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SIS Software is an application intended for use in the viewing, presentation and documentation of medical imaging, including different modules for image processing, image fusion, and intraoperative functional planning where the 3D output can be used with stereotactic image guided surgery or other devices for further processing and visualization. The device can be used in conjunction with other clinical methods as an aid in visualization of the subthalamic nuclei (STN).

SIS Software uses machine learning and image processing to enhance standard clinical images for the visualization of the subthalamic nucleus ("STN"). The SIS Software supplements the information available through standard clinical methods, providing adjunctive information for use in visualization and planning stereotactic surgical procedures. SIS Software provides a patient-specific, 3D anatomical model of the patient's own brain structures that supplements other clinical information to facilitate visualization in neurosurgical procedures.

The version of the software that is the subject of the current submission (Version 3.6.0) is a modification to the predicate SIS Software version 3.3.0 that was cleared under K183019. The subject and predicate devices rely on the same core technological principles. The only minor changes were modifications to enable the use of a more comprehensive MR to post operation CT registration methodology, and image processing techniques for CT images acquired with gantry tilt. The web user interface has also been enhanced to allow additional options for administrators/supervisors, and has added audit logging functions.

The provided text is a 510(k) summary for SIS Software Version 3.6.0. It describes the device, its intended use, and argues for its substantial equivalence to a predicate device (SIS Software Version 3.3.0). However, it does not provide detailed acceptance criteria or a comprehensive study report with the level of detail requested for each point in the prompt.

The document states that "software verification and validation testing has been repeated to validate that the modified software functions as specified and performs similarly to the predicate device." It also mentions "MRI to CT registration testing using the new methodology, which demonstrated that the software continued to register MR images to the CT space. The error was within the acceptance criteria, and was comparable to that for SIS Software version 3.3.0, which used the same protocol."

Based on the provided text, here is an attempt to address your request, highlighting where information is not provided in the source document.

Description of Acceptance Criteria and Proving Device Meets Criteria (Based on Provided Text)

The SIS Software Version 3.6.0 is a modification of a previously cleared device (Version 3.3.0). The study aims to demonstrate that the updated software continues to function as specified and performs similarly to the predicate device, specifically regarding MRI to CT registration and image processing for gantry-tilted CT scans. The primary acceptance criterion broadly seems to be that performance ("error") for the modified functions remains "within the acceptance criteria" and "comparable" to the predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: The document states that the MRI to CT registration testing used a "new methodology," and that the CT image processing for gantry tilt used "the same CT scans that were used in the validation testing for the predicate device." The specific numerical sample size (number of MR and CT scans) for the test sets is not provided.
• Data Provenance: The document does not provide information regarding the country of origin of the data or whether the data was retrospective or prospective.

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

• Not provided. The document describes software validation and verification testing but does not mention the use of experts or their qualifications for establishing ground truth for the test set.

4. Adjudication Method for the Test Set

• Not provided. The document does not describe any adjudication method.

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

• No. The document describes a software validation study demonstrating that the modified software performs comparably to its predicate. It does not describe an MRMC comparative effectiveness study involving human readers with and without AI assistance. Therefore, no effect size for human reader improvement is provided.

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

• Yes, implicitly. The performance data section describes "software verification and validation testing" which "demonstrated that the software continued to register MR images to the CT space" and that "the cropping image processing does not affect the performance of the software." This implies standalone algorithm performance testing. No human-in-the-loop studies are mentioned.

7. The Type of Ground Truth Used

• The document implies that the ground truth for registration and segmentation performance was established against results from the predicate device and internal specifications/protocols ("within the acceptance criteria," "comparable to that for SIS Software version 3.3.0," "functions as specified"). It does not explicitly state the type of ground truth used (e.g., expert consensus, pathology, outcome data, etc.) beyond comparison to the predicate's performance.

8. The Sample Size for the Training Set

• Not provided. The document does not discuss the training set, only the validation/test set. The device uses "proprietary algorithms" and states "minor modifications to the registration and CT image processing techniques are introduced... the basis for the device algorithm remain the same." This suggests the core algorithm was developed previously.

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

• Not provided. As the training set is not discussed, information on how its ground truth was established is absent.

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SIS Software is an application intended for use in the viewing, presentation of medical imaging, including different modules for image processing, image fusion, and intraoperative functional planning where the 3D output can be used with stereotactic image quided surgery or other devices for further processing and visualization. The device can be used in conjunction with other clinical methods as an aid in visualization of the subthalamic nuclei (STN).

SIS Software is an application intended for use in the viewing, presentation and documentation of medical imaging, including different modules for image processing, image fusion, and intraoperative functional planning where the 3D output can be used with stereotactic image guided surgery or other devices for further processing and visualization. The device can be used in conjunction with other clinical methods as an aid in visualization of the subthalamic nuclei (STN).

SIS Software uses machine learning and image processing to enhance standard clinical images for the visualization of the subthalamic nucleus ("STN"). The SIS Software supplements the information available through standard clinical methods, providing adjunctive information for use in visualization and planning stereotactic surgical procedures. SIS Software provides a patient-specific, 3D anatomical model of the patient's own brain structures that supplements other clinical information to facilitate visualization in neurosurgical procedures. The version of the software that is the subject of the current submission (Version 3.3.0) can also be employed to co-register a post-operative CT scan with the clinical scan of the same patient from before a surgery (on which the software has already visualized the STN) and to segment in the CT image (where needed), to further assist with visualization.

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

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria and performance data are presented for three main functionalities: STN Visualization, Co-Registration, and Segmentation.

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

• STN Visualization Test Set: 68 STNs (from 34 subjects).
  • Data Provenance: Not explicitly stated regarding country of origin. The data was "completely separate from the data set that was used for development" and "none of the 68 STNs were part of the company's database for algorithm development and none were used to optimize or design the company's software." This indicates it was a prospective test set, in the sense that it was not used for model development.
• Co-Registration Test Set: 5 MR series and 1 CT series of a phantom brain. This suggests a synthetic, controlled test environment rather than patient data.
• Segmentation Test Set: 26 post-surgical CT scans that contained leads, with a total sample size of 45 electrodes.
  • Data Provenance: Not explicitly stated regarding country of origin or whether it was retrospective or prospective patient data, but it involved "post-surgical CT scans."
• Anomaly Detection Test Set: The same 68 cases (68 total STNs, 65 successful/3 failed for v1.0.0 and 66 successful/2 failed for v3.3.0) used for STN Visualization.
  • Data Provenance: Same as STN Visualization.
• STN Smoothing Functionality Test Set: The shapes of the visualized targets from the "verification accuracy testing" were compared. This likely refers to the same 68 STNs from the STN Visualization study.

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

• STN Visualization: The text mentions "ground truth STNs (manually segmented clinical images superimposed)", but it doesn't specify the number or qualifications of experts who performed these manual segmentations.
• Co-Registration: "6 fiducial points were marked by an expert." The qualification of this expert is not provided.
• Segmentation: "ground truth segmentations were generated by 2 experts." The qualifications of these experts are not provided.
• Anomaly Detection: Ground truth for anomaly detection was defined by whether visualizations were "Inaccurate visualization" or "Accurate visualization," based on the STN visualization success criteria (>2mm vs <=2mm distance relative to ground truth). The establishment of this underlying ground truth (manual segmentation of STNs) is not detailed beyond what's mentioned for STN Visualization.
• STN Smoothing Functionality: Ground truth for accuracy was based on "verification accuracy testing," which likely refers back to the STN visualization ground truth.

4. Adjudication Method for the Test Set

• STN Visualization: Not explicitly stated. The "ground truth STNs (manually segmented clinical images superimposed)" implies a reference standard, but how discrepancies or initial ground truth was agreed upon if multiple experts were involved is not mentioned.
• Co-Registration: A single expert marked points. No adjudication method mentioned.
• Segmentation: "ground truth segmentations were generated by 2 experts." It does not mention an adjudication process if their segmentations differed (e.g., 2+1, 3+1). It's possible they reached consensus, or one might have corrected the other, but this is not stated.
• Anomaly Detection: No (applicable) adjudication as the ground truth was based on quantitative metrics from STN visualization.
• STN Smoothing Functionality: No (applicable) adjudication, as it relies on quantitative comparison to ground truth from STN visualization.

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 was mentioned. The study focuses on the device's standalone performance in providing aid for visualization and measurement. The claim is that the device provides "adjunctive information" and is an "aid in visualization." No human reader performance data (with or without AI) is provided.

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

Yes, the studies described for STN Visualization, Co-Registration, and Segmentation report the performance of the algorithm itself, without human-in-the-loop interaction for the specific quantitative metrics used. The anomaly detection component also describes the algorithm's performance in identifying anomalies.

7. The Type of Ground Truth Used

• STN Visualization:
  • Expert Consensus/Manual Segmentation: The ground truth for STN visualization was "manually segmented clinical images superimposed" and "High Field (7T) MRI." The 7T MRI serves as a high-resolution reference considered superior for STN visualization, and the manual segmentations on these images would form the core of the ground truth.
• Co-Registration:
  • Expert Marking on Phantom: Ground truth was based on fiducial points marked by an expert on a physical phantom.
• Segmentation:
  • Expert Segmentation: Ground truth was established by "2 experts" who generated segmentations of electrodes from CT images and manually aligned 3D components to those segmentations.
• Anomaly Detection:
  • Metric-Based (Derived from STN Visualization GT): Ground truth for anomaly detection was defined by the quantitative "accuracy" of the STN visualization (<2mm vs >2mm distance to the expert-derived ground truth).
• STN Smoothing Functionality:
  • Metric-Based (Derived from STN Visualization GT): Ground truth for evaluating smoothing was based on "COM, SD and DC" relative to the STN visualization ground truth.

8. The Sample Size for the Training Set

• The document states that the STN visualization validation data set (68 STNs) was "completely separate from the data set that was used for development" and "none were used to optimize or design the company's software."
• Regarding the anomaly detection component, it mentions "two separate commonly used outlier detection machine learning models were trained using the brains from the training set." The specific sample size for this training set is not provided.
• For co-registration, there's no mention of a training set as it appears to be a direct registration process, not a machine learning model.
• For segmentation, it's not explicitly stated if a training set was used for the automated segmentation; the validation focuses on the comparison to expert ground truth.

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

• For the anomaly detection component, it states the models were "trained using the brains from the training set, from which the same brain geometry characteristics were extracted." It then describes how anomaly scores were combined. However, the method for establishing the ground truth on this training set (i.e., what constituted an "anomaly" vs "non-anomaly" during training) is not detailed in the provided text. It presumably involved similar principles of accurate vs. inaccurate visualizations, but the source and method of that ground truth for training are not specified.
• For any other machine learning components (like the core STN visualization algorithm), the document states the methodology "relies on a reference database of high-resolution brain images (7T MRI) and standard clinical brain images (1.5T or 3T MRI)." The algorithm "uses the 7T images from a database to find regions of interest within the brain (e.g., the STN) on a patient's clinical (1.5 or 3T MRI) image." This implies the 7T MRI data serves as a form of ground truth for training the algorithm to identify STNs on clinical MRI, but the specific process of creating that ground truth from the 7T data (e.g., manual segmentation by experts on 7T) is not detailed.

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SIS Software is an application intended for use in the viewing, presentation of medical imaging, including different modules for image fusion, and intraoperative functional planning where the 3D output can be used with stereotactic image quided surgery or other devices for further processing and visualization. The device can be used in conjunction with other clinical methods as an aid in visualization of the subthalamic nuclei (STN).

SIS Software is an application intended for use in the viewing, presentation and documentation of medical imaging, including different modules for image processing, image fusion, and intraoperative functional planning where the 3D output can be used with stereotactic image guided surgery or other devices for further processing and visualization. The device can be used in coniunction with other clinical methods as an aid in visualization of the subthalamic nuclei (STN).

SIS Software uses machine learning and image processing to enhance standard clinical images for the visualization of the subthalamic nucleus ("STN"). The SIS Software supplements the information available through standard clinical methods, providing additional, adjunctive information to surgeons, neurologists and radiologists for use in visualization and planning stereotactic surgical procedures. SIS Software provides a patient specific, 3D anatomical model of the patient's own brain structures that supplements other clinical information to facilitate visualization in neurosurqical procedures. The software makes use of the fact that some structures in the brain are not easily visualized in 1.5T or 3T clinical MRJ, but are better visualized using high-resolution and high-contrast 7T MRI.

The company's software methodology relies on a reference database of high-resolution brain images (7T MRI) and standard clinical brain images (1.5T or 3T MRI). The 7T images allow visualization of anatomical structures that are then used to find regions of interest within the brain (i.e., the STN) on a patient's clinical image.

SIS visualization is incorporated in the standard clinical MR data, thereby not changing the current standard-of-care workflow protocol and does not require any additional visualization software or hardware platforms.

Here's a breakdown of the acceptance criteria and the study that proves the SIS Software meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are focused on the accuracy of the Subthalamic Nuclei (STN) visualization. The study compared the machine-predicted STN to ground truth STN.

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

• Pivotal Validation Test Set: Images from 34 subjects, resulting in 68 STNs (each subject has two STNs).
• Data Provenance: The text does not explicitly state the country of origin. It indicates that the data was retrospective, as it was composed of previously scanned clinical MRI (1.5T and 3T) and High Field (7T) MRI. Crucially, none of these 68 STNs were part of the company's database for algorithm development or optimization/design, ensuring an independent validation set.

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

The document does not specify the number of experts or their qualifications for establishing the ground truth for the pivotal validation test set. It only mentions "ground truth STNs (manually segmented clinical images and 7T images superimposed)."

For the developmental testing phase, it refers to the STN as "segmented on the 7T image of that subject." This implies expert segmentation, but details on the number or qualifications of these experts are not provided.

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1, none) for the test set ground truth. It states that the ground truth STNs were "manually segmented clinical images and 7T images superimposed," suggesting a single ground truth was established, likely by an expert or team of experts, but without specifying a review and adjudication process.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The study focuses on evaluating the standalone performance of the SIS Software against a pre-established ground truth. There is no mention of human readers assisting with or without AI, or any comparison of human reader performance.

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

Yes, a standalone (algorithm only) performance study was conducted. The "Performance Data" section describes how the "subject machine-learning method then predicted the subthalamic nucleus (STN)" and how the "SIS visualization via the subject software" was compared to ground truth. There is no indication of human interaction or interpretation improving the algorithm's output during the validation.

7. The Type of Ground Truth Used

The ground truth used was primarily based on expert defined anatomical structures from High-Field MRI (7T). Specifically, for the pivotal validation testing, it involved "ground truth STNs (manually segmented clinical images and 7T images superimposed)." For developmental testing, it was "the STN as segmented on the 7T image." The 7T MRI is noted to "allow visualization of anatomical structures that are then used to find regions of interest within the brain (i.e., the STN) on a patient's clinical image," implying that the 7T images serve as a higher-fidelity reference for the ground truth.

8. The Sample Size for the Training Set

The document refers to a "reference database of high-resolution brain images (7T MRI) and standard clinical brain images (1.5T or 3T MRI)" which the software methodology "relies on." However, it does not explicitly state the sample size of this training database.

For the developmental testing, which seems to analyze aspects of the training/development process, it mentions "10 subject datasets that were retrospectively selected from the locked SIS reference database." This is a smaller subset used for a specific "leave-one-out" test, not the full size of the training set.

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

The ground truth for the training set (referred to as the "reference database") was established by using 7T MRI images which "allow visualization of anatomical structures that are then used to find regions of interest within the brain (i.e., the STN)." This implies that experts segmented or labeled the STN structures on these high-resolution 7T images to create the ground truth for the training data that the machine learning model learned from.

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