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NeuroQuant is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures and lesions from a set of MR images. Volumetric measurements may be compared to reference percentile data.

NeuroQuant is a fully automated MR imaging post-processing software medical device that provides automatic labeling, visualization, and volumetric quantification of brain structures and lesions from a set of MR images and returns segmented images and morphometric reports.

NeuroQuant provides morphometric measurements of brain structures based on a 3D T1 MRI series. The optional use of the T2 FLAIR MR series and T2* GRE/SWI series allows for additional quantification of T2 FLAIR hyperintense lesions and T2* GRE/SWI hypointense lesions.

The device is used by medical professionals in imaging centers, hospitals, and other healthcare facilities as well as by clinical researchers. When used clinically, the output must be reviewed by a radiologist or neuroradiologist. The results are typically forwarded to the referring physician, most commonly a neurologist. The device is a "Prescription Device" and is not intended to be used by patients or other untrained individuals.

From a workflow perspective, the device is packaged as a computing appliance that is capable of supporting DICOM standard input and output. NeuroQuant supports data from all major MRI manufacturers and a variety of field strengths. For best results, scans should be acquired using specified protocols provided by CorTechs Labs.

As part of processing, the data is corrected by NeuroQuant for image acquisition artifacts, including gradient nonlinearities and bias field inhomogeneity, to improve overall image quality.

Next, image baseline intensity levels for gray and white matter are identified and corrected for scanner variability. The scan is then aligned with the internal anatomical atlas by a series of transformations. Probabilistic methods and neural network models are then used to label each voxel with an anatomical structure based on location and signal intensities.

Output of the software provides values as numerical volumes, and images of derived data as grayscale intensity maps and as color overlays on top of the anatomical image. The outputs are provided in standard DICOM format as image series and reports that can be displayed on many commercial DICOM workstations.

The software is designed without the need for a user interface after installation. Any processing errors are reported either in the output series error report or system log files.

The software can provide data on age and gender-matched normative percentiles. The default reference percentile data for NeuroQuant comprises normal population data.

The device provides DICOM Storage capabilities to receive MRI series in DICOM format from an external source, such as an MRI scanner or PACS server. The device provides transient data storage only. If additional scans from other time points are available, the software can perform change analysis.

Here's a breakdown of the acceptance criteria and the study details for the NeuroQuant device, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Brain Segmentation Model:
  • Test Set Size: 30 patients
  • Data Provenance: Curated to represent diverse patient population across the United States. Type of study (retrospective/prospective) and specific countries of origin within the US are not specified, but it implies retrospective data collection from diverse institutions within the US.
• FLAIR Lesion Segmentation Model:
  • Test Set Size: 63 patients
  • Data Provenance: Curated to represent diverse patient population across the United States. Type of study (retrospective/prospective) not specified, but implies retrospective data collection from diverse institutions within the US (data acquired across Philips, GE, and Siemens scanners).
• MCH Detection Model:
  • Test Set Size: 117 patients
  • Data Provenance: Curated to represent diverse patient population across the United States. Type of study (retrospective/prospective) not specified, but implies retrospective data collection from diverse institutions within the US (data acquired across Philips, GE, and Siemens scanners).

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

The document does not specify the number of experts used or their detailed qualifications (e.g., radiologist with 10 years experience) for establishing the ground truth of the test sets. It broadly states that the software was validated against "known ground truth values" and "gold standard - computer-aided expert manual segmentation," but provides no specifics on the human experts involved in generating this ground truth for the test sets.

4. Adjudication Method for the Test Set

The document does not specify any adjudication method (e.g., 2+1, 3+1) for the ground truth of the test sets. It only refers to a "gold standard - computer-aided expert manual segmentation."

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

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study, nor does it quantify how much human readers improve with AI vs. without AI assistance. The study focuses on the standalone performance of the algorithms.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, standalone performance was done. The performance metrics (Dice Similarity Coefficient, F1 Score) are measurements of the algorithm's output compared to a reference ground truth, indicating a standalone analysis. The document states that the results "must be reviewed by a trained physician," implying the device is a tool to assist, but the evaluation of the device itself focuses on its automated output.

7. The Type of Ground Truth Used

The ground truth for the test sets was established using "known ground truth values" and the "gold standard - computer-aided expert manual segmentation." This implies that human experts, potentially assisted by software tools, manually segmented or labeled the structures to create the reference standard for evaluation.

8. The Sample Size for the Training Set

• Brain Segmentation Model: Trained on 1,473 3D T1-weighted MRI series.
• FLAIR Lesion Segmentation Model: Developed using a training set of 340 T1 and FLAIR MRI series.
• MCH Detection Model: Trained on 463 2D T2*GRE/SWI MRI series.

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

The document does not explicitly detail how the ground truth for the training sets was established. It describes the data sources (diverse MRI series from various institutions) and mentions the use of "probabilistic methods and neural network models" for labeling in the device's processing, which implies that these models learn from some form of labeled or pre-segmented data. Given the "computer-aided expert manual segmentation" mentioned for ground truth in performance testing, it's highly probable that similar methods were used for generating labels for the training data, but this is not explicitly stated.

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OnQ Neuro is a fully automated post-processing medical device software intended for analyzing and evaluating neurological MR image data.

• OnQ Neuro is intended to provide automatic segmentation, quantification, and reporting of derived image metrics.
  OnQ Neuro is additionally intended to provide automatic fusion of derived parametric maps with anatomical MRI data.
  OnQ Neuro is intended for use on brain tumors, which are known/confirmed to be pathologically diagnosed cancer.
  OnQ Neuro is intended for comparison of derived image metrics from multiple time-points.
  The physician retains the ultimate responsibility for making the final diagnosis and treatment decision.

OnQ Neuro is a fully automated post-processing medical device software that is used by radiologists, oncologists, and other clinicians to assist with analysis and interpretation of neurological MR images. It accepts DICOM images using supported protocols and performs 1) automatic segmentation and volumetric quantification of brain tumors, which are known/confirmed to be pathologically diagnosed cancer, 2) automatic post-acquisition analysis of diffusion-weighted magnetic resonance imaging (DWI) data and optional automated fusion of derived image data with anatomical MR images, and 3) comparison of derived image metrics from multiple time-points.
Output of the software provides values as numerical volumes, and images of derived data as grayscale intensity maps and as graphical color overlays on top of the anatomical image. OnQ Neuro output is provided in standard DICOM format as image series and reports that can be displayed on most third-party commercial DICOM workstations.
The OnQ Neuro is a stand-alone medical device software package that is designed to be installed in the cloud or within a hospital's IT infrastructure on a server or PC-based workstation. Once installed and configured, the OnQ Neuro software automatically processes images sent from the originating system (MRI scanner or PACS). The software is configured at installation to receive input DICOM files from a network location, and output DICOM to a network destination.
The software is designed without the need for a user interface after installation. Any processing errors are reported either in the output series error report, or system log files.
OnQ Neuro software is intended to be used by trained personnel only and is to be installed by trained technical personnel.
Quantitative reports and derived image data sets are intended to be used as complementary information in the review of a case.
The OnQ Neuro software does not have any accessories or patient contacting components.

Here's a breakdown of the acceptance criteria and study details for the OnQ Neuro device, based on the provided text:

Device: OnQ Neuro
Indications for Use: Fully automated post-processing medical device software for analyzing and evaluating neurological MR image data, providing automatic segmentation, quantification, and reporting of derived image metrics, automatic fusion of parametric maps with anatomical MRI data, and comparison of derived image metrics from multiple time-points. Intended for use on brain tumors, which are known/confirmed to be pathologically diagnosed cancer.

1. Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size: Not explicitly stated. The text mentions "an independent test dataset" for segmentation performance testing.
• Data Provenance: Not explicitly stated. It is not specified if the data was retrospective or prospective or the country of origin.

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

• Number of Experts: Not explicitly stated. The text refers to "expert-labeled segmentations" and "expert rater manual segmentation performance," implying multiple experts, but the exact number isn't quantified.
• Qualifications of Experts: Not explicitly stated beyond "expert" and "radiologist" (in the context of manual segmentations). Specific details like years of experience or board certification are not provided.

4. Adjudication Method for the Test Set

The adjudication method is not explicitly stated. The text mentions "expert-labeled segmentations" as the ground truth, but does not detail how disagreements between experts were resolved (e.g., 2+1, 3+1).

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly stated to have been performed where human readers improve with AI vs. without AI assistance. The performance testing focuses on the accuracy of the automated segmentation against expert-labeled ground truth, indicating a standalone or comparative study with human performance as the ground truth, rather than human performance aided by AI.

• Effect Size: Not applicable, as a comparative effectiveness study with human readers was not described.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, a standalone (algorithm only) performance assessment was done. The "Performance Testing Summary" directly addresses the device's automatic segmentation accuracy ("OnQ Neuro automatic segmentation performance is evaluated by comparing the software-derived segmentations to expert-labeled segmentations"). The device is described as "fully automated" and not having a user interface for manual manipulation after installation. The primary comparison is the AI's output against human expert ground truth.

7. The Type of Ground Truth Used

The type of ground truth used is primarily expert consensus/manual segmentations. The text specifies "expert-labeled segmentations of brain tumors" and "expert rater manual segmentation performance" as the basis for comparison for the segmentation accuracy.

8. The Sample Size for the Training Set

The sample size for the training set is not explicitly stated. The document focuses on the validation of the device, not its training process.

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

How the ground truth for the training set was established is not explicitly stated. The document describes how the ground truth for the test set was established (expert-labeled segmentations), but not for the data used to train the algorithm.

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Meuro Quant is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures and lesions from a set of MR images. Volumetric measurements may be compared to reference percentile data.

NeuroQuant is a fully automated MR imaging post-processing medical device software that provides automatic labeling, visualization and volumetric quantification of brain structures and lesions from a set of MR images and returns segmented images and morphometric reports. The resulting output is provided in a standard DICOM format as additional MR series with segmented color overlays and morphometric reports that can be displayed on third-party DICOM workstations and Picture Archive and Communications Systems (PACS). The high throughput capability makes the software suitable for use in both clinical trial research and routine patient care as a support tool for clinicians in assessment of structural MRIs.

NeuroQuant provides morphometric measurements based on 3D T1 MRI series. The output of the software includes volumes that have been annotated with color overlays, with each color representing a particular segmented region, and morphometric reports that provide comparison of measured volumes to age and gender-matched reference percentile data. In addition, the adjunctive use of the T2 FLAIR MR series allows for improved identification of some brain abnormalities such as lesions, which are often associated with T2 FLAIR hyperintensities.

The NeuroQuant processing architecture includes a proprietary automated internal pipeline that performs artifact correction, atlas-based segmentation, volume calculation and report generation.

Additionally, automated safety measures include automated quality control functions, such as tissue contrast check, atlas alignment check and scan protocol verification, which validate that the imaging protocols adhere to system requirements.

From a workflow perspective, NeuroQuant is packaged as a computing appliance that is capable of supporting DICOM file transfer for input and output of results.

The provided text describes the 510(k) summary for the NeuroQuant device (K170981). Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implied by the performance statistics reported. While explicit acceptance thresholds are not given in a "PASS/FAIL" format, the document presents quantitative results from the performance testing.

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

The document does not explicitly state the sample size used for the test set. It mentions "3D T1 MRI scans" and "3D T1 and T2 FLAIR MRI scan pairs of subjects with brain lesions" were used for evaluation.

The document does not specify the country of origin of the data or whether it was retrospective or prospective.

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

The document states that segmentation accuracy was evaluated by "comparing segmentation accuracy with expert manual segmentations." However, it does not specify the number of experts used or their qualifications (e.g., radiologist with 10 years of experience).

4. Adjudication Method for the Test Set

The document mentions "expert manual segmentations" as the ground truth, but it does not describe any adjudication method (e.g., 2+1, 3+1, none) used to establish this ground truth among multiple experts if more than one was involved.

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

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study or any effect size of how human readers improve with or without AI assistance. The performance testing focuses solely on the device's accuracy and reproducibility against manual segmentation and repeated scans.

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

Yes, a standalone performance evaluation was done. The "Performance Testing" section describes how "NeuroQuant performance was evaluated by comparing segmentation accuracy with expert manual segmentations and by measuring segmentation reproducibility between same subject scans." This refers to the algorithm's performance directly, independent of a human reader's interaction with the output for primary diagnosis.

7. The Type of Ground Truth Used

The ground truth used for the segmentation accuracy evaluation was "expert manual segmentations." For reproducibility, the ground truth was the measurements from repeated scans of the same subjects, with the expectation that the device produces consistent results on these repeated scans.

8. The Sample Size for the Training Set

The document does not specify the sample size used for the training set. It describes the device's "proprietary automated internal pipeline that performs... atlas-based segmentation," and "dynamic probabilistic neuroanatomical atlas, with age and gender specificity." This implies a trained model, but the size of the dataset used for this training is not disclosed.

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

The document states the device uses "atlas-based segmentation" and a "dynamic probabilistic neuroanatomical atlas, with age and gender specificity." This suggests the training involves the creation or utilization of an anatomical atlas, which typically involves expert anatomical labeling and segmentation of a representative set of MR images to build probabilities for different brain regions. However, the specific methodology for establishing this ground truth for the training set (e.g., number of experts, their qualifications, adjudication) is not detailed in this summary.

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NeuroQuant™ is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures from a set of MR images. This software is intended to automate the current manual process of identifying, labeling and quantifying the volume of segmental brain structures identified on MR images.

NeuroQuant™ Medical Image Processing Software

The provided text is a 510(k) clearance letter from the FDA for the NeuroQuant™ Medical Image Processing Software. It does not contain specific details about the acceptance criteria or a study proving the device meets those criteria. Such information is typically found in the 510(k) summary or the full submission, which is not provided here.

Therefore, I cannot extract the requested information from the given text. The document only confirms that the device has been found substantially equivalent to a predicate device and can be marketed.

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AutoAlign™ Atlas-Based Image Registration software is intended to provide an output registration matrix that may be utilized to align an MRI brain scan to a known and consistent anatomic orientation, a process known as image registration. AutoAlign™ Atlas-Based Image Registration software is intended to be marketed as a software device that can provide improvements to the manual processes of image registration. The dominant use of AutoAlign™ Atlas-Based Image Registration software is its integration into proprietary MR image software packages by MRI scanner manufacturers to allow users to generate consistent patient image registrations for image acquisition, a process otherwise known as AutoSlice Prescriptioning.

AutoAlign™ Atlas-Based Image Registration software is intended to provide an output registration matrix that may be utilized to align an MRI brain scan to a known and consistent anatomic orientation, a process known as image registration. AutoAlign™ Atlas-Based Image Registration software is intended to be marketed as a software device that can provide improvements to the manual processes of image registration. The dominant use of AutoAlign™ Atlas-Based Image Registration software is its integration into proprietary MR image software packages by MRI scanner manufacturers to allow users to generate consistent patient image registrations for image acquisition, a process otherwise known as AutoSlice Prescriptioning.

AutoAlign™ Atlas-Based Image Registration has a feedback mechanism which measures and reports alignments which have the potential to be outside of stated specifications. This is reflected in as a "Measurement Index" value which is the average of the Mahalanobis distance for the voxel intensity of all atlas points to the patient images supplied for alignment.

The provided document describes the validation of the AutoAlign™ Atlas-Based Image Registration software. Here's a breakdown of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size for the Test Set and Data Provenance

• Sample Size: 259 MR image volumes.
• Data Provenance: Retrospective, anonymous, low-resolution multispectral MR scans from actual adult subjects (ages 15-89) with both normal and abnormal pathologies, supplied by Siemens AG, Erlangen, Germany.

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

• Number of Experts: One expert.
• Qualifications: Ph.D. trained in neurosciences.

4. Adjudication Method for the Test Set

The document does not describe an explicit adjudication method involving multiple experts for the ground truth. Instead, it states that "Post alignment measurements were made by an expert" (Ph.D. trained in neurosciences). Thus, the ground truth was established by a single expert.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The study focused on the standalone performance of the algorithm.

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

Yes, a standalone study was done. The "Effectiveness" section describes testing the AutoAlign software's ability to align MR Neuro images. The measurements were made post-alignment by a single expert, indicating an evaluation of the algorithm's output. The "Measurement Index" serves as a safety mechanism for operator review but is not part of the core performance validation against the specified criteria, which are purely algorithmic accuracy metrics.

7. The Type of Ground Truth Used

The ground truth used was expert consensus (from a single expert) on anatomical landmarks and angles. The expert made measurements on the aligned images to determine the positions of the anterior commissure (AC), posterior commissure (PC), and inter-hemispheric plane (IHP), as well as specific angles.

8. The Sample Size for the Training Set

The document does not explicitly state the sample size for the training set. It mentions the "embedded reference neuroanatomic Atlas" but does not detail its creation or the data used to train the AutoAlign algorithm. The 259 cases were used to "validate and test the efficacy" of the software, implying they were a test set, not a training set.

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

The document does not describe how the ground truth for the training set (i.e., the "embedded reference neuroanatomic Atlas") was established.

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AutoAlign software is intended to provide an output registration matrix that may be utilized to align an MRI brain scan to a known and consistent 3-dimensional (3-D) atlas of the human brain. AutoAlign software will be marketed as a software device that can provide improvements to the manual processes of MRI brain image registration.

To be utilized for the registration of brain images for MRI.

The device (software) operates by comparing a subjects' brain MR localizer images to a preexisting atlas of the human brain. The software then calculates a set of coordinates that can be used to align subsequent MRI images to the atlas. The accuracy of the alignment is measured and then programmatically reported. AutoAlign is a software device that provides the following features:

• . Imports MRI brain images
• Calculates and then outputs an optimized 3-D registration matrix that permits . alignment of the brain, regardless of the actual physical position of the subject's head in the image. For instance, in the test alignment:
  • o in the sagittal image, the intra-hemispheric plane is at the center slice of the MRI volume so the anterior & posterior commeasures (ac-pc line) are visible on that slice.
  • in the axial image, the intra-hemispheric plane is parallel to the Y axis. o
  • in the coronal image, the intra-hemispheric plane is parallel to the Y axis. o
• It can provide consistent scan/rescan alignment between separate scanning ● sessions within boundaries established and documented in Product Labeling Instructions.
• This software can be utilized by a MRI scanner original equipment manufacturer ● (OEM) to improve the workflow and automation of MRI brain study acquisitions.
• AutoAlign does not alter or otherwise modify the initial MR localizer image in any way
• The AutoAlign system does not have any adverse affects on health. This tool ● operates as a stand-alone software device, receives the MR scout localizer as input and outputs an optional registration prescription. AutoAlign does not alter or otherwise modify the initial MR localizer image in any way, and Labeling stipulates a review of the output registration by a trained MR operator.

The provided text does not contain detailed acceptance criteria or a study that explicitly proves the device meets such criteria. It outlines the intended use, device description, and acknowledges that "Final Verification and Validation of the software has not been completed" and will be notified to the FDA upon completion. It mentions "Performance Testing: AutoAlign will successfully complete testing as detailed in the Clinical Performance Summary," but this summary is not provided in the excerpt.

Therefore, I cannot fulfill your request for a table of acceptance criteria, reported device performance, sample sizes, expert qualifications, adjudication methods, MRMC study details, standalone performance, or ground truth details.

The document is an Abbreviated 510(k) Summary and a subsequent FDA clearance letter. It serves to explain the device's purpose and its substantial equivalence to predicate devices, rather than providing a detailed clinical performance study report.

Key information from the document related to performance (but not meeting your request for detailed study results):

• Intended Use: "AutoAlign software is intended to provide an output registration matrix that may be utilized to align an MRI brain scan to a known and consistent 3-dimensional (3-D) atlas of the human brain. AutoAlign software will be marketed as a software device that can provide improvements to the manual processes of MRI brain image registration."
• Device Description (related to performance): "The accuracy of the alignment is measured and then programmatically reported." And, "It can provide consistent scan/rescan alignment between separate scanning sessions within boundaries established and documented in Product Labeling Instructions."
• Performance Testing: "AutoAlign will successfully complete testing as detailed in the Clinical Performance Summary." (This summary is missing).
• Regulatory Status: The FDA's clearance letter indicates the device is cleared for marketing based on substantial equivalence, with the expectation that verification and validation will be completed.

Without the "Clinical Performance Summary" or similar documentation, it's impossible to provide the requested details about acceptance criteria and the study that proves the device meets them.

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Deep Gray is intended to measure the volume of any brain structure and tissue from a set of MR images. It provides visualization tools, basic and advanced regions of interest drawing features and volumetric quantification. Deep Gray is to be used by trained physicians.

Visualization/Processing/Analysis of brain images from MR scanners.

Deep Gray is a software device that provides the following features:

• Import of MR brain images (DICOM 3.0 format).
• Multi-frame and multi-orientation image display.
• Basic regions of interest drawing tools: free hand drawing, filled polygon . drawing. Labels can be associated with drawn objects.
• Advanced drawing tool: semi-automatic labeling of normal brain structures and . tissues.
• . Generation of a report listing the volumes of labeled structures and tissues.

The operator can choose to manually draw and label brain structures and tissues, or they can chose to perform a semi-automatic labeling, followed by visual inspection and manual adjustment. The Deep Gray system does not have any adverse affects on health. This tool measures and displays the volume of regions of interest. The operator can choose to accept, modify, or reject the volume and/or label suggested by the program.

The document provided a summary of the performance testing and clinical evaluation of the Deep Gray system, focusing on its semi-automatic labeling feature for brain structures. However, it does not include detailed acceptance criteria, specific reported performance metrics against those criteria, or comprehensive information about the study design that would be required to fully answer all aspects of your request.

Here's a breakdown of what can be extracted and what is missing based on the provided text:

Based on the provided text, the Deep Gray system underwent a clinical evaluation to compare its semi-automatic labeling feature with manual labeling of brain structures.

Here's an analysis of the requested information:

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

• Acceptance Criteria: Not explicitly stated in the provided text.
• Reported Device Performance: The document only states that "Laboratory performance comparisons between the semi-automatic labeling feature and manual labeling of brain structures has been successfully completed." No specific quantitative metrics (e.g., accuracy, precision, dice coefficient, volume difference, time difference) are provided.

Therefore, a table cannot be constructed with the available information.

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: Not specified in the provided text.
• Data Provenance: Not specified (e.g., country of origin, 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)

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The document only mentions "manual labeling," implying that human experts performed this, but their number and qualifications are not detailed.

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

• Adjudication Method: Not specified.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• MRMC Study: The document describes "performance comparisons between the semi-automatic labeling feature and manual labeling." While it compares an AI-assisted method (semi-automatic) to a manual method, it does not explicitly state that it was an MRMC study designed to measure the improvement of human readers with AI assistance. It seems to compare the output of the semi-automatic process to the output of manual labeling.
• Effect Size: Not provided.

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

• Standalone Performance: The description of the device states, "The operator can choose to manually draw and label brain structures and tissues, or they can chose to perform a semi-automatic labeling, followed by visual inspection and manual adjustment." This implies that the semi-automatic labeling is designed to be used with human oversight and potential adjustment. Therefore, the "clinical evaluation" appears to compare this semi-automatic, human-reviewed process against manual labeling, rather than a purely standalone algorithm without any human intervention. The term "algorithm only" is not explicitly addressed, but the operational description suggests human-in-the-loop.

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

• Type of Ground Truth: The ground truth for the comparison was established by "manual labeling of brain structures." This suggests expert-derived ground truth, where human experts manually delineated the structures. There's no mention of pathology or outcomes data.

8. The sample size for the training set

• Training Set Sample Size: Not specified in the provided text. The document mentions software development, testing, and validation, but not the specific details of a training set for a machine learning model.

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

• Training Set Ground Truth Establishment: Not specified.

Summary of Missing Information:

The provided 510(k) summary is very high-level regarding the performance testing and clinical evaluation. Critical details such as specific acceptance criteria, quantitative performance metrics, sample sizes, expert qualifications, and detailed study methodologies are not included. This type of information is typically found in the full submission documents or an accompanying clinical report, not an abbreviated 510(k) summary.

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