Search Results

Ask a specific question about this device

Ask a specific question about this device

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 staff as an aid in diagnosis, treatment and radiation therapy planning as well as for diagnostic and therapeutic interventions.

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.

Siemens intends to market a new software version, SOMARIS/10 syngo CT VB20 for the following SOMATOM Computed Tomography (CT) Scanner Systems:

SOMATOM X. Platform CT scanner systems:

• SOMATOM X.cite
• SOMATOM X.ceed

In this submission, the above listed CT scanner systems are jointly referred to as subject devices by "SOMATOM X. Platform" CT scanner systems.

The subject devices SOMATOM X. Platform CT scanner systems with SOMARIS/10 syngo CT VB20 are Computed Tomography X-ray Systems which feature one continuously rotating tube-detector system and function according to the fan beam principle (single source). The SOMATOM X. Platform CT scanner systems with software SOMARIS/10 syngo CT VB20 produces CT images in DICOM format, which can be used by trained staff for software applications, e.g. post-processing applications, commercially distributed by Siemens Healthcare GmbH 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.

Only trained and qualified users, certified in accordance with country-specific regulations, are authorized to operate the system. For example, physicians, radiologists, or technologists. The user must have the necessary U.S. qualifications in order to diagnose or treat the patient with the use of the images delivered by the system.

The platform software for SOMATOM X. Platform, SOMARIS/10 syngo CT VB20, is a command-based program used for patient management, data management, X-ray scan control, image reconstruction, and image archive/evaluation.

N/A

Ask a specific question about this device

The Atellica CH Diazo Total Bilirubin (D_TBil) assay is for in vitro diagnostic use in the quantitative determination of total bilirubin in human serum and plasma of adults and neonates using the Atellica CH Analyzer. Measurement of total bilirubin, an organic compound formed during the normal and abnormal destruction of red blood cells, is used in the diagnosis and treatment of liver, hemolytic, hematological, and metabolic disorders, including hepatitis and gallbladder block. A total bilirubin measurement in newborn infants is intended to aid in indicating the risk of bilirubin encephalopathy (kernicterus).

Atellica CH Diazo Total Bilirubin is a photometric test using 2,4-dichloroaniline (DCA). Direct bilirubin in presence of diazotized 2,4-dichloroaniline forms a red colored azocompound in acidic solution. A specific mixture of detergents enables the determination of the total bilirubin.

N/A

Ask a specific question about this device

The 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/or spectra, and that displays, depending on optional local coils that have been configured with the system, 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 yield information that may assist in diagnosis.

The 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 Flow.Ace & MAGNETOM Flow.Plus with software Syngo MR XB10 include new and modified hardware and software compared to the predicate devices, MAGNETOM Flow.Ace & MAGNETOM Flow.Plus with software syngo MR XA70A.

New compared to predicate:

• Spine support respiratory (Cushion) as a part of BM Spine Coil Set 1.5T (including new surface material)
• Gradient Configuration Upgrade

Modified same as predicate, but with new claim introduced:

• PETRA (new claim for the existing sequence)

In addition, the following hardware and software are transferred from the reference device MAGNETOM Flow.Neo with software Syngo MR XB10 (K252838), to the subject devices without any modifications:

Hardware (New compared to predicate, same as reference):

• BioMatrix Dockable Table with / without eDrive
• Comfort Sound: Cushion

Hardware (Modified compared to predicate, same as reference):

• Comfort Sound: BM Head/Neck Coil
• Relocatable Option

Software (New compared to predicate, same as reference):

• Open Workflow

Software (Modified compared to predicate, same as reference):

• BioMatrix Motion Sensor (SAMER)
• CS_VIBE
• SPAIR FatSat Improvements: SPAIR "Abdomen & Pelvis" mode and SPAIR Breast mode
• Deep Resolve Boost for FL3D_VIBE and SPACE
• Deep Resolve Sharp for FL3D_VIBE and SPACE
• Preview functionality for Deep Resolve Boost
• myExam Implant Suite
• GRE_PC
• Open Recon 2.0
• Deep Resolve Boost for TSE
• "MTC Mode" for SPACE

Other Modifications and / or Minor Changes (New compared to predicate, same as reference):

• Eco Power Mode Pro

Other Modifications and / or Minor Changes (Modified compared to predicate, same as reference):

• Off-Center Planning Support
• Flip Angle Optimization (Lock TR and FA)
• ID Gain (re-naming)
• Marketing bundle "myExam Companion"

Acceptance Criteria and Study Details for Siemens MAGNETOM Flow.Ace and Flow.Plus

Based on the provided 510(k) clearance letter, the acceptance criteria and study details are as follows. It's important to note that this document primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed de novo clinical trial for device efficacy. Therefore, specific metrics like sensitivity, specificity, or AUC for diagnostic performance are not explicitly stated as acceptance criteria in the typical sense for a new diagnostic claim.

The acceptance criteria are generally focused on demonstrating that the new and modified features of the MAGNETOM Flow.Ace and Flow.Plus systems maintain equivalent safety and performance to the predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly state a specific sample size for a test set in the context of clinical images or patient data.

• Data Provenance: "Sample clinical images" were provided as "claim evidence." The origin (e.g., country) and whether this data was retrospective or prospective are not specified in the provided text. The clinical publications referenced are peer-reviewed articles, which would typically involve patient data.

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

The document does not provide information regarding the number of experts, their qualifications, or how ground truth was established for any "test set" of images. The phrase "interpreted by a trained physician" is used in the Indications for Use, which is a general statement about MR diagnostic devices.

4. Adjudication Method for the Test Set

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

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

No, an MRMC comparative effectiveness study was not explicitly done or reported in this document. The submission relies on "sample clinical images" as "claim evidence" and references existing clinical publications for certain features to demonstrate substantial equivalence, rather than a direct comparative study showing improvement with AI assistance.

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

The document discusses "software verification and validation" and "nonclinical tests" which would imply standalone performance testing of the algorithms and features. However, it does not provide specific metrics or results for standalone algorithm performance (e.g., sensitivity, specificity, or AUC if applicable to specific features). The focus is on the performance of the integrated system.

7. The Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for any specific evaluation. The "Indications for Use" mention that images "when interpreted by a trained physician yield information that may assist in diagnosis." For the referenced clinical publications, the ground truth would depend on the methodology of those studies (e.g., pathology, clinical follow-up, expert consensus in a research setting), but this information is external to the 510(k) summary itself.

8. The Sample Size for the Training Set

The document does not provide any information regarding the sample size for a training set. This is a 510(k) submission for an updated MR system, not a de novo AI/ML device where training data details are typically prominent. While features like "Deep Resolve Boost" and "Deep Resolve Sharp" likely leverage AI/ML, the details of their development and training are not disclosed in this summary.

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

The document does not provide any information on how ground truth was established for a training set.

Ask a specific question about this device

MI View&GO is a medical diagnostic application for viewing, manipulation, quantification, analysis and comparison of medical images with one or more time-points. MI View&GO supports functional data, such as positron emission tomography (PET) or nuclear medicine (NM), as well as anatomical datasets, such as computed tomography (CT) or magnetic resonance (MR).

MI View&GO is intended to be utilized by appropriately trained health care professionals to aid in the management of diseases associated with oncology, cardiology, neurology, and organ function. The images and results produced by MI View&GO can also be used by the physician to aid in radiotherapy treatment planning.

MI View&GO is a software-only medical device which will be delivered in conjunction with Siemens SPECT/CT and PET/CT scanners. MI View&GO software provides additional specific capabilities for handling of PET and SPECT as well as CT and MR data directly at the acquisition console.

The MI View&GO software integrates molecular imaging more efficiently in the clinical environment by providing an interface for its users to review, post-process and read medical images immediately after acquisition. The purpose of the MI View&GO is to allow the technologist and reading physician to:

• Review acquired and reconstructed images at the scanner console
• Determine that the acquired data is of sufficient quality for reading, so the patient can be released.
• Prepare images for reading
• Perform a basic read

Here's an analysis of the acceptance criteria and study detailed in the provided FDA 510(k) clearance letter for MI View&GO, structured according to your requested points:

Acceptance Criteria and Device Performance Study for MI View&GO (K254016)

1. Acceptance Criteria and Reported Device Performance

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

• Improved Lung Segmentation:
  • Sample Size: 20 patients.
  • Data Provenance:
    • Retrospective.
    • Half of the patients were new, and the other 50% were randomly selected from the predicate testing cohort.
    • 50% of patients were from the US.
    • All patients from Siemens Scanner.
• Improved PERCIST Liver Algorithm (binary liver mask input):
  • Sample Size: 20 patients.
  • Data Provenance:
    • Patients obtained from clinical partners in Europe and USA.
    • Randomly selected with stratification.
    • All subjects from Siemens Scanner.
• Improved PERCIST Liver Algorithm (Reference Region Placement & Intersection with Suspicious Uptake Masks):
  • Sample Size: 129 subjects for the "intersection with suspicious uptake masks" analysis.
  • Data Provenance: Not explicitly stated for the "reference region placement" analysis, but implied to be from the same or similar source as the 129 subjects.

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

• Improved Lung Segmentation: Not explicitly stated. The ground truth for segmentation metrics (Dice, ASSD) is typically established by manual segmentation performed by experts, but the number of experts and their qualifications are not detailed in this document.
• Improved PERCIST Liver Algorithm (Reference Region Placement):
  • Number of Experts: Two expert readers.
  • Qualifications: "Expert readers" is mentioned, but specific qualifications (e.g., radiologist 10 years experience) are not provided.
• Improved PERCIST Liver Algorithm (Intersection with Suspicious Uptake Masks):
  • Number of Experts: One expert reader.
  • Qualifications: "Expert reader" is mentioned; specific qualifications are not provided.

4. Adjudication Method for the Test Set

• Improved Lung Segmentation: Not explicitly mentioned. For segmentation ground truth derived from multiple experts, methods like consensus or averaging are common, but not specified here.
• Improved PERCIST Liver Algorithm (Reference Region Placement): Semi-automatic evaluation by two expert readers. The document states the subject device algorithm was compared to this "reference standard," implying this semi-automatic output was considered the ground truth. No explicit adjudication method (like 2+1) is described for resolving differences between the two experts, if they occurred.
• Improved PERCIST Liver Algorithm (Intersection with Suspicious Uptake Masks): Identified by "an expert reader." This implies a single expert's identification served as the ground truth. No adjudication mentioned.

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

• No, a formal MRMC comparative effectiveness study involving human readers with and without AI assistance is not described in this document.
• The studies conducted focus on the algorithm's performance against historical data, expert interpretations, or comparing an improved algorithm to a predicate algorithm.

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

• Yes, standalone performance studies were conducted for specific features:
  • Improved Lung Segmentation: The Dice coefficient and ASSD evaluation was a standalone algorithmic performance assessment against presumed expert-derived ground truth.
  • Improved PERCIST Liver Algorithm (binary liver mask input): The Dice coefficient and ASSD evaluation for the liver mask was a standalone algorithmic performance assessment.
  • Improved PERCIST Liver Algorithm (Reference Region Placement): The comparison of the algorithm's results to the semi-automatic evaluation by two expert readers is a standalone algorithm assessment, where the expert input constitutes the ground truth.
  • Improved PERCIST Liver Algorithm (Intersection with Suspicious Uptake Masks): This was a standalone algorithmic evaluation of how often the algorithm's PERCIST VOIs intersected suspicious uptake areas identified by an expert.

7. The Type of Ground Truth Used

• Improved Lung Segmentation: Likely expert consensus/manual segmentation (implied by Dice coefficient and ASSD, which compare algorithm output to a gold standard segmentation).
• Improved PERCIST Liver Algorithm (binary liver mask input): Likely expert consensus/manual segmentation (implied by Dice coefficient and ASSD for the liver mask).
• Improved PERCIST Liver Algorithm (Reference Region Placement): Expert semi-automatic evaluation from two expert readers. These semi-automatic outputs were treated as the reference standard.
• Improved PERCIST Liver Algorithm (Intersection with Suspicious Uptake Masks): Expert identification of suspicious tracer uptake masks by a single expert reader.

8. The Sample Size for the Training Set

• Not explicitly stated in the document. The document mentions that the lung lobe segmentation algorithm was "re-trained with additional data" and that there was "No overlap of patients between training, tuning, and test cohorts," but does not provide details on the training set's size.

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

• Not explicitly stated in the document. For machine learning models, ground truth for training data is typically established through expert labeling (e.g., manual segmentation, disease annotation), but the specifics are not provided here.

Ask a specific question about this device

The Atellica® IM TSH3‑Ultra II (TSH3ULII) assay is for in vitro diagnostic use in the quantitative determination of thyroid-stimulating hormone (TSH, thyrotropin) in human serum and plasma (EDTA and lithium heparin) using the Atellica® IM Analyzer. Measurements of thyroid stimulating hormone produced by the anterior pituitary are used in the diagnosis of thyroid or pituitary disorders.

This assay is a third-generation assay that employs anti-FITC monoclonal antibody covalently bound to paramagnetic particles, an FITC-labeled anti-TSH capture mouse monoclonal antibody, and a tracer consisting of a proprietary acridinium ester and an anti‑TSH mouse monoclonal antibody conjugated to bovine serum albumin (BSA) for chemiluminescent detection.

N/A

Ask a specific question about this device

AI-Rad Companion Brain MR is a post-processing image analysis software that assists clinicians in viewing, analyzing, and evaluating MR brain images.

AI-Rad Companion Brain MR provides the following functionalities:
• Automated segmentation and quantitative analysis of individual brain structures and white matter hyperintensities
• Quantitative comparison of each brain structure with normative data from a healthy population
• Presentation of results for reporting that includes all numerical values as well as visualization of these results

AI-Rad Companion Brain MR runs two distinct and independent algorithms for Brain Morphometry analysis and White Matter Hyperintensities (WMH) segmentation, respectively. In overall, comprises four main algorithmic features:

• Brain Morphometry
• Brain Morphometry follow-up
• White Matter Hyperintensities (WMH)
• White Matter Hyperintensities (WMH) follow-up

The feature for Brain Morphometry is available since the first version of the device (VA2x), while segmentation of White Matter Hyperintensities was added since VA4x and the follow-up analysis for both is available since VA5x. The brain morphometry and brain morphometry follow-up feature have not been modified and remain identical to previous VA5x mainline version.

AI-Rad Companion Brain MR VA60 is an enhancement to the predicate, AI-Rad Companion Brain MR VA50 (K232305). Just as in the predicate, the brain morphometry feature of AI-Rad Companion Brain MR addresses the automatic quantification and visual assessment of the volumetric properties of various brain structures based on T1 MPRAGE datasets. From a predefined list of brain structures (e.g. Hippocampus, Caudate, Left Frontal Gray Matter, etc.) volumetric properties are calculated as absolute and normalized volumes with respect to the total intracranial volume. The normalized values are compared against age-matched mean and standard deviations obtained from a population of healthy reference subjects. The deviation from this reference population can be visualized as 3D overlay map or out-of-range flag next to the quantitative values.

Additionally, identical to the predicate, the white matter hyperintensities feature addresses the automatic quantification and visual assessment of white matter hyperintensities on the basis of T1 MPRAGE and T2 weighted FLAIR datasets. The detected WMH can be visualized as a 3D overlay map and the quantification in count and volume as per 4 brain regions in the report.

Here's a structured overview of the acceptance criteria and study details for the AI-Rad Companion Brain MR, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Reported Device Performance

Study Details

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

   • White Matter Hyperintensities (WMH) Feature: 100 subjects (Multiple Sclerosis patients (MS), Alzheimer's patients (AD), cognitive impaired (CI), and healthy controls (HC)).
   • White Matter Hyperintensities (WMH) Follow-up Feature: 165 subjects (Multiple Sclerosis patients (MS) and Alzheimer's patients (AD)).
   • Data Provenance: Data acquired from Siemens, GE, and Philips scanners. Testing data had balanced distribution with respect to gender and age of the patient according to target patient population, and field strength (1.5T and 3T). This indicates a retrospective, multi-vendor, multi-national (implied by vendor diversity) dataset.

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

   • Number of Experts: Three radiologists.
   • Qualifications: Not explicitly stated beyond "radiologists." It is not specified if they are board-certified, or their years of experience.

3. Adjudication Method for the Test Set:

   • For each dataset, three sets of ground truth annotations were created manually.
   • Each set was annotated by a disjoint group consisting of an annotator, a reviewer, and a clinical expert.
   • The clinical expert was randomly assigned per case to minimize annotation bias.
   • The clinical expert reviewed and corrected the initial annotation of the changed WMH areas according to a specified annotation protocol. Significant corrections led to re-communication with the annotator and re-review.
   • This suggests a 3+1 Adjudication process, where three initial annotations are reviewed by a clinical expert.

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

   • No, an MRMC comparative effectiveness study comparing human readers with and without AI assistance was not done. The study focuses on the standalone performance of the AI algorithm against expert ground truth.

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

   • Yes, a standalone performance study was done. The "Accuracy was validated by comparing the results of the device to manual annotated ground truth from three radiologists." This evaluates the algorithm's performance directly.

6. The Type of Ground Truth Used:

   • Expert Consensus / Manual Annotation: The ground truth for both WMH and WMH follow-up features was established through "manual annotated ground truth from three radiologists" and involved a "standard annotation process" with annotators, reviewers, and clinical experts.

7. The Sample Size for the Training Set:

   • The document states that the "training data used for the fine tuning the hyper parameters of WMH follow-up algorithm is independent of the data used to test the white matter hyperintensity algorithm follow up algorithm." However, the specific sample size for the training set is not provided in the given text.

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

   • The document implies that the WMH follow-up algorithm "does not include any machine learning/ deep learning component," suggesting a rule-based or conventional image processing algorithm. Therefore, "training" might refer to parameter tuning rather than machine learning model training.
   • For the "fine-tuning the hyper parameters of WMH follow-up algorithm," the ground truth establishment method for this training data is not explicitly detailed in the provided text. It only states that this data was "independent of the data used to test" the algorithm.

Ask a specific question about this device

Magnetic Resonance Imaging (MRI) is a noninvasive technique used for diagnostic imaging. MRI with its soft tissue contrast capability enables the healthcare professional to differentiate between various soft tissues, for example, fat, water, and muscle, but can also visualize bone structures.

Depending on the region of interest, contrast agents may be used.

The MR system may also be used for imaging during interventional procedures and radiation therapy planning.

The PET images and measures the distribution of PET radiopharmaceuticals in humans to aid the physician in determining various metabolic (molecular) and physiologic functions within the human body for evaluation of diseases and disorders such as, but not limited to, cardiovascular disease, neurological disorders, and cancer.

The integrated system utilizes the MRI for radiation-free attenuation correction maps for PET studies. The integrated system provides inherent anatomical reference for the fused MR and PET images due to precisely aligned MR and PET image coordinate systems.

BIOGRAPH One with software Syngo MR XB10 includes new and modified hardware and software compared to the predicate device, Biograph mMR with software syngo MR E11P-AP01. A high level summary of the new and modified hardware and software is provided below:

Hardware

New Hardware

• Gantry offset phantom
• SDB (Smart Distribution Box)

New Coils

• BM Contour XL Coil
• BM Head/Neck Pro PET-MR Coil
• BM Spine Pro PET-MR Coil
• Transfer of up-to-date RF coils from the reference device MAGNETOM Vida.

Modified Hardware

• Main components such as:
  • Detector cassettes / DEA
  • Phantom holder
  • Gantry tube
  • Backplane
  • Magnet and cabling
  • Gradient coil
  • MaRS (measurement and reconstruction system)
  • MI MARS
  • PET electronics
  • RF transmitter TBX3 3T (TX Box 3)
• Other components such as:
  • Cover
  • Filter plate
  • Patient table
  • RFCEL_TEMP

Modified Coils

• Body coil
• Transfer of up-to-date RF coils from the reference device MAGNETOM Vida with some improvements.

Software

New Features and Applications

• Fast Whole-Body workflows
• Fast Head workflow
• myExam PET-MR Assist
• CS-Vibe
• myExam Implant Suite
• DANTE blood suppression
• SMS Averaging for TSE
• SMS Averaging for TSE_DIXON
• SMS without diffusion function
• BioMatrix Motion Sensor
• RF pulse optimization with VERSE
• Deep Resolve Boost for FL3D_VIBE and SPACE
• Deep Resolve Sharp for FL3D_VIBE and SPACE
• Preview functionality for Deep Resolve Boost
• EP2D_FID_PHS
• EP_SEG_FID_PHS
• ASNR recommended protocols for imaging of ARIA
• Open Workflow
• Ultra HD-PET
• "MTC Mode"
• OpenRecon 2.0
• Deep Resolve Boost for TSE
• GRE_PC
• The following functions have been migrated for the subject device without modifications from MAGNETOM Skyra Fit and MAGNETOM Sola Fit:
  • 3D Whole Heart
• Ghost reduction (Dual polarity Grappa (DPG))
• Fleet Reference Scan
• AutoMate Cardiac (Cardiac AI Scan Companion)
• Complex Averaging
• SPACE Improvement: high bandwidth IR pulse
• SPACE Improvement: increase gradient spoiling
• The following function has been migrated for the subject device without modifications from MAGNETOM Free.Max:
  • myExam Autopilot Spine
• The following functions have been migrated for the subject device without modifications from MAGNETOM Sola:
  • myExam Autopilot Brain
  • myExam Autopilot Knee
• Transfer of further up-to-date SW functions from the reference devices.

New Software / Platform

• PET-Compatible Coil Setup
• Select&GO
• PET-MR components communication

Modified Features and Applications

• HASTE_CT
• FL3D_VIBE_AC
• PET Reconstruction
• Transfer of further up-to-date SW functions from the reference devices with some improvements.

Modified Software / Platform

• Several software functions have been improved. Which are:
  • PET Group
  • PET Viewing
  • PET RetroRecon
  • PET Status and Tune-up/QA

Other Modifications and / or Minor Changes

• Indications for use
• Contraindications
• SAR parameter
• Off-Center Planning Support
• Flip Angle Optimization (Lock TR and FA)
• Inline Image Filter
• Marketing bundle "myExam Companion"
• ID Gain
• Automatic System Shutdown (ASS) sensor (Smoke Detector)
• Patient data display (PDD)

The FDA 510(k) Clearance Letter for BIOGRAPH One refers to several AI/Deep Learning features. However, the provided document does not contain explicit acceptance criteria for these AI features in a table format, nor does it detail a comparative effectiveness study (MRMC study) for human readers. It primarily focuses on demonstrating non-inferiority to the predicate device through various non-clinical tests.

Below is an attempt to extract and synthesize the information based on the provided text, while acknowledging gaps in the information regarding specific acceptance criteria metrics and clinical studies.

Acceptance Criteria and Study Details for BIOGRAPH One AI Features

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical acceptance criteria in a dedicated table format. Instead, it describes performance in terms of achieving "convergence of the training" and "improvements compared to conventional parallel imaging," or confirming "very similar metrics" to the predicate. The "acceptance criteria" are implied by these statements and the successful completion of the described tests.

2. Sample Sizes Used for Test Set and Data Provenance

The document primarily describes a validation dataset which serves as the "test set" for the AI models during development, and an additional "test dataset" for specific evaluations.

• Deep Resolve Boost for FL3D_VIBE and SPACE:

  • Test Set Description: The "collaboration partners (testing)" data is mentioned as the source for testing, implying an external, independent test set. No specific number for this test set is provided beyond the 1265 measurements for training/validation.
  • Sample Size (Validation/Training): 27,679 3D patches from 1265 measurements.
  • Data Provenance: "in-house measurements (training and validation) and collaboration partners (testing)." The country of origin is not specified but is likely Germany (Siemens Healthineers AG) and/or China (Siemens Shenzhen Magnetic Resonance LTD.) where the manufacturing is listed.
  • Retrospective/Prospective: "Input data was retrospectively created from the ground truth by data manipulation and augmentation." This indicates retrospective data use.

• Deep Resolve Sharp for FL3D_VIBE and SPACE:

  • Test Set Description: The document states, "The high-resolution datasets were split to 70% training and 30% validation datasets before training to ensure independence of them." This implies the 30% validation dataset is used as the test set.
  • Sample Size (Validation/Training): 27,679 3D patches from 1265 measurements (split into 70% training and 30% validation).
  • Data Provenance: "in-house measurements (training and validation) and collaboration partners (testing)."
  • Retrospective/Prospective: "Input data was retrospectively created from the ground truth by data manipulation." This indicates retrospective data use.

• Deep Resolve Boost for TSE (First Mention - General Performance):

  • Test Set Description: The "evaluation on the test dataset" is mentioned. The validation set is 30% of the 500 measurements.
  • Sample Size (Validation/Training): Approximately 13,000 high resolution 3D patches from 500 measurements (split into 70% training and 30% validation).
  • Data Provenance: "in-house measurements."
  • Retrospective/Prospective: "Input data was retrospectively created from the ground truth by data manipulation." This indicates retrospective data use.

• Deep Resolve Boost for TSE (Second Mention - Banding Artifacts):

  • Test Set Description: "Additional test dataset for banding artifact reduction: more than 2000 slices." This dataset was acquired after the release of the predicate network.
  • Sample Size: More than 2000 slices.
  • Data Provenance: "in-house measurements and collaboration partners."
  • Retrospective/Prospective: Not explicitly stated for this specific additional dataset, but the training/validation data for the predicate was retrospective.

3. Number of Experts and Qualifications for Ground Truth

• Radiologist Evaluation for Deep Resolve Boost for TSE (Second Mention): The document mentions "the radiologist evaluation revealed no difference in suitability for clinical diagnostics."

  • Number of Experts: Not specified (singular "radiologist" used, but typically multiple are implied for such evaluations).
  • Qualifications: "Radiologist." No specific years of experience or subspecialty are mentioned.

• Other features: For Deep Resolve Boost/Sharp for FL3D_VIBE and SPACE, and Deep Resolve Boost for TSE (first mention), the ground truth is derived directly from acquired image data (see section 7). No independent human expert ground truth establishment for these.

4. Adjudication Method (for Test Set)

• Radiologist Evaluation for Deep Resolve Boost for TSE (Second Mention): The adjudication method is not specified in the document (e.g., 2+1, 3+1). It only states "the radiologist evaluation."

• Other features: Adjudication methods are not applicable as human experts were not establishing ground truth for objective metrics.

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

• Was an MRMC study done? No, the document does not describe an MRMC comparative effectiveness study where human readers' performance with and without AI assistance is compared. The evaluation for Deep Resolve Boost for TSE mentions "radiologist evaluation" but not in a comparative MRMC study context.
• Effect Size: Not applicable, as no MRMC study was performed.

6. Standalone (Algorithm Only) Performance

• Was standalone performance done? Yes, the performance testing for all Deep Resolve features (Boost and Sharp for FL3D_VIBE, SPACE, and TSE) was conducted "algorithm only" by evaluating metrics like PSNR, SSIM, MSE, and LPIPS, and then visual inspection/radiologist evaluation. These refer to the algorithm's direct output performance.

7. Type of Ground Truth Used

• Deep Resolve Boost for FL3D_VIBE and SPACE: "The acquired datasets (as described above) represent the ground truth for the training and validation."
• Deep Resolve Sharp for FL3D_VIBE and SPACE: "The acquired datasets represent the ground truth for the training and validation." Input data was manipulated (cropped k-space) to create low-resolution input and high-resolution output/ground truth from the same dataset.
• Deep Resolve Boost for TSE (First Mention): "The acquired datasets represent the ground truth for the training and validation." Input data was manipulated (cropped k-space) to create low-resolution input and high-resolution output/ground truth from the same dataset.
• Deep Resolve Boost for TSE (Second Mention): "The acquired training/validation datasets... represent the ground truth for the training and validation." Input data was manipulated by undersampling k-space, adding noise, and mirroring k-space.
• Summary: The ground truth for all AI features was derived from acquired, high-resolution original image data (retrospectively manipulated to simulate inputs). For Deep Resolve Boost for TSE (second mention), there was also an implicit "expert consensus" or "expert reading" component for the "radiologist evaluation" regarding clinical suitability.

8. Sample Size for the Training Set

• Deep Resolve Boost for FL3D_VIBE and SPACE: 81% of 1265 measurements (for 27,679 3D patches).
• Deep Resolve Sharp for FL3D_VIBE and SPACE: 70% of 1265 measurements (for 27,679 3D patches).
• Deep Resolve Boost for TSE (First Mention): 70% of 500 measurements (for approx. 13,000 high resolution 3D patches).
• Deep Resolve Boost for TSE (Second Mention): More than 23,250 slices (93% of the combined training/validation dataset from K213693).

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

• Deep Resolve Boost for FL3D_VIBE and SPACE: The "acquired datasets" represent the ground truth. "Input data was retrospectively created from the ground truth by data manipulation and augmentation. This process includes further undersampling of the data by discarding k-space lines as well as creating sub-volumes of the acquired data."
• Deep Resolve Sharp for FL3D_VIBE and SPACE: The "acquired datasets represent the ground truth." "Input data was retrospectively created from the ground truth by data manipulation. k-space data has been cropped such that only the center part of the data was used as input. With this method corresponding low-resolution data as input and high-resolution data as output / ground truth were created for training and validation."
• Deep Resolve Boost for TSE (First Mention): Similar to Deep Resolve Sharp for FL3D_VIBE and SPACE: "The acquired datasets represent the ground truth for the training and validation. Input data was retrospectively created from the ground truth by data manipulation. k-space data has been cropped such that only the center part of the data was used as input. With this method corresponding low-resolution data as input and high-resolution data as output / ground truth were created for training and validation."
• Deep Resolve Boost for TSE (Second Mention): "The acquired training/validation datasets... represent the ground truth for the training and validation. Input data was retrospectively created from the ground truth by data manipulation and augmentation. This process includes further undersampling of the data by discarding k-space lines, lowering of the SNR level by addition of noise and mirroring of k-space data."

In summary, for all AI features, the ground truth for training was established by using high-quality, originally acquired MRI data that was then retrospectively manipulated (e.g., undersampled, cropped, noise added) to create synthetic "lower quality" input data for the AI model to learn from, with the original high-quality data serving as the target output or ground truth.

Ask a specific question about this device

Atellica IM total PSA II (tPSAII) assay is for in vitro diagnostic use in the quantitative measurement of total prostate-specific antigen (PSA) in human serum and plasma (EDTA and lithium-heparin) using the Atellica IM Analyzer.

This assay is indicated as an aid in the detection of prostate cancer in conjunction with a digital rectal exam (DRE) in men aged 50 years and older. Prostate biopsy is required for diagnosis of prostate cancer. This assay is further indicated as an aid in the management (monitoring) of patients with prostate cancer.

The Atellica IM total PSA II (tPSAII) assay consists of:

tPSAII ReadyPack® primary reagent pack

• Lite Reagent (10.0 mL/reagent pack): Unlabeled monoclonal mouse anti-fPSA antibody (~250 ng/mL); monoclonal mouse anti-PSA antibody (~180 ng/mL) labeled with acridinium ester; buffer; bovine serum albumin (BSA); preservative.
• Solid Phase (20.0 mL/reagent pack): Monoclonal mouse anti-PSA antibody (~3.5 μg/mL) labeled with biotin and bound to streptavidin paramagnetic particles; buffer; BSA; bovine gamma globulin (BGG); sodium azide (< 0.1%); preservative.
  • Storage: Unopened at 2–8°C (Until expiration date on product), Onboard (42 days).

tPSAII CAL (2.0 mL/vial): Purified PSA from human seminal fluid in buffer; BSA; NaN3 (< 0.1%).

• Storage: Unopened at 2–8°C (Until expiration date on product), Opened at 2–8°C (30 days), On the system at room temperature (8 hours).

The tPSAII assay will have two configurations: 100 tests kit and 500 tests kit (5 x 100T in the carton).

N/A

Ask a specific question about this device