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Disposable Pre-calibrated Suction is an accessory of the Cranial Image Guided Surgery System and intended to be used as a navigated suction device in any surgical procedure in which the use of the Cranial Image Guided Surgery System may be indicated.

Surgical example procedures include:

• · Cranial resection of tumors and other lesions
• · Resection of skull base tumors or other lesions
• · AVM Resection

Disposable Pre-calibrated Suction is an accessory of the Cranial IGS system and intended to be used as a navigated suction device in any surgical procedure in which the use of the Cranial IGS system may be indicated.

The Brainlab Disposable Suction is an accessory for the currently released and developed Brainlab optical IGS systems for cranial procedures. The device will be pre-calibrated, i.e. it will be automatically recognized by the system and is immediately ready to use. By tracking the flat markers attached to the integrated tracking array, the instrument and thereby position of the tip can be located by the Brainlab optical IGS systems for cranial procedures.

The device is intended for single short term invasive use on an individual patient during a single procedure. This invasive device is used for a short-term limited contact (

The provided text describes a 510(k) submission for a medical device called "Disposable Pre-calibrated Suction." It is an accessory to a Cranial Image Guided Surgery System. The document focuses on demonstrating substantial equivalence to predicate devices rather than proving a specific AI algorithm's performance against acceptance criteria for a diagnostic task.

Therefore, the information required to populate the fields related to AI model acceptance criteria, test set details (sample size, provenance, expert consensus, adjudication, MRMC studies), training set details, and ground truth establishment is not present in the provided text. The document primarily details mechanical, accuracy, shelf-life, biocompatibility, and sterility testing for a physical medical instrument.

However, I can provide the acceptance criteria and reported performance for the instrument tracking accuracy, which is a key technical performance aspect mentioned in the document.

Here's a table based on the provided "Performance Data" section:

Acceptance Criteria and Reported Device Performance (Instrument Tracking Accuracy)

Study Proving Device Meets Acceptance Criteria:

The study described is primarily a technical performance verification aimed at demonstrating substantial equivalence for a physical medical instrument, not an AI algorithm.

1. Sample sized used for the test set and the data provenance:

   • The document does not specify the sample size (number of measurements or trials) used for the instrument tracking accuracy testing.
   • Data Provenance: Not explicitly stated, but implied to be from internal testing by Brainlab AG given the context of a 510(k) summary. No country of origin for the data is mentioned, nor whether it was retrospective or prospective.

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

   • This is not applicable as the "ground truth" here refers to the actual physical location and orientation of the instrument during accuracy testing, which would be established by high-precision measurement systems, not human experts.

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

   • Not applicable for instrument performance testing.

4. 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:

   • Not applicable. This device is a navigated suction instrument, not an AI-powered diagnostic tool requiring human reader studies.

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

   • The "standalone" performance here refers to the instrument's accuracy as measured by the tracking system, independent of a human's surgical skill. The table above presents this standalone performance.

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

   • For instrument tracking accuracy, the ground truth would be established by a highly accurate (e.g., optical or mechanical) reference measurement system, often referred to as a "gold standard" measurement setup. It is not expert consensus, pathology, or outcomes data.

7. The sample size for the training set:

   • Not applicable. This is not an AI/machine learning device that requires a training set. The "pre-calibrated" aspect refers to factory calibration, not AI model training.

8. How the ground truth for the training set was established:

   • Not applicable as there is no training set for an AI model.

In summary, the provided document focuses on the engineering and biocompatibility validation of a physical medical device accessory rather than the clinical or diagnostic performance of an AI algorithm. Therefore, many of the requested details related to AI model evaluation are not present.

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