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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.

• 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.

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