Search Results

Automatic Registration is a software device for image guided surgery intended to be used in combination with compatible Brainlab navigation systems such as the Brainlab Spine & Trauma Navigation System. Automatic Registration provides an image registration for intraoperatively acquired 3D CT/CBCT or fluoroscopic images.

The Subject Device Automatic Registration is an accessory to the Brainlab Spine & Trauma Navigation System. It correlates intraoperatively acquired patient data (3D CT/CBCT or fluoroscopic images) to the surgical environment in order to provide a patient registration for subsequent use by the Brainlab Spine & Trauma Navigation. The device includes the following software modules:

• Automatic Registration 2.6
• Universal Atlas Performer 6.0
• Universal Atlas Transfer Performer 6.0 .
  And uses as well several hardware devices, mainly registration matrices, adhesive flat markers and a calibration phantom, for performing the registration. The software is installed on an Image Guided Surgery (IGS) platform. The registration matrices are reusable devices, delivered nonsterile and having patient contact.

Here's a breakdown of the acceptance criteria and the study details for the Automatic Registration device, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Accuracy Testing (New Registration Method): The testing was performed on human cadavers. The exact number of cadavers or cases within them is not specified.
• AI/ML Assessment: The summary states the algorithm was developed using a "controlled internal process that defines activities from the inspection of input data to the training and verification of the algorithm." No specific sample size for the test set is provided, nor is the data provenance (e.g., country of origin, retrospective/prospective) explicitly mentioned for the AI/ML assessment tests.
• Usability Testing: 15 representative users were used for summative usability testing.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• For the accuracy testing on human cadavers, the method for establishing ground truth and the number/qualifications of experts are not explicitly stated. It can be inferred that ground truth for navigation accuracy would involve precise measurements by trained personnel, likely using specialized equipment, but no details are provided.
• For the AI/ML algorithm assessment, the criteria for "quantifying object detection, quality of vertebra level assignment, quality of landmark predictions, and the performance of the observer view direction" would imply expert review. However, the number of experts and their qualifications for establishing this ground truth are not specified.

4. Adjudication Method for the Test Set

The document does not specify any adjudication methods (e.g., 2+1, 3+1) for the test sets described.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done or at least not reported in this summary. The summary focuses on the standalone performance of the device and its components, particularly the new AI/ML registration update method, against predefined accuracy criteria and usability.

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

Yes, a standalone performance evaluation was done for the new registration method, specifically the AI/ML algorithm component. The "Machine Learning" section describes the assessment of the AI/ML detected landmarks on X-rays, evaluating aspects like object detection, vertebra level assignment, and landmark predictions. The accuracy bench testing also evaluates the "overall system registration accuracy" of the new method, implying its standalone performance in achieving the specified accuracy.

7. The Type of Ground Truth Used

• For accuracy testing (bench test on cadavers): The ground truth would likely be established through highly precise physical measurements on the cadavers, probably using a gold-standard measurement system (e.g., CMM, anatomical landmarks verified with high-precision tools). The document doesn't explicitly state the methodology, but this is typical for navigation accuracy.
• For AI/ML assessment: The ground truth for "object detection, quality of vertebra level assignment, quality of landmark predictions, and the performance of the observer view direction for 2D X-rays" would have been established by human experts, likely radiologists or orthopedic surgeons with expertise in spinal anatomy and imaging. The document doesn't explicitly detail the methodology or the experts involved.

8. The Sample Size for the Training Set

The sample size for the training set of the Convolutional Neural Network (CNN) is not specified in the provided document. It only mentions that the algorithm was "developed using a controlled internal process that defines activities from the inspection of input data to the training and verification of the algorithm."

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

The document states that the AI/ML algorithm was developed using a "Supervised Learning approach." This implies that the training data was labeled by human experts. However, the specific method (e.g., single expert, consensus, specific qualifications of experts) for establishing this ground truth for the training set is not detailed in the provided text.

Ask a specific question about this device

Automatic Registration is a surgical device for image guided surgery intended to be used in combination with compatible Brainlab navigation systems. Automatic Registration provides an image registration for intraoperatively acquired 3D CT/ CBCT or fluoroscopic images. It consists of the software module Automatic Registration and hardware accessories.

The Subject Device is intended to be used in combination with compatible Brainlab navigation systems. Automatic Registration provides an image registration for intraoperatively acquired 3D CT/CBCT or fluoroscopic images. It consists of the software module Automatic Registration and hardware accessories.

In a spinal context, Automatic Registration serves as accessory to the Spine & Trauma navigation system.

The Matrices are reusable devices delivered in non-sterile condition. The devices makes the Correlation ("Registration") of Intraoperative acquired patient data to the surgical environment possible by determining its position in relation to the patient and the navigated instruments.

The document describes K203679, a premarket notification for the "Automatic Registration" device by Brainlab AG. The device is a surgical device for image-guided surgery intended to be used in combination with compatible Brainlab navigation systems, providing image registration for intraoperatively acquired 3D CT/CBCT or fluoroscopic images.

Here's an analysis of the acceptance criteria and study data provided:

Acceptance Criteria and Device Performance

Note: The study for the subject device (Automatic Registration) used stricter acceptance criteria for its own evaluation (mean navigation accuracy: <= 1.5mm, 95th percentile of single navigation accuracy: <= 2.5mm) and successfully met them, demonstrating better performance than the predicate device. The table above compares its performance against the predicate's acceptance criteria to show superiority.

Study Details

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

   • Test set sample size:
     • Total number of navigation points: n = 228
     • Total number of scans used: m = 50
   • Data provenance: "simulated test environment"

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

   • Not specified. The document indicates a "simulated test environment" and accuracy measurements, but does not mention human experts establishing ground truth for the test.

3. Adjudication method for the test set:

   • Not applicable as the ground truth was established by direct measurement in a simulated environment, not by human review and adjudication.

4. Multi Reader Multi Case (MRMC) comparative effectiveness study:

   • No MRMC comparative effectiveness study was done. The study focuses purely on the device's accuracy in a standalone/simulated setting, not on its impact on human reader performance.

5. Standalone performance:

   • Yes, a standalone (algorithm only without human-in-the-loop performance) study was performed. The data presented reflects the "Automatic Registration" device's accuracy in a simulated environment.

6. Type of ground truth used:

   • The ground truth was established through direct measurement of navigation accuracy in a "simulated test environment." This implies a precisely controlled and measurable setup where the true position is known.

7. Sample size for the training set:

   • Not specified. The document only mentions the test set used for performance evaluation.

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

   • Not specified, as the training set details are not provided.

Ask a specific question about this device