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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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