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Brainlab Knee is intended to be an 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 an individual 3D-model of the patient's bone, which is generated through acquiring multiple landmarks on the bone surface. 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, x-ray, MR-based model of the anatomy. The system aids the surgeon to accurately navigate a knee prosthesis to the intraoperatively planned position. Ligament balancing and measurements of bone alignment are provided by Brainlab Knee.

Example orthopedic surgical procedures include but are not limited to:

• · Total Knee Replacement
• · Ligament Balancing
• · Range of Motion Analysis
• · Patella Tracking

Brainlab knee is an image guided surgery system for total knee replacement surgery . based on landmark based visualization of the femur and tibia.

The provided 510(k) summary for Brainlab Knee (K102990) describes the device, its intended use, and substantial equivalence to predicate devices, but it does not contain the detailed information necessary to complete the requested table and answer all questions regarding acceptance criteria and the specific study proving the device meets those criteria.

The submission focuses on verification and validation activities at a high level. It generally discusses that the system was verified and validated according to BrainLAB procedures, and that functionality was verified on released platforms with workbench tests on milled model bones. It also mentions validation methods such as comparison to previous products, literature research, real-world testing, usability tests, design reviews, and software validation. Furthermore, it states that validation activities were supported by design reviews with initial design surgeons and a cadaver test.

However, the provided text lacks specific, quantifiable acceptance criteria for performance metrics. It does not report precise device performance values against such criteria. The document describes general validation and verification but does not detail a specific study with defined acceptance criteria and reported outcomes to prove those criteria were met for this particular submission.

Here's a breakdown of what can and cannot be answered from the provided text:

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

• Cannot be provided based on the input. The document does not specify any quantifiable acceptance criteria (e.g., accuracy thresholds, precision ranges) for the Brainlab Knee system. Consequently, it does not report specific device performance values against such criteria. It generally states that "Cut and implant positions have been compared to theoretical values" during workbench tests, but no actual values or criteria are provided.

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

• Cannot be fully provided. The document mentions "workbench test have been performed on precisely milled model bones" and "a cadaver test."
  • Sample size for test set: Not specified for either the model bones or the cadaver test.
  • Data provenance: Not specified (e.g., country of origin, retrospective/prospective).

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)

• Cannot be provided. The document mentions "design reviews with many of the initial design surgeons" which "supported" the validation. However, it does not specify the number or qualifications of experts involved in establishing ground truth for any specific test set, nor does it explicitly state their role in ground truth determination.

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

• Cannot be provided. The document does not describe any adjudication method for establishing ground truth or resolving discrepancies in test results.

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

• No, an MRMC study was not done, or at least not described in this submission. The Brainlab Knee system is an image-guided surgery system, not an AI-assisted diagnostic tool that would typically involve "human readers" in an MRMC study context. The document focuses on the system's ability to aid surgeons in navigating prostheses and performing measurements, not on its impact on diagnostic reader performance. Therefore, an effect size of human readers improving with/without AI assistance is not applicable to the information provided.

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

• Yes, implicitly. The "workbench test" performed on precisely milled model bones, where "Cut and implant positions have been compared to theoretical values," represents a form of standalone testing. This indicates that the algorithm's output (planned positions) was evaluated against a known ground truth (theoretical values) without immediate human surgical intervention as part of the evaluation of the algorithm's core functionality.

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

• "Theoretical values" and potentially "established history of successful use" and "design surgeons' input."
  • For workbench tests on model bones, the ground truth was "theoretical values" for cut and implant positions.
  • Validation also included "Comparison of the design to a previous product having an established history of successful use," which implies the ground truth for some aspects might be derived from the proven performance of predicate devices.
  • "Design reviews with many of the initial design surgeons" also contributed to validation, suggesting expert input played a role, though not explicitly as "ground truth" for a specific test set.

8. The sample size for the training set

• Not applicable / Not provided. The Brainlab Knee is an image-guided surgery system that uses landmarks collected intraoperatively to create a 3D model. It's not an AI/machine learning system in the modern sense that typically has a "training set" of data to learn from in the same way. Its functionality is based on geometric algorithms and image processing, not a trained predictive model that would require a large training dataset to learn patterns.

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

• Not applicable / Not provided. As explained above, the concept of a "training set" with established ground truth is not directly applicable to this type of device based on the information given.

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BrainLAB ACL is intended to be used as an intraoperative image-guided navigation system to enable minimally invasive surgery. It links a freehand probe, tracked by a passive marker sensor system, to a virtual computer image space on the model of a bone, overlaid with individually acquired patient landmarks.

The system is indicated for any surgical anterior cruciate ligament procedure in which the use of stereotactic surgery for the planning and navigation of interosseous canals may be appropriate, and where a reference to a rigid anatomical structure can be established.

ACL is an image quided surgery system for the replacement of torn ligaments in the knee joint. It is based on intra-operatively acquired landmarks that are used for planning and navigation. It supports the surgeon in the planning and drilling of transplants canals in the position to regain the stability of the knee joint.

This 510(k) summary for the BrainLAB ACL device does not contain the detailed information necessary to fully answer all aspects of your request regarding acceptance criteria and a specific study proving device performance against those criteria. The provided document is a regulatory submission for substantial equivalence rather than a detailed scientific study report.

Here's a breakdown of what can be extracted and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

This information is not provided in the document. The 510(k) summary focuses on verifying and validating the device in general terms and establishing substantial equivalence to predicate devices, rather than presenting specific quantitative performance criteria and results from a singular study.

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

This information is not provided. The document mentions "Subsystem & System Verification using Xtool," "Non-Xtool tests," "License group tests," "Installer tests," and "Startup tests," as well as "Testing and evaluation under real world conditions" and "Preclinical and clinical validation." However, no specific sample sizes for test data, or details on whether this data was retrospective or prospective, or its country of origin, are given.

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

This information is not provided. While "Evaluation at special sites" and "Preclinical and clinical validation" imply expert involvement, the document does not specify the number or qualifications of any experts involved in establishing ground truth for testing.

4. Adjudication Method for the Test Set:

This information is not provided.

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

This information is not provided. The document mentions "Comparison with a previously marketed medical device" as part of the validation process, but it does not describe an MRMC study or any effect size related to human readers improving with or without AI assistance. The BrainLAB ACL is an image-guided surgery system, not an AI diagnostic tool, so an MRMC study in the typical sense for AI might not apply directly.

6. If a Standalone Performance Study Was Done:

The document broadly states:

• "Subsystem & System Verification using Xtool."
• "Non-Xtool tests."
• "Preclinical and clinical validation."

However, it does not provide details of specific standalone performance metrics or results for the algorithm or system without human interaction, in the way a diagnostic AI would. The device is intended as an intraoperative image-guided navigation system, inherently involving a human surgeon.

7. The Type of Ground Truth Used:

This information is not explicitly stated or detailed. Given the nature of an image-guided surgery system, ground truth would likely refer to the accuracy of instrument tracking, alignment, and navigation relative to anatomical landmarks or surgical plans. This would probably be established through direct measurement, imaging correlation, and expert assessment of surgical outcomes or accuracy in phantom/cadaver studies. The document mentions "intra-operatively acquired landmarks that are used for planning and navigation," suggesting that anatomical landmarks form a key part of the "ground truth" for the system's function.

8. The Sample Size for the Training Set:

This information is not provided. The BrainLAB ACL is described as linking a probe to a virtual computer image space "overlaid with individually acquired patient landmarks." This suggests a system that relies on patient-specific imaging and real-time intraoperative data rather than a large, pre-trained machine learning model in the modern sense. Therefore, the concept of a "training set" for a machine learning algorithm might not be directly applicable in the conventional way.

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

This information is not provided, and as noted in point 8, the concept of a "training set" might not fully apply to this type of device in the same way it would for a contemporary AI diagnostic system. The system uses "individually acquired patient landmarks" for its function, which are established intraoperatively.

Summary of what is present:

The document focuses on establishing substantial equivalence to predicate devices (VectorVision® ACL (K042512) and BrainLAB Knee (K073615)) through a series of general verification and validation activities. These include design reviews, software validation, literature research, and comparison with previously marketed devices. The FDA's 510(k) clearance confirms this substantial equivalence based on the provided information.

However, the specific quantitative acceptance criteria and detailed study results you are asking for, which would typically be found in a detailed clinical or performance study report, are not included in this 510(k) summary. These summaries are regulatory documents intended to demonstrate that a new device is as safe and effective as a legally marketed predicate, not comprehensive scientific publications of specific performance trials.

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BrainLAB knee is intended to be an 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 an individual 3D-model of the patient's bone, which is generated through acquiring multiple landmarks on the bone surface. 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, x-ray, MR-based model of the anatomy. The system aids the surgeon to accurately navigate a knee prosthesis to the intraoperatively planned position. Ligament balancing and measurements of bone alignment are provided by BrainLAB knee.

Example orthopedic surgical procedures include but are not limited to:

• · Total Knee Replacement
• · Ligament Balancing
• Range of Motion Analysis
• · Patella Tracking

BrainLAB knee is intended to enable operational planning and/navigation in orthopedic surgery. It links a surqical instrument, tracked by flexible passive markers to virtual computer image space on an individual 3D-model of the patient's bone, which is gegerated through acquiring multiple landmarks on the bone surface. BrainLAB knee uses the registered landmarks to navigate the femoral and tibial cutting guides and the implant to the planned optimally position.

BrainLAB knee allows 3-dimensional reconstruction of the mechanical axis and alignment of the implants. BrainLAB knee software registers the, patient data needed for planning and navigating the surgery intraoperatively. No preoperative CT-scanning is necessary.

The provided text does not contain specific acceptance criteria or a detailed study proving the device meets acceptance criteria. The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed performance data against specific acceptance criteria.

Therefore, most of the requested information cannot be extracted from the given text.

Here's what can be stated based on the provided text:

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

• Acceptance Criteria: Not specified in the provided text.
• Reported Device Performance: Not detailed in the provided text as specific quantitative performance metrics. The document broadly states that "The validation proves the safety and effectiveness of the information provided by BrainLAB in this 510 (k) application."

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

• Not specified in the provided text.

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):

• Not specified in the provided text.

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

• Not specified in the provided 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:

• Not specified in the provided text. The device is a surgical navigation system, not an AI-assisted diagnostic device, so an MRMC study with "human readers" is unlikely to be relevant in this context.

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

• The device is described as an "intraoperative image guided localization system" that "aids the surgeon." This implies a human-in-the-loop system, so a standalone algorithm-only performance study as typically understood for diagnostic AI might not be directly applicable or detailed here. The text does not provide information about such a study.

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

• Not specified in the provided text. The system generates a "3D-model of the patient's bone, which is generated through acquiring multiple landmarks on the bone surface." Performance would likely be evaluated against the accuracy of navigation to planned positions, but the method for establishing this "ground truth" for validation is not described.

8. The sample size for the training set:

• Not specified in the provided text. The device relies on an "individual 3D-model of the patient's bone" generated intraoperatively, rather than a pre-trained AI model in the typical sense that would require a "training set."

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

• Not applicable/Not specified. As noted in point 8, the system's operation doesn't suggest a "training set" in the context of machine learning model development.

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