(193 days)
Brainlab trauma is intended to be a pre- and intraoperative image guided localization system to enable minimally invasive surgery. It links a freehand probe, tracked by a passive marker sensor system to virtual computer image space on a patient's pre- or intraoperative image data being processed by a VectorVision 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 bone structure like tubular bones, pelvic, calcaneus and talus, scapula, or vertebra, can be identified relative to a CT, fluoroscopic, X-ray or MR based model of the anatomy. In addition to the image guided navigation, Brainlab trauma also enables image-free navigation of trajectories for trauma procedures.
Example procedures include but are not limited to:
- Spinal procedures and spinal implant procedures such as pedicle screw placement.
- Pelvis and acetabular fracture treatment such as screw placement or iliosacral screw fixation.
- Fracture treatment procedures such as intramedullary nailing or plating or screwing, or external fixation procedures in the tubular bones.
- Retrograde drilling of osteochondral lesions.
Brainlab trauma is intended to enable operational navigation in spinal, traumatologic surgery. It links surgical instruments tracked by passive markers to a virtual computer image space.
In Brainlab trauma this virtual computer image space refers either to intraoperatively acquired and registered x-ray images of the individual patient's bone structure or to a landmark, which is intraoperatively defined by the surgeon using the tip of a tracked instrument.
Brainlab trauma allows surgical navigation considering patient movement in correlation to calibrated surgical instruments. This allows implant positioning, screw placement and bone fracture reduction in different views and reduces the need for treatments under permanent fluoroscopic radiation.
The provided text describes modifications to an existing image-guided surgery system (Brainlab trauma) and the verification and validation activities conducted to demonstrate its safety and effectiveness. However, it does not explicitly define acceptance criteria in terms of specific performance metrics with numerical thresholds for accuracy, sensitivity, or specificity. Instead, it states that "All tests have been successfully completed" and "All relevant hazards have been taken into consideration and the corresponding measures are effective," implying that the device met internal specifications without providing those specifications.
Therefore, many of the requested sections regarding acceptance criteria and performance metrics cannot be directly answered from the provided text.
Here's an attempt to answer based on the available information, with caveats where data is missing:
1. Table of Acceptance Criteria and Reported Device Performance
| Feature/Metric | Acceptance Criteria (Not explicitly stated with numerical thresholds in the provided text, but implied as "correct functionality" and "accuracy") | Reported Device Performance |
|---|---|---|
| Accuracy of image registration using xSpot | Implied: Must be accurate for surgical navigation. | Tested in a "non-clinical setup using both plastic bones (sawbone) and cadavers." Validated in cadaver sessions and clinical sites. All tests successfully completed; features proven safe and effective. (Specific accuracy values are not reported). |
| Accuracy of x-ray image free trajectory placement | Implied: Must be accurate for depth and placement. | Verified regarding "accuracy of depth and placement." All tests successfully completed. (Specific accuracy values are not reported). |
| Accuracy of implant calibration/navigation | Implied: Must be accurate for implant navigation. | Verified to "ensure the accurate implant navigation." Validated in cadaver sessions and clinical sites. All tests successfully completed; features proven safe and effective. (Specific accuracy values are not reported). |
| Workflow Functionality | Implied: Correct behavior of software and user interface. | Verified through "testing of the workflow," "detailed verification of the signed specifications covering the detailed functionality of the buttons," and "workflow based concept for the graphical user interface." Validated in sawbone environments, cadavers, and clinical sites. All tests successfully completed. |
| Safety and Effectiveness | Implied: Device must be safe and effective for its intended use. | "All tests have been successfully completed." "All relevant hazards have been taken into consideration and the corresponding measures are effective." "All system features could be proven to be safe and effective in a clinical environment." (This is a qualitative statement, not a quantitative metric). |
| Spherical drill limitation | Implied: Correctly enable warnings to prevent breaking out/into spherical anatomical regions. | Verified and validated as part of the overall system. Clinically validated as part of the "screw workflow in combination with the spherical drill limitation." All tests successfully completed. |
| Semi-automatic segmentation of bone shaft fragments | Implied: Correct segmentation functionality. | Clinically validated. All tests successfully completed. |
| Drill angle cone | Implied: Correct functionality. | Validated in sawbone environment and clinically. All tests successfully completed. |
While the document states that tests were successfully completed and the device was proven safe and effective, it does not provide numerical results or specific quantifiable acceptance criteria for these claims within the provided extract.
2. Sample size used for the test set and the data provenance
- Sample Size: Not explicitly stated. The document mentions "plastic bones (sawbone)" and "cadavers" for non-clinical testing, and "Three clinical sites" for clinical validation. The exact number of sawbones, cadavers, or patient cases at the clinical sites is not provided.
- Data Provenance:
- Non-clinical: Sawbone (plastic bone) and cadaver models. Origin not specified (e.g., country of origin for cadavers).
- Clinical: Data from "Three clinical sites." The country of origin for these clinical sites is not specified, but the manufacturer is based in Germany, and the FDA submission is for the USA, so sites could be in either or both.
- Retrospective/Prospective: The clinical validation appears to be prospective in nature, as it describes the "features clinically validated" in a clinical environment, implying active testing.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
This information is not provided in the given text. It is stated that "Three clinical sites have been validating Brainlab trauma as well as new and changed features regarding a user friendly and correct functionality," which implies expert users (surgeons, clinical staff) were involved, but their specific number or qualifications for establishing ground truth are not detailed.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not provided in the given text.
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
- MRMC Comparative Effectiveness Study: The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study involving human readers with and without AI assistance. The Brainlab trauma system is an image-guided navigation system, not an AI diagnostic aid for "human readers." Its purpose is to assist surgeons during procedures.
- Effect Size: Therefore, no effect size related to human reader improvement with AI assistance is mentioned.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
The context of Brainlab trauma is an "image guided localization system" that "links a freehand probe... to virtual computer image space" and "allows surgical navigation." It is inherently a system designed to be used with a human surgeon in the loop. The verification and validation activities include testing hardware (xSpot, instruments), software functionalities, and workflows in both non-clinical and clinical settings, all implying human interaction.
It's highly unlikely that a "standalone" or "algorithm-only" performance would be assessed for such a device, as its utility is defined by its interaction with a surgeon during a procedure. The closest analogue would be the accuracy measurements (e.g., image registration, trajectory placement, implant calibration) performed on sawbones and cadavers, which represent the algorithmic performance in a controlled environment before clinical human interaction, but these are components of the human-in-the-loop system.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
The type of ground truth used is implied through the nature of the tests:
- Non-clinical (sawbones, cadavers): Ground truth for accuracy tests (e.g., image registration, trajectory placement, implant accuracy) would likely involve precise physical measurements (e.g., using a coordinate measuring machine or similar high-precision instruments) on the models or anatomical structures to compare against the system's generated coordinates or paths.
- Clinical sites: For clinical validation, the ground truth for "user friendly and correct functionality" could be a combination of:
- Surgeon assessment and feedback: Through direct observation and qualitative reporting.
- Intraoperative imaging: Comparing the navigated position/trajectory with subsequent intraoperative fluoroscopy or other imaging to confirm accuracy.
- Clinical outcomes (short-term): While not explicitly stated, successful completion of procedures, correct implant placement, and lack of complications in the short term would contribute to "proven to be safe and effective."
8. The sample size for the training set
The document does not mention a "training set" or "training data" in the context of machine learning. The Brainlab trauma system described predates widespread deep learning applications in medical devices (2011). It's an image-guided surgery system relying on image processing, registration algorithms, and a database of implants, rather than a machine learning model that requires a discrete "training set" in the modern sense.
9. How the ground truth for the training set was established
Since no "training set" for a machine learning model is mentioned, this question is not applicable based on the provided text. The "ground truth" for the development of its algorithms (e.g., for registration, trajectory planning, spherical drill limitation) would have been established through engineering principles, mathematical modeling, and rigorous bench testing against known physical standards.
§ 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).