(291 days)
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.
§ 882.4560 Stereotaxic instrument.
(a)
Identification. A stereotaxic instrument is a device consisting of a rigid frame with a calibrated guide mechanism for precisely positioning probes or other devices within a patient's brain, spinal cord, or other part of the nervous system.(b)
Classification. Class II (performance standards).