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Cranial Navigation: The Cranial Navigation is intended as image-guided planning and navigation system to enable navigated cranial surgery. It links instruments to a virtual computer image data being processed by the navigation platform. The system is indicated for any medical condition in which a reference to a rigid anatomical structure can be identified relative to images (CT. CTA. X-Ray, MR, MRA and ultrasound) of the anatomy, including: Cranial Resection (Resection of tumors and other lesions, Resection of skull-base tumor or other lesions, AVM Resection), Craniofacial Procedures (including cranial and midfacial bones) (Tumor Resection, Bone Tumor Defect Reconstruction, Bone Trauma Defect Reconstruction, Bone Congenital Defect Reconstructions, Orbital cavity reconstruction procedures), Removal of foreign objects.

Cranial EM System: Cranial EM is intended as an image-guided planning and navigation system to enable neurosurgery procedures. The device is indicated for any medical condition in which a reference to a rigid anatomical structure can be identified relative to images (CT, CTA, X-Ray, MR, MRA and ultrasound) of the anatomy, such as: Cranial Resection (Resection of tumors and other lesions, Resection of skull-base tumor or other lesions), Intracranial catheter placement.

The subject device consists of several devices: Cranial Navigation using optical tracking technology, its accessory Automatic Registration iMRI, and Cranial EM System using electromaqnetic tracking technology.

Cranial Navigation is an image guided surgery system for navigated treatments in the field of cranial surgery, including the newly added Craniofacial indication. It offers different patient image registration methods and instrument calibration to allow surgical navigation by using optical tracking technology. The device provides different workflows guiding the user through preoperative and intraoperative steps. The software is installed on a mobile or fixed Image Guided Surgery (IGS) platform to support the surgeon in clinical procedures by displaying tracked instruments in patient's image data. The IGS platforms consist of a mobile Monitor Cart or a fixed ceiling mounted display and an infrared camera for image guided surgery purposes. There are three different product lines of the IGS platforms: "Curve", "Kick" and Buzz Navigation (Ceiling-Mounted). Cranial Navigation consists of: Several software modules for registration, instrument handling, navigation and infrastructure tasks (main software: Cranial Navigation 4.1 including several components), IGS platforms (Curve Navigation 17700, Kick 2 Navigation Station, Buzz Navigation (Ceiling-Mounted) and predecessor models), Surgical instruments for navigation, patient referencing and registration.

Automatic Registration iMRI is an accessory to Cranial Navigation enabling automatic image registration for intraoperatively acquired MR imaging, The registration object can be used in subsequent applications (e.g. Cranial Navigation 4.1). It consists of the software Automatic Registration iMRI 1.0, a registration matrix and a reference adapter.

Similarly, the Cranial EM System, is an image-guided planning and navigation system to enable neurosurgical procedures. It offers instrument handling as well as patient registration to allow surqical navigation by using electromagnetic tracking technology. It links patient anatomy (using a patient reference) and instruments in the real world or "patient space" to patient scan data or "imaqe space". This allows for the continuous localization of medical instruments and patient anatomy for medical interventions in cranial procedures. It uses the same software infrastructure components as the Cranial Navigation, and the software is also installed on IGS platforms consisting of a mobile monitor cart and an EM tracking unit. It consists of: Different software modules for instrument set up, registration and navigation (Main software: Cranial EM 1.1 including several components), EM IGS platforms (Curve Navigation 17700 and Kick 2 Navigation Station EM), Surgical instruments for navigation, patient referencing and registration.

The provided text describes a 510(k) premarket notification for Brainlab AG's Cranial Navigation, Navigation Software Cranial, Navigation Software Craniofacial, Cranial EM System, and Automatic Registration iMRI. The document focuses on demonstrating substantial equivalence to predicate devices, particularly highlighting the introduction of Artificial Intelligence/Machine Learning (AI/ML) algorithms for specific features.

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text:

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

The document mentions acceptance criteria relating to system accuracy.

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

• Test Set Sample Size: The document does not explicitly state the numerical sample size for the test set used for evaluating the AI/ML algorithm or other performance metrics. It only mentions the "test pool data is set aside at the beginning of the project."
• Data Provenance: The document does not specify the country of origin of the data or whether it was retrospective or prospective. It just refers to "training pool data" and "test pool data."

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

The document does not provide information on the number or qualifications of experts used to establish ground truth for the test set. It mentions the AI/ML algorithm was developed using a Supervised Learning approach and that its performance was evaluated against a test pool, but no details on human ground truth labeling are given.

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

The document does not describe any adjudication methods used for the test set ground truth.

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

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study. The AI/ML functionality described is for "pre-registration" and "centering of views," which are aids to the navigation system, but there is no indication of a study measuring human reader performance with and without AI assistance. The performance testing for AI/ML focuses on its own accuracy (precision and recall) compared to a previous atlas-based method, not human reader improvement.

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

Yes, a standalone (algorithm only) performance evaluation was done for the AI/ML features. The text states: "For the two features now implemented using AI/ML (landmark detection in the pre-registration step and centering of views if no instrument is tracked to the detected abnormity), performance testing comparing conventional to machine learning based landmark detection and abnormity detection were performed showing equivalent performance as in the predicate device." It also highlights that for abnormity detection, "both precision and recall of the ML-based method are higher in comparison to the atlas-based method." This indicates an isolated evaluation of the algorithm's performance.

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

The document does not explicitly state the type of ground truth used for the AI/ML algorithm. It mentions "Supervised Learning" and "training pool data," implying that the training data had pre-established labels (ground truth), but it doesn't specify if these labels came from expert consensus, pathology, or another source. Given the context of image-guided surgery, it's highly probable the ground truth for abnormalities and landmarks would be derived from expert annotations on medical images.

8. The sample size for the training set

The document does not explicitly state the numerical sample size for the training set. It only refers to a "training pool data."

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

The document mentions that the AI/ML algorithm was developed using a "Supervised Learning approach." This means that the training data was pre-labeled. However, it does not specify how this ground truth was established (e.g., by manual annotation from a certain number of experts, based on surgical findings, etc.). It only states that the "training process begins with the model observing, learning, and optimizing its parameters based on the training pool data."

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Cranial EM is intended as an image-guided planning and navigation system to enable neurosurgery procedures. The device is indicated for any medical condition in which a reference to a rigid anatomical structure can be identified relative to images (CT, CTA, X-Ray, MR, MRA, and ultrasound) of the anatomy, such as:

• Cranial Resection
  • Resection of tumors and other lesions
  • Resection of skull-base tumor or other lesions
• Intracranial catheter placement

The Subject Device, Cranial EM System, consists of software and hardware components. It links patient anatomy (using a patient reference) and instruments in the real world or "patient space" to patient scan data or "image space". This allows for the continuous localization of medical instruments and patient anatomy for medical interventions in cranial procedures. The tracking data are acquired via electromagnetic tracking. Cranial EM is a touchscreen-based intraoperative navigation software. The placement of surgical instruments in a three-dimensional representation overlaid on anatomical image sets, such as MR and/or CT, can support the surgeon during various surgical interventions. Cranial EM uses scanned images of the patient that are acquired before surgery is performed.

The following software make up the main module of the device:

• 1. EM Setup 2.1
• 2. Head Registration 2.1
• 3. EM Instruments 2.1
• 4. Navigation 2.1

The Subject Device Consists of the following hardware components:
Platforms:

• 1. Kick 2 Navigation Station (Article Number: 18202)
• 2. Curve Navigation 17700 (Article Number: 17700)

Instruments:

• 1. EM Patient Reference 2.0 (18099-24)
• 2. EM Pointer (18099-02C)
• 3. EM Instrument Reference (18099-05A)
• 4. EM Registration Pointer (18099-23)
• 5. EM Stylet 2.0 (18097-01)
• 6. EM Short Pointer (18099-27)
• 7. EM Skull Reference Base (18099-06)

1. Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance for Test Set:
The provided document does not specify a distinct sample size for a test set in terms of patient data or case studies. Instead, it mentions "System accuracy tests (Software + platforms + instruments)" were conducted. The data provenance is not explicitly stated as country of origin, nor whether it was retrospective or prospective patient data. The accuracy tests described appear to be laboratory performance evaluations of the device's hardware and software components rather than clinical trials with patient data.

3. Number and Qualifications of Experts for Ground Truth:
Not applicable. The ground truth for the core accuracy metrics (mean location error and mean trajectory angle error) was established through technical measurements of the system's performance, not through expert consensus on clinical cases.

4. Adjudication Method for Test Set:
Not applicable. Since the ground truth was established through technical measurements (System accuracy tests), an adjudication method for a test set involving human interpretation is not relevant here.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
The document states, "No clinical testing submitted for the Subject Device. Data from existing literature was leveraged for validating the Subject Device's indications." Therefore, no MRMC comparative effectiveness study was conducted, and no effect size for human readers' improvement with AI assistance is provided for this submission.

6. Standalone Performance:
Yes, a standalone performance evaluation was done. The "System accuracy tests (Software + platforms + instruments)" directly assess the algorithm's performance (in conjunction with its hardware) without a human-in-the-loop. The reported performance metrics (mean location error ≤ 2 mm, mean trajectory angle error ≤ 2 degrees) are results of this standalone testing.

7. Type of Ground Truth Used:
The type of ground truth used for the device's core accuracy metrics (mean location error and mean trajectory angle error) was technical measurement and engineering specifications. This is established by rigorous performance testing of the device hardware and software against known physical standards.

8. Sample Size for the Training Set:
The document does not provide information regarding a specific "training set" for an AI algorithm in the context of device accuracy. The "System accuracy tests" were likely performance validation tests rather than training data for an adaptive algorithm.

9. How the Ground Truth for the Training Set was Established:
Not applicable. Information regarding a training set and its ground truth establishment is not provided in the document. The device's validation appears to rely on established engineering principles and performance testing against predefined accuracy standards, rather than machine learning models requiring extensive training data.

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