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The Cranial IGS System, when used with a compatible navigation platform and compatible instrument accessories, is intended as an image-guided planning and navigation system to enable navigated surgery. It links instruments to a virtual computer image space on patient image data that is 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
• · Cranial biopsies
• Intracranial catheter placement
• · Intranasal structures and Paranasal Sinus Surgery
  • · Functional endoscopic sinus surgery (FESS)
  • · Revision & distorted anatomy surgery all intranasal structures and paranasal sinuses

The Cranial IGS System consists of software and hardware (instruments) components that when used with a compatible navigation or "IGS platform" enables navigated surgery. It links instruments in the real world or "patient scan data or "image space". This allows for the continuous localization of medical instruments and patient anatomy for medical interventions in cranial and ENT procedures.

The provided text describes the Cranial Image Guided Surgery System, which is a medical device. The information details the device's intended use, technological characteristics compared to predicate devices, and a summary of verification and validation activities.

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

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the "Cranial Image Guided Surgery System" are not explicitly stated as distinct acceptance criteria values in the document. Instead, the document presents performance verification results (accuracy), which are implicitly the performance targets for the device. The "mean accuracy" values mentioned are the internal acceptance criteria the device was required to meet.

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

The document mentions "Nonclinical performance testing (Accuracy)" and "The following table summarizes the performance verification results of the system." However, it does not specify the sample size (e.g., number of test cases, number of images, or number of simulated scenarios) used for these accuracy tests.

The data provenance is also not explicitly stated in terms of country of origin or whether the data was retrospective or prospective. Given that it's "nonclinical performance testing," it is likely that the testing involved phantom studies or simulated scenarios rather than real patient data.

3. Number of Experts Used to Establish Ground Truth and Their Qualifications

The document does not specify the number of experts used to establish ground truth for the test set or their qualifications. The accuracy testing seems to be based on physical measurements against established ground truth (e.g., from a phantom or known geometry), rather than expert consensus on image interpretation.

4. Adjudication Method for the Test Set

The document does not mention any adjudication method (e.g., 2+1, 3+1, none) for the test set. Given that the performance testing is focused on mechanical/measurement accuracy (location and trajectory angle errors), an adjudication method requiring human interpretation would not be applicable in the same way as it would be for a diagnostic AI system.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done

The document does not indicate that an MRMC comparative effectiveness study was done. The assessment presented is focused on the device's accuracy in navigation, not on a comparison of human reader performance with and without AI assistance. This device is an image-guided surgery system, which assists surgeons during procedures, rather than an AI diagnostic tool primarily interpreted by human readers.

6. If a Standalone (i.e., Algorithm Only Without Human-in-the-Loop Performance) Was Done

The "Nonclinical performance testing (Accuracy)" described can be considered a form of standalone performance evaluation for the algorithm's core functionality (localization and trajectory determination). The results presented (location error, trajectory angle error) are metrics of the system's inherent accuracy, without explicitly involving real-time human interaction for performance measurement in these specific tests. However, the device is ultimately intended for human-in-the-loop use in surgery.

7. The Type of Ground Truth Used

The ground truth used for the "Nonclinical performance testing (Accuracy)" appears to be physical measurement against a known standard or ideal. For instance, in a phantom study, the "true" location and trajectory would be precisely known or measurable, allowing for the calculation of errors from the device's output. The text does not specify if it was expert consensus, pathology, or outcomes data.

8. The Sample Size for the Training Set

The document does not provide any information regarding the sample size used for the training set. It primarily discusses the device's verification and validation, but not the development or training of any underlying algorithms (if applicable, beyond traditional image processing and navigation).

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

Since no information on a "training set" is provided, there is no mention of how ground truth for a training set was established. The device's functionality appears to be based on established navigation principles and software engineering, rather than a machine learning model that requires a labeled training dataset with associated ground truth for learning.

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The BrainLAB Cranial IGS System is intended to be an intra-operative image guided localization system to enable minimally invasive surgery. It links a freehand probe, tracked by a magnetic sensor system or a passive marker sensor system to a virtual computer image space on patient image data being processed by the IGS workstation. The system is indicated for any medical condition in which the use of stereotactic surgery may be appropriate and where a reference to a rigid anatomical structure, such as the skull, a long bone, or vertebra, can be identified relative to a CT, CTA, X-Ray, MR, MRA and ultrasound based model of the anatomy.

Example procedures include but are not limited to:

Cranial Procedures: Turnor resections Skull base surgery Cranial biopsies Craniotomies/ Craniectomies Pediatric Catheter Shunt Placement General Catheter Shunt Placement Thalamotomies/ Palliodotomies

ENT Procedures: Transphenoidal procedures Maximillary antrostomies Ethmoidectomies Spheno-idotomies/ sphenoid explorations Turbinate resections Frontal sinusotomies Intranasal procedures

The Cranial IGS System consists of the IGS workstation, the touch screen monitor and the 3D tracking system. A set of hardware accessories provides for comfortable and accurate use of the system.

The IGS workstation holds the patient data during the surgery and runs the cranial software application.

The patient data needed for the image-guided surgery is acquired pre-operatively or intraoperatively and is transferred to the IGS workstation via network, data carrier or data bus, The cranial software application offers the display of the patient data in various reconstructions, segmentations and overlays on the touch screen in addition to position information of tracked instruments - optionally combined with outlined information. The touch screen enables the control of the cranial software application and can be draped for sterile use by the surgeon.

The electro-magnetic or optical 3D tracking system performs the localization of patient and surgical tools within the operating field.

The virtual diagnostic image spaces are correlated ("registered") to the surgical environment by collecting the 3D position of anatomical landmarks or fiducial markers with a tracked pointer probe and relating them with the corresponding features extracted from the diagnostic image data sets. Alternatively, the patient's skin surface can be scanned with a laser device or touched with a pointer device and matched to the 3D reconstruction of the patient data set. If several diagnostic image spaces have been acquired from the same patient, only one of them has to be registered whereas the remaining ones can be fused to the registered data set.

Intra-operatively acquired patient data can furthermore be correlated ("registered") to the surgical environment by determining its spatial position to the patient during its acquisition.

Structures in the patient's body are localized using trackable pre-calibrated or intraoperatively calibrated surgical instruments. Examples of surgical instruments are the pointer tool, biopsy needles, catheter stylets or suction tubes.

Surgical microscopes, ultrasound devices and endoscopes are additional intra-operative image sources, which are connected with the Cranial IGS System via signal transmission cables. They can be calibrated and tracked similar as any other surgical instrument. Their images can be displayed on the touch screen or external monitors and combined with the available patient data in correct spatial relation. The settings of microscope and ultrasound devices offering a communication interface can be controlled from the Cranial IGS System. Navigation information can be displayed in the microscope's image injection module.

Defined components of the Cranial IGS System are prepared for the use in magnetresonance environments.

The Cranial IGS System contains hardware accessories and software features to improve the support and guidance of surgical instruments.

The Cranial IGS System contains a network based software interface that allows downloading medical data (such as image sets, objects, trajectories or points) and, tracking data from the system as well as to upload and display an image stream to the system. This interface can be used to implement custom visualization of medical data (e.g. included modalities which are otherwise unknown to the cranial software application) as well as to control other devices. These view data is strictly under the responsibility of the user and clearly marked as such.

Here's a breakdown of the acceptance criteria and study information based on the provided text:

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

The provided text does not explicitly state specific quantitative acceptance criteria or detailed reported device performance in the format of a table with numerical values. The document instead states:

"The Cranial IGS System has been verified and validated according to BrainLAB's procedures for product design and development. The validation proves the safety and effectiveness of the system."

This indicates that internal validation was performed against predefined criteria, but the specific metrics and their performance targets are not publicly detailed in this 510(k) summary. Given the nature of image-guided surgery systems, performance criteria would typically relate to:

• Accuracy of registration: How closely the virtual image space aligns with the physical surgical environment. This is often measured in terms of target registration error (TRE) or fiducial registration error (FRE).
• Tracking accuracy: The precision with which surgical instruments are localized in real-time.
• System latency: The delay between actual instrument movement and its display on the screen.
• Reproducibility: Consistency of results across multiple uses.

Without the specific validation report, detailed quantitative acceptance criteria and reported performance cannot be extracted from this summary.

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

The provided document does not specify the sample size used for any test set or the data provenance. It only mentions "patient data" as being acquired pre-operatively or intra-operatively, but not for the purpose of a formal test set for regulatory submission. The validation is described as being performed "according to BrainLAB's procedures for product design and development," implying internal testing rather than a public clinical trial with a defined test set.

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)

The document does not provide any information regarding the number or qualifications of experts used to establish ground truth for a test set.

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

The document does not mention any adjudication method for a test set.

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 describe an MRMC comparative effectiveness study or any study comparing human readers with and without AI assistance. The device is an image-guided surgery system, not an AI diagnostic aid for image interpretation.

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

The document describes an "intra-operative image guided localization system" which inherently involves human-in-the-loop performance for surgical guidance. While the system has standalone functions (e.g., image processing, tracking), its intended use and validation would be in the context of assisting a surgeon. The summary does not explicitly detail a standalone (algorithm-only) performance study.

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 validation or any testing. For an image-guided surgery system, ground truth for accuracy would typically involve precise physical measurements using phantoms or cadaveric models against known fiducial points, or potentially intraoperative verification methods in a clinical setting (though this is not detailed).

8. The sample size for the training set

The document does not specify any sample size for a training set. The device likely relies on established principles of image processing, computer vision, and tracking, rather than deep learning models requiring large discrete training sets as typically understood in AI/ML contexts.

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

Since no training set is explicitly mentioned or implied for a machine learning model, the document does not provide information on how ground truth for a training set was established.

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