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BrightMatter Guide with SurfaceTrace Registration is intended as a planning and intraoperative guidance system to enable open and percutaneous computer assisted surgery. The system is indicated for medical conditions requiring neurosurgical cranial procedures where the use of computer assisted planning and surgery may be appropriate. The system can be used for intraoperative guidance where a reference to a rigid anatomical structure can be identified. The user should consult the "Accuracy Characterization" section of the User Manual to assess if the system is suitable for their needs.

The subject device, BrightMatter Guide with SurfaceTrace Registration is a modification of the software component of BrightMatter Navigation system that is presented in K142024. The system is a planning and image guided surgical system that enables computer assisted surgery where use of stereotactic image guidance may be considered appropriate. In particular, the device is suitable for neurosurgical cranial procedures. The planning functionality of the device is provided by an already cleared device, BrightMatter Planning (K140337). The remaining system provides a sequence of discrete workflow activities (or phases) that guide a surgeon through the process of data preparation for the surgical procedure. Then the device aids the surgeon in visualizing the location of the surgical tools relative to clinical images and physical location of the patient.

Following is a summary of steps involved in data preparation and registration of the patient's head position relative to pre-surgical clinical images:

• . Importing plan and imaging data
• Reviewing and selecting a previously generated surgical plan .
• . Optionally fusing (merging or co-registering) additional imaging data
• Registering the clinical images to the patient using either Point registration or . SurfaceTrace based registration.

Following steps are provided as visualization tools during the execution of the surgical procedure:

• . Aid in visualizing location of the surgical site as planned by the surgeon (using BrightMatter Planning software, K140337)
• . For trajectory-centric procedures, help visualize insertion of tracked surgical tools by identifying location of surgical tool's position and orientation relative to clinical images and the surgical plan developed by the surgeon
• . Visualize location of tracked surgical tools after the intended target location has been reached
• . The purpose of this 510k submission is introduce a new registration methodology using changes that are limited to the software component of the previously cleared system. Key functional components of the system are an optical tracking sub-system, navigated surgical tools, custom software application and an external display. The navigated surgical tools are tracked using single-use passive reflective markers (K033621) that are attached to the surgical tools prior to each surgical procedure.

The surgical display and tracking camera are mounted on an Auxiliary Cart. The computer is housed in a Navigation Cart. These components have been cleared as part of the BrightMatter Navigation system (K142024).

As with many systems in the OR, not all components need to be sterile during use. The only subcomponents that come in contact with the patient are the Pointing Tool, Port Reference Tool and Calibration Block. These tools fit in the limited contact duration category. The tools have been cleared as part of the BrightMatter Navigation system (K142024).

Here's a breakdown of the acceptance criteria and study information for the BrightMatter Guide with SurfaceTrace Registration device, based on the provided document:

Acceptance Criteria and Device Performance

Study Details:

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

   • Test Set Sample Size: The document refers to an "accuracy measurement phantom of similar volume to an adult head." It does not specify a numerical sample size for individual measurements on this phantom but implies a comprehensive set of measurements taken at various locations (center and boundaries of the tracking volume).
   • Data Provenance: The study was conducted internally by Synaptive Medical Inc. in Canada (based on the submitter's address). The data is prospective in the sense that the testing was performed on the device to characterize its performance for this submission.

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

   • The ground truth for the test set (the accuracy measurement phantom) was established using a Coordinate Measurement Machine (CMM). This implies mechanical measurement, not expert human assessment directly for the ground truth of the phantom. No human experts are explicitly mentioned as establishing the ground truth for the CMM measurements themselves.

3. Adjudication Method for the Test Set:

   • Not applicable. The ground truth for the phantom was established by a CMM, an objective mechanical measurement, not through human consensus.

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

   • No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly described. The testing focused on the standalone accuracy and performance of the device's new registration method. The "Design Validation" mentions simulated use with intended users, but this is described as confirming substantial equivalence and not an MRMC study measuring reader improvement with AI assistance.

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

   • Yes, a standalone performance evaluation was conducted. The "Characterization of system accuracy" section directly assesses the device's ability to measure positional and angular errors using the SurfaceTrace registration method on an accuracy phantom, explicitly stating "Positional error was measured to be less than 2 mm and angular error was measured to be less than 2 degrees." This is an algorithm-only assessment of the core accuracy of the registration.

6. Type of Ground Truth Used:

   • Mechanical Measurement (CMM): The ground truth for the accuracy phantom was obtained using a Coordinate Measurement Machine (CMM).

7. Sample Size for the Training Set:

   • The document does not specify the sample size for any training set. It describes a modification to the software component of a previously cleared device and focuses on the performance characterization of the new SurfaceTrace Registration method. This submission is for a modification, and while the underlying BrightMatter Navigation system would have had development, the details of its training data are not provided here.

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

   • Since the document does not specify a training set sample size or details about a training set, it does not provide information on how its ground truth was established.

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Synaptive ImageDrive Pro is a device that displays medical images (including mammograms) and data from various imaging sources. Images and data can be viewed, communicated, processed and displayed within the system or across computer networks at distributed locations.

Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretations. Mammographic images may only be interpreted using an FDA approved monitor that offers at least 5 Megapixel resolution and meet other technical specifications reviewed and accepted by the FDA.

Typical users of this system are trained professionals, including but not limited to radiologists, physicians, nurses, medical technicians, and assistants.

Synaptive ImageDrive Pro is a medical imaging informatics system that allows the storage, management, display and analysis of imaging and non-imaging data. The Synaptive ImageDrive Pro data repository can be populated either by manually uploading data through the user interface (UI) or in an unattended fashion by automatically receiving DICOM objects or HL7 messages from other information systems such as PACS and EMRs. Automatic data processing rules can be configured to process data when it arrives in the system. An example of this is when pre-operative MR scans are done and sent to ImageDrive to ensure that they were acquired using appropriate scan protocol before they are consumed downstream by other Synaptive applications, such as BrightMatter Plan (cleared as a Class-II device, K140337). Once in the system, the data is indexed so that it can be easily searched in the future. Imaging data can also be de-identified and securely shared with authorized persons. Further, the system provides an extensible architecture to enable local or distributed processing of the data. As data accumulates in the system, analytics can be generated to summarize, for example, intra- and inter-patient statistics and trends in surgical treatment planning based on surgical plan data that is output from external software that can generate data in compatible formats. An example of such external system is the surgical planning software manufactured by this applicant - - BrightMatter Plan. The subject device is composed of the following key features: A hierarchical folder system Three data storage areas Data processing functionality Analytics capability Image viewing capability

The provided text does not contain specific acceptance criteria or an explicit study describing the device's performance against those criteria in a quantitative manner. The document is a 510(k) summary for the Synaptive ImageDrive Pro, focusing on demonstrating substantial equivalence to a predicate device rather than presenting detailed performance metrics from a dedicated clinical or non-clinical study to meet specific acceptance criteria.

However, based on the non-clinical testing and design validation sections, we can infer the intent of the acceptance criteria was to ensure the device performs as intended and is safe and effective.

Here's an attempt to answer your request based on the available information, with many points marked as "N/A" or "Not specified" due to the nature of the document:

1. Table of acceptance criteria and the reported device performance

Since specific quantitative acceptance criteria are not provided, I will infer general categories of performance based on the non-clinical testing performed.

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

• Sample size for the test set: Not specified. The document mentions "System validation testing using intended users" but does not provide details on the number of users or the test cases/data used.
• Data provenance: Not specified. It's a medical imaging informatics system that displays images from "various imaging sources," but the origin of data used for testing is not detailed. The product description indicates it receives DICOM objects or HL7 messages from PACS and EMRs, implying it handles clinical data.

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

• Not specified. The document mentions "System validation testing using intended users," which implies clinical professionals, but does not detail how ground truth was established for the validation tests or how many experts were involved.

4. Adjudication method for the test set

• 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

• No. The document explicitly states: "This technology is not new, therefore a clinical study was not considered necessary prior to release." The device is a Picture Archiving and Communication System (PACS) and generally, these systems are cleared based on substantial equivalence to existing technology, and not typically through MRMC studies that assess reader performance with and without AI. The device described does not appear to have an "AI" component that assists readers in interpretation in the way that would necessitate such a study.

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

• The device itself is a PACS for viewing, communicating, processing, and displaying medical images. It's not an algorithm that performs a diagnostic task independently. Its "performance" is tied to its functional capabilities (storage, management, display, processing, communication). Software verification and system validation were done, which tests the algorithm/system's internal functionality.

7. The type of ground truth used

• Not explicitly stated for the "test set" in the context of diagnostic performance. For general software verification and system validation, the "ground truth" would be the expected behavior of the system as defined by its requirements and design specifications. For the "intended users" validation, it would be the successful execution of tasks by users in a simulated environment.

8. The sample size for the training set

• Not applicable. This device is a PACS, not a machine learning algorithm that requires a "training set" in the conventional sense. Its "training" is in its development and programming, rather than learning from data.

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

• Not applicable, as there is no "training set."

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To provide for access and allow for visualization of the surgical field during brain and spinal surgery. Indications may include subcortical access to diseases such as the following:

• Primary/Secondary Brain Tumors
• Vascular Abnormalities/Malformations
• Intraventricular Tumors/Cysts

The NICO BrainPath and Accessories are designed to provide minimally invasive access to neurological tissues. The design specifically supports the creation of an atraumatic surgical corridor to access most areas of the brain. The BrainPath also facilitates the expanding neurosurgical armamentarium of trajectory planning software (e.g., Synaptive® Medical Inc. BrightMatter® Planning Software, K140337), navigation (e.g., Synaptive BrightMatter Navigation System, K142024), optics, corridor resection (e.g., NICO Myriad), and biopsy. To date, the BrainPath technology has been used to successfully access primary and secondary brain tumors, vascular abnormalities or malformations, and intraventricular tumors and cysts.

The BrainPath consists of multiple-sized reusable and re-sterilizable obturators with coordinating single patient use disposable sheaths. The obturator and sheath are assembled in the operating room immediately prior to use. After placement, the obturator is removed leaving behind the sheath which provides a 13.5 mm surgical corridor to the lesion or abnormality.

The BrainPath Accessories include a "manipulation tool," which is similar to a dental probe and is used for manipulating the position of the sheath after it has been placed. The accessories also include "shepherd's hooks" for attaching to various commercially available retractors, and a sterilization tray for the reusable components (i.e., obturators and manipulation tools).

This document is a 510(k) premarket notification for the NICO® BrainPath® and Accessories, a medical device for neurosurgery. It does not describe a study that proves the device meets specific acceptance criteria related to its clinical efficacy in the context of an AI/ML powered device. Instead, it focuses on demonstrating substantial equivalence to a predicate device (NICO Brain Port, K120691) through non-clinical testing.

Therefore, many of the requested elements for an AI/ML powered medical device study are not applicable or cannot be extracted from this document, as this is a traditional medical device submission.

Here's a breakdown of the available information based on your request, with an emphasis on what is present in the document and what is not:

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

The document provides a table of "Non-Clinical Testing" with "Result/Conclusion". These are the acceptance criteria and performance for a traditional medical device, not an AI/ML powered one.

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

• Not Applicable. This document describes non-clinical engineering and biocompatibility testing for a surgical device, not a study involving patient data or test sets in the context of AI/ML.

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)

• Not Applicable. Ground truth, in the context of diagnostic accuracy, is not established in this type of submission. The "ground truth" for the non-clinical tests is based on established engineering standards and validated methods (e.g., ISO standards for biocompatibility, cleaning validation protocols).

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

• Not Applicable. No adjudication method is described as this is not a study assessing diagnostic performance.

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

• No. An MRMC comparative effectiveness study was not done. This device is a surgical instrument, not an AI-powered diagnostic tool. The document states that the BrainPath "facilitates the expanding neurosurgical armamentarium of trajectory planning software (e.g., Synaptive® Medical Inc. BrightMatter® Planning Software, K140337), navigation (e.g., Synaptive BrightMatter Navigation System, K142024), optics, corridor resection (e.g., NICO Myriad), and biopsy." This indicates it can be used with other technologies, some of which may be software-based, but BrainPath itself is not an AI/ML device, and no MRMC study on its comparative effectiveness with AI assistance is mentioned.

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

• No. A standalone algorithm performance study was not done. The device is a physical surgical tool and does not operate as an algorithm.

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

• The "ground truth" for the non-clinical tests is based on established scientific and engineering principles, such as:
  • Biocompatibility standards: (e.g., ISO 10993 for Cytotoxicity, Sensitization, Irritation).
  • Sterilization validation methods: (e.g., for steam autoclaving, gas plasma, gamma irradiation).
  • Mechanical and functional specifications: (e.g., ability to interface with other instruments, packaging integrity).
  • These are not "expert consensus" or "pathology" in the clinical sense, but rather adherence to predefined and validated test methodologies and acceptance limits.

8. The sample size for the training set

• Not Applicable. There is no training set mentioned as this is not an AI/ML device.

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

• Not Applicable. As there is no training set, there is no ground truth establishment for it.

In summary: This document is a 510(k) submission for a physical surgical device, the NICO® BrainPath®, demonstrating substantial equivalence to a predicate device through non-clinical testing. It does not pertain to the development or validation of an AI/ML powered medical device, and thus, most of the requested information regarding AI/ML study design and criteria is not present.

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BrightMatter Navigation System is intended as a planning and intraoperative guidance system to enable open and percutaneous computer assisted surgery. The system is indicated for medical conditions requiring neurosurgical cranial procedures where the use of computer assisted planning and surgery may be appropriate. The system can be used for intra-operative guidance where a reference to a rigid anatomical structure can be identified.

The subject device, BrightMatter Navigation system, is a planning and image guided surgical system that enables computer assisted surgery where use of stereotactic image guidance may be considered appropriate. In particular, the device is suitable for neurosurgical cranial procedures. The planning functionality of the device is provided by an already cleared device, BrightMatter Planning (K140337). The remaining system provides a sequence of discrete workflow activities (or phases) that guide a surgeon through the process of data preparation for the surgical procedure. Then the device aids the surgeon in visualizing the location of the surgical tools relative to clinical images and physical location of the patient.

Following is a summary of steps involved in data preparation and registration of the patient's head position relative to pre-surgical clinical images:

• Importing plan and imaging data
• Reviewing and selecting a previously generated surgical plan
• Optionally fusing (merging or co-registering) additional imaging data
• Preparing and executing point-based registration

Following steps are provided as visualization tools during the execution of the surgical procedure:

• Aid in visualizing location of the surgical site as planned by the surgeon (using BrightMatter Planning software, K140337)
• For trajectory-centric procedures, help visualize insertion of tracked surgical tools by identifying location of surgical tool's position and orientation relative to clinical images and the surgical plan developed by the surgeon
• Visualize location of tracked surgical tools after the intended target location has been reached

Key functional components of the subject device are an optical tracking sub-system, navigated surgical tools, custom software application and an external display. The navigated surgical tools are tracked using single-use passive reflective markers (K033621) that are attached to the surgical tools prior to each surgical procedure.

The surgical display and tracking camera are mounted on an Auxiliary Cart. The computer is housed in a Navigation Cart.

As with many systems in the OR, not all components need to be sterile during use. The only subcomponents that come in contact with the patient are the Pointing Tool, Port Reference Tool and Calibration Block. These tools fit in the limited contact duration category.

Here's a breakdown of the acceptance criteria and study information for the BrightMatter Navigation System v1.0, based on the provided FDA 510(k) document:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not explicitly stated as a number of "samples" in a traditional clinical sense. The accuracy testing was conducted using a "measurement phantom" that mimicked brain volume.
• Data Provenance: The document does not specify the country of origin for the phantom data. The study was a non-clinical study, primarily involving engineering and system performance testing.

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

• Number of Experts: Not applicable. The ground truth for the accuracy testing was established using a Coordinate Measurement Machine (CMM), which is a precision metrology instrument, not human experts.
• Qualifications of Experts: Not applicable.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. The ground truth was established by CMM measurements, not human consensus requiring adjudication.

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

• MRMC Study: No, an MRMC comparative effectiveness study was not conducted.
• Effect Size: Not applicable. The device is a navigation system for surgical guidance, not an AI diagnostic tool primarily evaluated by human reader performance on a diagnostic task.

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

• Standalone Performance: Yes, the described accuracy testing (positional and angular error) is a form of standalone performance evaluation for the tracking and guidance algorithm. It assesses the device's inherent measurement capabilities without direct surgeon interaction in the measurement process (though it simulates surgical workflow via CT images of the phantom).

7. The Type of Ground Truth Used

• Type of Ground Truth: The ground truth for the accuracy testing was established by Coordinate Measurement Machine (CMM) measurements on a measurement phantom. This provides high-precision physical measurements. In addition, for biocompatibility testing, established laboratory standards and measurement techniques were used to determine results.

8. The Sample Size for the Training Set

• Sample Size for Training Set: The document does not provide information about a "training set" in the context of an AI/machine learning model. The BrightMatter Navigation System is described as using an "optical tracking sub-system" and software for guidance. It's not explicitly stated to be an AI/ML device in the sense of requiring a large training dataset for model development. The software development follows a standard Software Development Life Cycle.

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

• Ground Truth for Training Set: Not applicable, as there's no explicit mention of a training set or AI/ML model training in the conventional sense. The "training" of such a system would typically involve calibrating the optical tracking hardware and validating the software algorithms against known physical parameters, where the "ground truth" would be the precisely known physical dimensions and positions.

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