application is intended to provide the user a tool to reconstruct panoramic and paraxial views of jaw.
The CT Lung Density Analysis application is intended to segment pulmonary, lobes, and airway, providing the user quantitative parameters, structure information to evaluate the lung and airway.
The CT Lung Nodule application is intended to provide the user a tool for the review and analysis of thoracic CT images, providing quantitative and characterizing information about nodules in the lung in a single study, or over the time course of several thoracic studies.
The CT Vessel Analysis application is intended to provide a tool for viewing, manipulating, and evaluating CT vascular images.
The Inner view application is intended to perform a virtual camera view through hollow structures (cavities), such as vessels.
The CT Brain Perfusion application is intended to calculate the parameters such as: CBV, CBF, etc. in order to analyze functional blood flow information about a region of interest (ROI) in brain.
The CT Heart application is intended to segment heart and extract coronary artery. It also provides analysis of vascular stenosis, plaque and heart function.
The CT Calcium Scoring application is intended to identify calcifications and calculate the calcium score.
The CT Dynamic Analysis application is intended to support visualization of the CT datasets over time with the 3D/4D display modes.
The CT Bone Structure Analysis application is intended to provide visualization and labels for the ribs and spine, and support batch function for intervertebral disk.
The CT Liver Evaluation application is intended to provide processing and visualization for liver segmentation and vessel extraction. It also provides a tool for the user to perform liver separation and residual liver segments evaluation.

uWS-CT is a comprehensive software solution designed to process, review and analyze CT studies. It can transfer images in DICOM 3.0 format over a medical imaging network or import images from external storage devices such as CD/DVDs or flash drives. These images can be functional data, as well as anatomical datasets. It can be at one or more time-points or include one or more time-frames. Multiple display formats including MIP and volume rendering and multiple statistical analysis including mean, maximum and minimum over a user-defined region is supported. A trained, licensed physician can interpret these displayed images as well as the statistics as per standard practice.

The provided document, a 510(k) summary for the uWS-CT software, does not contain detailed information about specific acceptance criteria and the results of a study proving the device meets these criteria in the way typically required for AI/ML-driven diagnostics.

The document primarily focuses on demonstrating substantial equivalence to a predicate device (uWS-CT K173001) and several reference devices for its various CT analysis applications. It lists the functions of the new and modified applications (e.g., CT Lung Density Analysis, CT Brain Perfusion, CT Heart, CT Calcium Scoring, CT Dynamic Analysis, CT Bone Structure Analysis, CT Liver Evaluation) and compares them to those of the predicate and reference devices, indicating that their functionalities are "Same."

While the document states that "Performance data were provided in support of the substantial equivalence determination" and lists "Performance Evaluation Report" for various CT applications, it does not provide the specifics of these performance evaluations, such as:

• Acceptance Criteria: What specific numerical thresholds (e.g., accuracy, sensitivity, specificity, Dice score for segmentation) were set for each function?
• Reported Device Performance: What were the actual measured performance values?
• Study Design Details: Sample size, data provenance, ground truth establishment methods, expert qualifications, adjudication methods, or results of MRMC studies.

The document explicitly states:

• "No clinical study was required." (Page 16)
• Software Verification and Validation was provided, including hazard analysis, SRS, architecture description, environment description, and cyber security documents. However, these are general software development lifecycle activities and not clinical performance studies.

Therefore, based solely on the provided text, I cannot fill out the requested table or provide the detailed study information. The document suggests that the performance verification was focused on demonstrating functional equivalence rather than presenting quantitative performance metrics against pre-defined acceptance criteria in a clinical context.

Summary of what can be extracted and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance: This information is not provided in the document. The document states "Performance Evaluation Report" for various applications were submitted, but the content of these reports (i.e., the specific acceptance criteria and the results proving they were met) is not included in this 510(k) summary.

2. Sample size used for the test set and the data provenance: This information is not provided. The document states "No clinical study was required." The performance evaluations mentioned are likely internal verification and validation tests whose specifics are not detailed here.

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. Given that "No clinical study was required," it's unlikely a formal multi-expert ground truth establishment process for a clinical test set, as typically done for AI/ML diagnostic devices, was undertaken for this submission in a publicly available manner.

4. Adjudication method (e.g. 2+1, 3+1, none) 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: This information is not provided. The document explicitly states "No clinical study was required."

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The document states this is a "software solution intended to be used for viewing, manipulation, and storage of medical images" that "supports interpretation and evaluation of examinations within healthcare institutions." The listed applications provide "a tool for the user to perform..." or "a tool for the review and analysis..." which implies human-in-the-loop use. Standalone performance metrics are not provided.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): This information is not provided.

8. The sample size for the training set: This information is not provided. The document is a 510(k) summary for a software device, not a detailed technical report on an AI/ML model's development.

9. How the ground truth for the training set was established: This information is not provided.

In conclusion, the supplied document is a regulatory submission summary focused on demonstrating substantial equivalence based on intended use and technological characteristics, rather than a detailed technical report of performance studies for an AI/ML device with specific acceptance criteria and proven results. For this type of information, one would typically need access to the full 510(k) submission, which is not publicly available in this format, or a peer-reviewed publication based on the device's clinical performance.

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Your MAGNETOM MR system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal and oblique cross sectional images, spectroscopic images and/or spectra, and that displays the internal structure and/or function of the head, body, or extremittes. Other physical parameters derived from the images and/or spectra may also be produced. Depending on the region of interest, contrast agents may be used.

These images and/or spectra and the physical parameters derived from the images and/or spectra when interpreted by a trained physician vield information that may assist in diagnosis.

Your MAGNETOM MR system may also be used for imaging during interventional procedures when performed with MR compatible devices such as in-room displays and MR Safe biopsy needles.

Software syngo MR XA12M is the latest software version for MAGNETOM Amira and MAGNETOM Sempra. It supports the existing "A Tim+Dot system" configuration for MAGNETOM Amira and MAGNETOM Sempra, and the newly introducted "A BioMatrix system" configuration for MAGNETOM Amira. Software version syngo MR XA12M for MAGNETOM Amira and MAGNETOM Sempra includes software applications migrated from the secondary predicate device MAGNETOM Sola with syngo MR XA11A (K181322). Only minor adaptations were needed to support the system specific hardware and optimize the sequences/protocols. In addition, new software features, Segmented TOF, HASTE with variable flip angle, SMS in RESOLVE and QDWI, are also introduced in syngo XA12M. The device also includes hardware updates such as new/modified coils and other components.

This document describes the 510(k) premarket notification for the Siemens MAGNETOM Amira and MAGNETOM Sempra Magnetic Resonance Diagnostic Devices (MRDD) with software syngo MR XA12M. The submission aims to demonstrate substantial equivalence to previously cleared predicate devices.

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria for this device are not explicitly stated in terms of specific performance metrics (e.g., sensitivity, specificity, accuracy). Instead, substantial equivalence is claimed based on adherence to recognized standards, verification and validation testing, and image quality assessments. The reported device performance is broadly presented as performing "as intended" and exhibiting an "equivalent safety and performance profile" to the predicate devices.

The table below summarizes the technological changes and the general assessment of their performance as described in the submission:

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

The document does not specify a distinct "test set" with a quantifiable sample size (e.g., number of patients or images). The evaluation relies on "sample clinical images" for the new coils and software features. The provenance of this data (e.g., country of origin, retrospective or prospective collection) is also not detailed.

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

The document does not explicitly state the number of experts used or their specific qualifications for establishing ground truth for the "sample clinical images." The indication for use mentions that "images and/or spectra and the physical parameters derived from the images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis." This implies that the interpretation of images, including those used in performance testing, would be by a "trained physician," but no specific details are provided about the number or expertise of such individuals in the context of validating the device features.

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1, none) for the "sample clinical images" used in performance testing.

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

No MRMC comparative effectiveness study is mentioned. The submission focuses on demonstrating substantial equivalence through non-clinical performance testing and adherence to standards, rather than evaluating the improvement of human readers with or without AI assistance.

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

The primary purpose of the device (MAGNETOM MR system) is to produce images, and the software features enhance image acquisition and processing. The performance testing described (image quality assessments, software verification and validation) evaluates the algorithm's output (images) in a standalone manner prior to a physician's interpretation. However, the device's indications for use inherently involve human interpretation ("when interpreted by a trained physician"). The document does not describe a purely "algorithm-only" performance assessment in the context of clinical decision-making, as the device's function is to aid diagnosis by a human.

7. The Type of Ground Truth Used

The type of ground truth for the "sample clinical images" is not explicitly stated. Given that no clinical trials were conducted, it's highly probable that qualitative "image quality assessments" were made by internal experts or against known phantom/in-vivo characteristics, and potentially compared to images from the predicate device. There is no mention of pathology, expert consensus (beyond general physician interpretation), or outcomes data being used as ground truth for this submission.

8. The Sample Size for the Training Set

The document does not mention a "training set" in the context of machine learning or AI algorithms. The changes are largely software and hardware updates, along with the integration of existing features from a predicate device. If any of the new features (e.g., Segmented ToF, HASTE with variable flip angle) involve learned components, the training set size and characteristics are not disclosed.

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

Since no training set is discussed or implied for machine learning, the method for establishing ground truth for a training set is not described. The software and hardware updates appear to be based on engineering development and optimization rather than machine learning model training.

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Your MAGNETOM system is indicated for use as a magnetic device (MRDD) that produces transverse, sagittal, coronal and oblique cross sectional images and/or spectra, and that displays the internal structure and/or function of the head, body, or extremities. Other physical parameters derived from the images and/or spectra may also be produced. Depending on the region of interest, contrast agents may be used. These inages and/ or spectra and the physical parameters derived from the images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

Your MAGNETOM system may also be used for imaging during interventional procedures when performed with MR compatible devices such as in-room displays and MR Safe biopsy needles.

MAGNETOM Lumina with software syngo MR XA11B includes modified hardware and software compared to the predicate device, MAGNETOM Vida with syngo MR XA11A. A high level summary of the modified features is provided below:

Hardware
Modified Hardware

• -Gradient system with XK gradient engine (36/200): Reduction in GPA performance with unchanged hardware components
• -Cover: Adapted system design
• Tim [180x32] configuration: patient table with 180 simultaneous connectable coil elements

Software
New Features and Applications

• GOLiver: Set of optimized pulse sequences for fast and efficient imaging of the abdomen / liver. It is designed to provide consistent exam slots and to reduce the workload for the user in abdominal / liver MRI.
  Other Modifications and / or Minor Changes
• Turbo Suite marketing bundle: Turbo Suite is a marketing bundle of components for accelerated MR imaging offered for the MAGNETOM Lumina MR system.

Here's a breakdown of the acceptance criteria and study information for the MAGNETOM Lumina device, based on the provided document:

This document does not describe the specific acceptance criteria or a detailed clinical study demonstrating the device's performance in a way that typically includes metrics like sensitivity, specificity, or AUC, as would be expected for an AI/algorithm-based diagnostic tool. Instead, this 510(k) summary focuses on demonstrating substantial equivalence to a predicate device (MAGNETOM Vida) through non-clinical testing and adherence to recognized standards.

The "device" in question (MAGNETOM Lumina) is a Magnetic Resonance Diagnostic Device (MRDD), an MRI scanner, not an AI-powered diagnostic algorithm in the sense of providing specific disease detection or quantification with performance metrics. The new software feature "GOLiver" within the MAGNETOM Lumina is described as a set of optimized pulse sequences for imaging, designed to improve workflow, not an AI for diagnosis.

Therefore, many of the requested elements (like effect size of AI assistance, sample size for test set with ground truth, expert qualifications for ground truth, adjudication methods) are not applicable or not provided in the context of this 510(k) submission, which is for an MRI scanner itself.

However, I can extract information related to the closest aspects of acceptance criteria and testing that are present:

Acceptance Criteria and Device Performance for MAGNETOM Lumina

Given that the device is an MRI system (not an AI diagnostic algorithm), the acceptance criteria and performance evaluation are centered on safety, functionality, and image quality compared to a predicate device, rather than diagnostic accuracy metrics of an AI.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not explicitly stated as a number of patients or cases in the typical sense for an AI diagnostic study. The document mentions "Sample clinical images were taken for the hardware and software feature." This implies a set of images, but the quantity or characteristics of these images are not detailed.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). The phrase "Sample clinical images were taken" suggests existing data, but further details are absent.

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

• Not applicable/not provided in the context of this submission. The "image quality assessment" was performed by implicitly qualified personnel comparing images, but there is no mention of a formal "ground truth" establishment by multiple experts with specific qualifications to evaluate diagnostic accuracy metrics typically derived from AI output.

4. Adjudication Method for the Test Set

• Not applicable/not provided. No formal adjudication method like 2+1 or 3+1 is mentioned, as this is not a study assessing diagnostic accuracy outcomes from an AI.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and Effect Size

• No MRMC comparative effectiveness study was explicitly done to evaluate how human readers improve with AI vs. without AI assistance. The document refers to "MAGNETOM Lumina" as an MRI system, not an AI-assisted diagnostic tool for interpretation. The software feature (GOLiver) is for optimized image acquisition, minimizing user workflow in abdominal/liver MRI, not for diagnostic assistance to human readers.

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

• Not applicable. The MAGNETOM Lumina is an MRI device, which acquires images for a human to interpret. It is not a standalone algorithm meant to provide a diagnosis without human interaction.

7. The Type of Ground Truth Used

• For the "Image quality assessment of the new set of pulse sequences (GOLiver)," the "ground truth" implicitly referred to was a comparison against the image quality produced by the pulse sequences of the predicate device. This is a comparison of technical image characteristics rather than a clinical ground truth (e.g., pathology, surgical findings, long-term outcomes for disease presence).

8. The Sample Size for the Training Set

• Not applicable/not provided. The device is an MRI scanner. While there is software, the document doesn't describe an AI model that underwent "training" in the machine learning sense with a specific training set to learn diagnostic patterns. The "GOLiver" feature is described as "optimized pulse sequences," which implies engineering and parameter tuning, not machine learning model training.

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

• Not applicable/not provided. As there's no mention of a traditional "training set" for an AI model, the concept of establishing ground truth for it is not relevant to this document.

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Your MAGNETOM system is indicated for use as a magnetic device (MRDD) that produces transverse, sagittal, coronal and oblique cross sectroscopic images and or spectra, and that displays the internal structure and/or function of the head, body, or extremities. Other physical parameters derived from the images and/or spectra may also be produced. Depending on the region of interest, contrast agents may be used. These images and/ or spectra and the physical parameters derived from the images and/or spectra when interpreted by a trained physician yicld information that may assist in diagnosis.

Your MAGNETOM system may also be used for imaging during interventional procedures when performed with MR compatible devices such as in-room displays and MR Safe biopsy needles.

MAGNETOM Vida with software syngo MR XA11B includes new and modified component, features and software compared to the predicate device, MAGNETOM Vida with syngo MR XA11A. A high level summary of the new and modified features is provided below:

Hardware
New Hardware

• Nose Marker for Inline Motion Correction
  Software
  New Features and Applications
• TFL with Inline Motion Correction: Tracking of motion of the head during 3D MPRAGE head scans with a nose marker and a camera system. The MR system uses the tracking information to compensate for the detected motion.
• i GOLiver: GOLiver is a set of optimized pulse sequence for fast and efficient imaging of the abdomen / liver. It is designed to provide consistent exam slots and to reduce the workload for the user in abdominal / liver MRI.
• TSE_MDME: A special variant of the TSE pulse sequence type which acquires several contrasts (with different TI and TE, i.e. Multi Delay Multi Echo) within a single sequence.
• SEMAC: SEMAC is a method for metal artifact correction in ortho imaging of patients i with whole joint replacement. Using Compressed Sensing the acquisition can be accelerated.
• Angio TOF with Compressed Sensing: The Compressed Sensing (CS) functionality is now available for TOF MRA within the BEAT pulse sequence type. Scan time can be reduced by an incoherent undersampling of k-space data. The usage of CS as well as the acceleration factor and further options can be freely selected by the user.
• SMS for RESOLVE and QDWI: Simultaneous excitation and acquisition of multiple i slices with the Simultaneous multi-slice (SMS) technique for readout-segmented echo planar imaging (RESOLVE) and quiet diffusion weighted imaging (QDWI).
• i SPACE with Compressed Sensing: The Compressed Sensing (CS) functionality is now available for the SPACE pulse sequence type. Scan time can be reduced by the incoherent under-sampling of the k-space data. The usage of CS as well as the acceleration factor and other options can be freely selected by the user.
• RT Respiratory self-gating for FL3D_VIBE: Non-contrast abdominal and thoracic i examination in free breathing with reduced blur induced by respiratory motion.
  Other Modifications and / or Minor Changes
• i Turbo Suite is a marketing bundle of components for accelerated MR imaging offered for the MAGNETOM Vida MR systems.
• i Noise masking: a mechanism to remove the noise floor in outer regions is now available for RESOLVE and QDWI.

The provided FDA 510(k) summary for the MAGNETOM Vida (K183254) does not contain the specific details for acceptance criteria and a study proving the device meets those criteria, as typically seen for AI/ML-based medical devices or devices with new diagnostic functionalities.

This 510(k) is for an updated version of an existing MRI system (MAGNETOM Vida with software syngo MR XA11B) compared to its predicate (MAGNETOM Vida with syngo MR XA11A). The changes primarily involve new hardware (1 mention) and several new or modified software features for image acquisition and processing (e.g., motion correction, optimized pulse sequences, metal artifact correction, accelerated imaging techniques).

The document states: "No clinical tests were conducted to support substantial equivalence for the subject device; however, sample clinical images were provided to support the new/modified component and software features per the FDA guidance document 'Submission of Premarket Notifications for Magnetic Resonance Diagnostic Devices', dated November 18, 2016."

This indicates that the primary focus of the submission was on demonstrating that the new features maintain the safety and performance profile of the predicate device through non-clinical testing (image quality assessments, software verification/validation) and by providing sample clinical images (not a formal clinical study with acceptance criteria).

Therefore, I cannot populate the requested table and answer many of the questions directly from the provided text because such a detailed study with acceptance criteria, ground truth, expert readers, and effect sizes was not performed or described in this 510(k) submission for this specific device clearance.

Below, I will indicate which information is not present in the document and briefly explain why, based on the nature of this 510(k) (which is for an updated MRI system, not an AI/ML diagnostic algorithm).

Acceptance Criteria and Study for MAGNETOM Vida (K183254)

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size for Test Set: NOT PRESENT. The document mentions "sample clinical images were provided," but it does not specify the number of images or patients used for these samples, nor does it describe a formal "test set" in the context of a diagnostic performance study.
• Data Provenance (country of origin, retrospective/prospective): NOT PRESENT. The origin of the "sample clinical images" is not specified.

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

• NOT APPLICABLE/NOT PRESENT. Since no formal clinical study with a defined "test set" and "acceptance criteria" was described for diagnostic performance, there's no mention of experts establishing ground truth for such a study. The product is an MRI system, and interpretations are made by "trained physicians."

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

• NOT APPLICABLE/NOT PRESENT. No formal adjudication method is described, as no specific clinical diagnostic performance test set requiring such expert consensus was presented in this 510(k).

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. An MRMC study was not conducted or described for this submission. This device is an MRI scanner with new and modified acquisition and processing features, not an AI-assisted diagnostic tool that directly aids human readers to improve diagnostic accuracy.

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

• NO. This is an MRI system. Its "performance" is inherently tied to image acquisition and quality, which are then interpreted by a human. It does not perform a standalone diagnostic function like an AI algorithm for disease detection.

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

• NOT APPLICABLE/NOT PRESENT. No formal ground truth definition is provided for a clinical performance study since one was not conducted for the purpose of demonstrating substantial equivalence. The device's performance was evaluated through non-clinical tests (e.g., image quality assessments).

8. The sample size for the training set:

• NOT APPLICABLE/NOT PRESENT. The document describes software modifications including some advanced imaging techniques (e.g., Compressed Sensing). While these might involve optimization based on data, the submission does not describe a traditional "training set" in the context of an AI/ML algorithm development or a diagnostic study. The software development and testing follow IEC 62304 and other relevant standards.

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

• NOT APPLICABLE/NOT PRESENT. As no "training set" in the context of diagnostic AI/ML was described, no information on its ground truth establishment is provided.

Ask a specific question about this device

Your MAGNETOM system is indicated for use as a magnetic device (MRDD) that produces transverse, sagittal, coronal and oblique cross sectional images and/or spectra, and that displays the internal structure and/or function of the head, body, or extremities. Other physical parameters derived from the images and/or spectra may also be produced. Depending on the region of interest, contrast agents may be used. These inages and/ or spectra and the physical parameters derived from the images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

Your MAGNETOM system may also be used for imaging during interventional procedures when performed with MR compatible devices such as in-room displays and MR Safe biopsy needles.

MAGNETOM Vida with software syngo MR XA11A includes new and modified hardware and software compared to the predicate device, MAGNETOM Vida with syngo MR XA10A. A high level summary of the new and modified features is provided below:

Hardware
New Hardware

• New coils:
• BM Body 12 |
• BM Spine 24 |
• | Head/Neck 16
• -Head 32 MR Coil 3T
• Other components:
• camera —
• computer
• Multi-Channel Interface —

Modified Hardware

• Main components such as 32 independent RF channels -
• -Other components such as Tx-Box / RF filter plate / transmit system

Software
New Features and Applications

• GOKnee3D (examination comprising the AutoAlign knee localizer and two SPACE with CAIPIRINHA sequences to support fast high-resolution 3D exams of the knee)
• SPACE with CAIPIRINHA (3D SPACE pulse sequence type with the iPAT mode CAIPIRINHA)
• GOBrain (brain examination in short acquisition time)
• GOBrain+ (adaptation of GOBrain pulse sequences)
• | MR Breast Biopsy (supports planning and execution of MR guided breast biopsies and wire localizations)
• | MRSim / Synthetic CT (provides MR pulse sequences for the creation of Synthetic CT images based on the MR image input)
• Cardiac Dot Flow Add-In (extension of Cardiac Dot Engine to support blood flow measurements)
• PCASL mode (extension of ASL pulse sequence types by a new blood labeling mode)
• SMS in TSE (Simultaneous Multi Slice (SMS) support for TSE)

Modified Features and Applications

• | SliceAdjust (the framework support was extended to include additional pulse sequence types)

• RetroGating (Compressed Sensing Cardiac Cine acquisitions which split the data acquisition over multiple heartbeats can now be configured to perform complete sampling of the cardiac cycle without prior definition of an acquisition window. Combination with arrhythmia rejection is possible.)

• iPAT / TSE Reference Scan (Changes in the TSE, FAST TSE and TSE DIXON pulse sequence types includes the possibility to use a reference scan "TSE/Separate" for GRAPPA acquisition and reconstruction)

• Care Bolus in Angio Dot Engine (workflow support for bolus administration (bolus detection))

• MRCP in SPACE (improvement of the image quality for MR Cholangiopancreatography (MRCP) acquisitions based on the SPACE pulse sequence type)

• MR Elastography:

• Replacement of existing masking by masking performed on the prescan images used within the prescan/normalize (PSN) functionality.

• Optimization of pulse sequence type timing. |

• Changes in MEG time period (no longer fixed to the wavelength of the | MEG and also implementation of a reduced MEG period)

• Respiratory Sensor Support (additional support for respiratory triggered measurements is provided in several SE-, GRE- and EPI-based pulse sequence types)

Modified (general) Software / Platform

• ー Single and dual monitor workflow (In the single monitor setup the features of the LHS monitor and RHS monitor are provided on separate tab-cards)
• Touch positioning (Select&GO 2.0) (extension to additional body area positions when dedicated coils are plugged in)
• Dot Cockpit (additional features for handling of scan pulse sequences and offline Dot Cockpit)
• MR View&GO (Addition of Mosaic View (view mode to scroll through dimensions | instead of space) and 4D Movie Toolbar (movie toolbar to navigate the 4th dimension))

Other Modifications and / or Minor Changes

• teamplay Protocols Interface (interface to support external pulse sequences | management systems)

• Unilateral Hip (added in Large Joint Dot Engine) (user workflow optimized, since information/settings are taken from the patient registration)

• GRE RefScan (external GRE RefScan has been extended to multiple pulse sequence types)

• Asymmetric saturation pulses (support for regional saturation with an asymmetric shape has been added for BOLD imaging)

• CP Mode modification ("RF Transmit Mode" is provided as part of the patient registration based on IEC 60601-2-33)

• SPAIR FatSat (new "SPAIR Breast" mode in several pulse sequence types and extension of "Abdomen&Pelvis" and "Thorax" modes)

• Compressed Sensing GRASP-VIBE (improvement of SPAIR fat saturation performance)

• MAGNETOM RT Pro Edition marketing bundle (extension of the bundle)

• Siemens "BioMatrix" (extension with additional components)

The provided text is a 510(k) Summary for the Siemens MAGNETOM Vida MRI system (K181433). It describes the device, its intended use, and compares it to a predicate device (MAGNETOM Vida with syngo MR XA10A). However, this document primarily focuses on establishing substantial equivalence based on non-clinical testing and adherence to standards, rather than clinical performance studies with acceptance criteria in the typical sense for AI/CADe devices.

Therefore, many of the requested details regarding acceptance criteria, clinical study design (sample size, expert qualifications, adjudication, MRMC studies, standalone performance), and ground truth establishment (especially for AI/ML models) are not present in this document. This is because this submission is for an MRI system, not an AI/CADe device. It focuses on hardware and software modifications of a diagnostic imaging machine, not on an algorithm that interprets images.

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

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

The document discusses "performance testing" but does not provide specific quantitative acceptance criteria or detailed reported performance in a table format as might be expected for an AI/CADe device. Instead, the "acceptance" is qualitative:

2. Sample sized used for the test set and the data provenance:

• Test Set Sample Size: Not explicitly stated. The document mentions "sample clinical images" were taken for the new coils and software features, but no specific number of patients or images is given.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective).
• Retrospective or Prospective: Not specified.

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

• Number of Experts: Not specified.
• Qualifications of Experts: The device's output is "interpreted by a trained physician," implying that physicians are involved in assessing the images, but their specific role in establishing "ground truth" for the non-clinical tests is not detailed. For this type of MRI system submission, ground truth isn't established in the same way as for an AI interpretation algorithm. The "truth" is the physical output of the MRI system.

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

• Not applicable/Not specified. This level of detail on ground truth adjudication is typically for AI/CADe clinical studies, not MRI system performance tests focused on image quality and safety.

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 was done. The document explicitly states: "No clinical tests were conducted to support substantial equivalence for the subject device". This is not an AI-assisted reading device, but a diagnostic imaging system.

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

• Not applicable. This is an MRI device, not an AI algorithm. "Performance tests" were done on the device itself and its components (e.g., image quality assessments).

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

• For an MRI system, the "ground truth" for non-clinical testing refers to the physical and technical performance of the device (e.g., image clarity, signal-to-noise ratio, spatial resolution, adherence to safety limits). It is not about diagnostic accuracy against a clinical ground truth like pathology. The comparison is made against prior versions/predicate devices and established industry standards for image quality and safety.

8. The sample size for the training set:

• Not applicable. This document does not describe an AI/ML algorithm that requires a training set.

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

• Not applicable. As above, no AI/ML training set is discussed.

In summary: The provided document is a 510(k) summary for a Magnetic Resonance Diagnostic Device (MRDD), the MAGNETOM Vida MRI system. Its purpose is to demonstrate substantial equivalence to a legally marketed predicate device based on non-clinical performance testing (e.g., image quality assessments, software verification/validation) and conformity to recognized standards (e.g., IEC, ISO, NEMA). It explicitely states that no clinical tests were conducted for this submission. Therefore, the detailed requirements for AI/CADe device performance studies (like MRMC, training/test set ground truth, expert adjudication, etc.) are not addressed in this document because they are not relevant to this specific type of medical device submission.

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Your MAGNETOM system is indicated for use as a magnetic device (MRDD) that produces transverse, sagittal, coronal and oblique cross sectional images and/or spectra, and that displays the internal structure and/or function of the head, body, or extremities. Other physical parameters derived from the images and/or spectra may also be produced. Depending on the region of interest, contrast agents may be used. These inages and/ or spectra and the physical parameters derived from the images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

Your MAGNETOM system may also be used for imaging during interventional procedures when performed with MR compatible devices such as in-room displays and MR Safe biopsy needles.

MAGNETOM Sola with software syngo MR XA11A is similar to the previous cleared predicate device MAGNETOM Aera with syngo MR E11C (K153343) but includes new and modified hardware and software compared to MAGNETOM Aera. A high level summary of the hardware and software changes is included below.

The provided text describes the Siemens MAGNETOM Sola, a Magnetic Resonance Diagnostic Device (MRDD), and its journey through FDA clearance via a 510(k) premarket notification (K181322). The submission argues for substantial equivalence to a predicate device, MAGNETOM Aera (K153343). However, the document does not include a table of acceptance criteria or report device performance against specific metrics as requested. It outlines the scope of changes, safety testing, and refers to clinical images and a specific clinical study for nerve stimulation thresholds, but it doesn't detail performance-based acceptance criteria for image quality or diagnostic accuracy in the way typically seen for AI/ML devices.

Here's an attempt to answer the questions based only on the provided text, highlighting where information is absent:

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

The document does not provide a table of acceptance criteria or specific reported device performance metrics against such criteria in the context of diagnostic accuracy or image quality improvements. The submission focuses on demonstrating substantial equivalence through:

• Similar intended use to the predicate device.
• Conformity to relevant standards (IEC, ISO, NEMA).
• Software verification and validation.
• Sample clinical images to support new/modified features.
• A clinical study to determine nerve stimulation thresholds for gradient system output.

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

• Sample size for test set:
  • For the nerve stimulation thresholds study: 36 individuals.
  • For testing new/modified pulse sequences and algorithms, and supporting new coils/features: "Sample clinical images" were taken, but the exact number of cases or individuals is not specified.
• Data provenance: Not specified (e.g., country of origin, retrospective or prospective). The text only mentions "Sample clinical images were taken" and "A clinical study... was conducted."

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

The document does not specify the number or qualifications of experts used to establish ground truth for image quality assessments or the clinical images provided. The nerve stimulation study likely involved medical professionals, but their role in "ground truth" establishment for diagnostic purposes is not detailed.

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

The document does not describe any adjudication method for the test set.

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 document. The device is a Magnetic Resonance Diagnostic Device, not explicitly an AI/ML-driven diagnostic aid that would directly assist human readers in interpretation or diagnosis in the context typically seen in MRMC studies for AI.

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

The document describes the MAGNETOM Sola as a "magnetic resonance diagnostic device" which produces images and/or spectra that, "when interpreted by a trained physician, yield information that may assist in diagnosis." This indicates a human-in-the-loop system, implying that a standalone "algorithm only" performance study for direct diagnostic output was not the primary focus or perhaps applicable in the traditional sense for this device submission which is for the MR system itself rather than an AI-driven interpretation tool. However, the software verification and validation are for the algorithm within the system.

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

The type of ground truth used for image quality assessments or for the "sample clinical images" is not explicitly stated. For the nerve stimulation study, the "observed parameters were used to set the PNS (Peripheral Nerve Stimulation) threshold level," which seems to be a physiological measurement rather than a diagnostic ground truth.

8. The sample size for the training set

The document does not mention a training set sample size. This type of information is typically provided for AI/ML models that undergo specific training, which isn't the primary focus of this MRDD system clearance description.

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

Since a training set is not mentioned, the method for establishing its ground truth is also not provided.

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syngo.via MI Workflows are medical diagnostic applications 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.

syngo.via MI Workflows enable visualization of information that would otherwise have to be visually compared disjointedly. syngo.via MI Workflows provide analytical tools to help the user assess, and document changes in morphological or functional activity at diagnostic and therapy follow-up examinations. syngo.via MI Workflows can perform harmonization of SUV (PET) across different PET systems or different reconstruction methods.

syngo.via MI workflows support the interpretation of examinations and follow up documentation of findings within healthcare institutions, for example, in Radiology, Nuclear Medicine and Cardiology envirents.

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.via MI Workflows are a complement to these standard procedures.

The syngo.via MI Workflows are software only medical devices which will be delivered on CD-ROM / DVD to be installed onto the commercially available Siemens syngo.via software platform by trained service personnel.

syngo.via MI Workflows is a medical diagnostic application for viewing, manipulation, 3Dvisualization and comparison of medical images from multiple imaqinq 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.via MI Workflows enable visualization of information that would otherwise have to be visually compared disjointedly. synqo.via MI Workflows provide analytical tools to help the user assess, and document changes in morphological or functional activity at diagnostic and therapy follow-up examinations. They additionally support the interpretation and evaluation of examinations and follow up documentation of findings within healthcare institutions, for example, in Radiology (Oncology), Nuclear Medicine and Cardiology environments.

The provided text primarily focuses on the submission of a 510(k) premarket notification for syngo.via MI Workflows VB30A and its substantial equivalence to a predicate device (syngo.via MI Workflows VB20). It outlines the device's intended use, technological characteristics, and compliance with various regulatory standards.

However, the document does not contain the detailed information necessary to fully answer all parts of your request regarding acceptance criteria and a specific study proving the device meets those criteria. The text mentions that "Verification and Validation activities have been successfully performed on the software package," and "All testing has met the predetermined acceptance values," but it does not provide specific numerical acceptance criteria, reported performance values, sample sizes, ground truth establishment, or details of any comparative effectiveness studies.

Therefore, I cannot populate all sections of the table or provide detailed answers to items 2-9.

Here's what can be extracted and what is missing:

1. Table of acceptance criteria and the reported device performance

Missing Information: Specific numerical or qualitative acceptance criteria for particular features (e.g., accuracy of 3D visualization, quantification precision, speed of processing) and the actual reported performance values are not detailed in this document.

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

• Missing Information: The document states "Verification and Validation activities have been successfully performed," but does not provide any specifics about the sample size of the test set, the provenance of the data used for testing, or whether it was retrospective or prospective.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Missing Information: This information is not present in the provided text.

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

• Missing Information: This information is not present in the provided text.

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

• Missing Information: The document outlines the device as a "medical diagnostic application for viewing, manipulation, 3D-visualization and comparison of medical images" and explicitly states it is a "complement to these standard procedures." It does not describe any MRMC comparative effectiveness study, nor does it provide any effect size for human reader improvement with or without AI assistance. The device is software for image manipulation and viewing, not an AI for interpretation in the sense of directly assisting reader decisions, but rather providing tools.

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

• Missing Information: This document does not detail any standalone performance studies. The device is described as an application for viewing and manipulation, implying human interaction.

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

• Missing Information: This information is not present in the provided text.

8. The sample size for the training set

• Missing Information: The document describes the device as a software platform with new features added to a predicate device. It does not mention any "training set" in the context of machine learning, suggesting this is not a machine learning device that requires a training set in the typical sense.

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

• Missing Information: As no training set is mentioned in the context of machine learning, there is no information on how its ground truth would be established.

Summary of what the document does convey:

• The device (syngo.via MI Workflows VB30A) is a software-only medical device for medical image viewing, manipulation, 3D-visualization, and comparison from multiple modalities and time-points.
• It is an updated version of a predicate device (syngo.via MI Workflows VB20) with new features in specific workflows (MM Oncology, MI Neurology, MI Cardiology, MI Reading / SPECT Processing).
• It is intended to run on the Siemens syngo.via software platform.
• Siemens claims substantial equivalence to the predicate device and states that "no changes that raise any new issues of safety and effectiveness."
• Verification and validation activities were successfully performed, and all testing met predetermined acceptance values.
• The device complies with various recognized industry standards (ISO 14971, IEC 62304, NEMA PS 3.1 - 3.20, IEC 62366, ISO 15223-1).

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The Scenium display and analysis software has been developed to aid the clinician in the assessment and quantification of pathologies taken from PET and SPECT scans.

The software is deployed via medical imaging workplaces and is organized as a series of workflows which are specific to use with particular drug and disease combinations.

The software aids in the assessment of human brain scans enabling automated analysis through quantification of mean pixel values located within standard regions of interest. It facilitates comparison with existing databases of normal patients and normal parameters derived from these databases, derived from FDG-PET, amyloid-PET, and SPECT studies, calculation of uptake ratios between regions of interest, and subtraction between two functional scans.

Scenium VE10 display and analysis software enables visualization and appropriate rendering of multimodality data, providing a number of features which enable the user to process acquired image data.

Scenium VE10 consists of three workflows:

• Database Comparison
• Ratio Analysis
• Subtraction

These workflows are used to assist the clinician with the visual evaluation, assessment and quantification of pathologies, such as dementia (i.e., Alzheimer's), movement disorders (i.e., Parkinson's) and seizure analysis (i.e., Epilepsy).

The modifications made to the Scenium VE10 software (K162339) to create the Scenium VE20 software include:

• The ability to create and support normal databases in the Striatal Analysis workflow
  • DaTSCAN-SPECT normals database
• Improvements related to the analysis screen for reporting in Striatal Analysis

In addition, workflow structures changed within the VE20 release. Previously, the three workflows (Database Comparison, Ratio Analysis, and Subtraction) encompassed the Scenium software. Ratio Analysis has since been split into two separate workflows. Now, the following four workflows exist within Scenium VE20:

• Database Comparison
• Striatal Analysis
• Cortical Analysis
• Subtraction

These changes are based on current commercially available software features and do not change the technological characteristics of the device.

Scenium VE20 analysis software is intended to be run on commercially available software platforms such as the Siemens syngo.MI Workflow software platform (K150843).

The provided text is a 510(k) summary for the Scenium VE20 device, which is an image processing software for PET and SPECT scans. It primarily focuses on demonstrating substantial equivalence to a predicate device (Scenium VE10) rather than presenting a detailed study with specific acceptance criteria and performance metrics for the VE20 device itself.

Therefore, the document does not contain the detailed information required to fill out all sections of your request about acceptance criteria and a specific study proving the device meets them. The text states that "All testing has met the predetermined acceptance values" but does not elaborate on what those values were or what performance metrics were used to determine them for the Scenium VE20 specifically.

Here's what can be extracted and inferred, along with the information that is explicitly missing:

Acceptance Criteria and Reported Device Performance

Study Details Provided

The document refers to "Verification and Validation activities" and "All testing" but does not describe a specific clinical or technical study designed to prove the device meets explicit acceptance criteria in the way a clinical trial or performance study would. Instead, it relies on demonstrating equivalence to an already cleared device.

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

   • Not explicitly stated for Scenium VE20. The document does not describe a separate clinical test set or its sample size.
   • Data provenance: Not mentioned.

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

   • Not applicable / Not stated. No specific external "test set" and associated ground truth establishment process is described in this document for the Scenium VE20.

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

   • Not applicable / Not stated. No specific external "test set" and adjudication process is described.

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, an MRMC comparative effectiveness study was not explicitly mentioned or described as part of this 510(k) submission. The device (Scenium VE20) is primarily an image processing software for quantification and comparison, aiding clinicians, but the submission doesn't present it as an AI assistant in the typical sense that would necessitate an MRMC study comparing human performance with and without its aid. The improvements are related to expanding database and analysis capabilities.

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

   • Not explicitly described. The document states "Verification and Validation activities have been successfully performed on the software package, including assurance that functions work as designed, performance requirements and specifications have been met..." This implies internal testing of the algorithm's functions, but details of a formal "standalone" performance study are not provided. The device "aids the clinician" and its output facilitates "comparison," implying human interpretation remains central.

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

   • Not explicitly stated. Given the nature of the software (quantification and comparison with normal databases), the ground truth for the underlying databases (FDG-PET, amyloid-PET, and SPECT studies, DaTSCAN-SPECT normals database) would likely have been established through clinical diagnosis, expert consensus, or follow-up outcomes, but this is not detailed in the context of VE20's "testing."

7. The sample size for the training set:

   • Not explicitly stated for the Scenium VE20's development. The document mentions the "ability to create and support normal databases in the Striatal Analysis workflow" including a "DaTSCAN-SPECT normals database." The size and composition of these "normal databases" are not specified as "training sets" for a deep learning model, as the document doesn't explicitly state the use of AI/deep learning in the typical sense for image interpretation. This sounds more like statistical comparison to pre-existing normal population data.

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

   • Not explicitly stated for the Scenium VE20's development. For the normal databases, the ground truth would inherently be "normal patients" (as described in the text "existing databases of normal patients"). How the "normal" status was confirmed for these patients in these databases is not elaborated upon in this submission.

In summary, this 510(k) submission focuses on demonstrating substantial equivalence to a predicate device by highlighting that the modifications do not alter the fundamental technological characteristics or intended use, and that internal verification and validation activities confirmed the software functions as designed and met its requirements. It does not provide the kind of detailed performance study and acceptance criteria that might be found in submissions for novel AI-powered diagnostic devices.

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Your MAGNETOM system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal and oblique cross sectional images, spectroscopic images and that displays the internal structure and/or function of the head, body, or extremittes. Other physical parameters derived from the images and or spectra may also be produced. Depending on the region of interest, contrast agents may be used. These mages and or spectra and the physical parameters derived from the images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

Your MAGNETOM system may also be used for imaging during interventional procedures when performed with MR compatible devices such as in-room displays and MR Safe biopsy needles.

MAGNETOM Vida with software syngo MR XA10A is similar to the previous cleared predicate device MAGNETOM Skyra with syngo MR E11C (K153343) but includes new and modified hardware and software compared to MAGNETOM Skyra.

Here's a breakdown of the acceptance criteria and study information for the MAGNETOM Vida device, based on the provided text:

Preamble: It's important to note that this document is a 510(k) summary for a premarket notification for a Class II medical device (Magnetic Resonance Diagnostic Device). The primary goal of a 510(k) submission is to demonstrate "substantial equivalence" to a legally marketed predicate device, not necessarily to prove absolute efficacy in a clinical setting in the same way a PMA (Premarket Approval) might require. Therefore, the "acceptance criteria" and "device performance" are primarily focused on meeting established standards and showing that changes do not negatively impact safety or effectiveness compared to the predicate.

Acceptance Criteria and Reported Device Performance

The general acceptance criteria are that the device performs as intended and is "substantially equivalent" to the predicate device, especially regarding safety and effectiveness. The specific performance reported largely revolves around conformance to recognized standards and successful completion of verification and validation.

Table of Acceptance Criteria and Reported Device Performance:

Study Information

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

   • Clinical Study (PNS Threshold): 33 individuals. The document does not specify the country of origin or whether it was retrospective or prospective, but clinical studies for such thresholds are typically prospective.
   • Nonclinical Tests (Image Quality, Software V&V, Performance Tests): The document does not specify a numerical sample size but mentions "sample clinical images were taken" for new coils and software features. It does not provide provenance (country, retrospective/prospective) for these samples specifically, but they would likely be internal studies.

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

   • The document does not explicitly state the number or qualifications of experts used to establish ground truth for the image quality assessments, software V&V, or performance tests. However, it indicates that the interpretation of images and spectra is done "by a trained physician." For image quality assessments, it's implied that Siemens' internal experts or qualified personnel performed these.

3. Adjudication Method for the Test Set:

   • The document does not specify an adjudication method like 2+1 or 3+1 for any of the tests described.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No MRMC comparative effectiveness study is mentioned for the entire device. The submission focuses on demonstrating substantial equivalence through compliance with standards, verification, and validation, rather than a direct comparison of reader performance with and without the new AI features (if any specific AI features are implied, they are integrated within the "new software" and not evaluated separately as AI-assisted reading).
   • There is no mention of an effect size for human readers with vs. without AI assistance.

5. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study:

   • No standalone performance study of an algorithm is explicitly described. The device is an MRI system, which always involves a human operator and physician interpretation. The "new software" features are part of the overall system performance, not presented as a discrete AI algorithm for standalone evaluation.

6. Type of Ground Truth Used:

   • PNS Threshold Study: The ground truth would be the observed physiological response (nerve stimulation) in the 33 individuals, used to set the safety threshold.
   • Image Quality Assessments: The ground truth would likely be internal Siemens expert assessment of expected image characteristics, clarity, and diagnostic interpretability against established quality metrics or comparisons to images from the predicate device.
   • Software V&V/Performance Tests: Ground truth would be derived from specifications, expected functional outputs, and adherence to regulatory standards.
   • General Diagnosis: The "Indications for Use" state that images and spectra, "when interpreted by a trained physician yield information that may assist in diagnosis." This implies physician interpretation is the ultimate ground truth for diagnostic purposes in clinical use, but not for the technical performance studies described in the 510(k).

7. Sample Size for the Training Set:

   • The document does not provide information on a specific training set size. This type of 510(k) submission generally does not detail the internal development and training data for software components, especially when the changes are primarily updates to an existing system, rather than a de novo AI algorithm approval.

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

   • Since no specific training set or study for algorithm training is described, the method for establishing its ground truth is not provided.

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