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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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