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The AIS S4 Navigation Instruments are intended to assist the surgeon in precisely locating anatomical structures in either open, minimally invasive, or percutaneous procedures. They are indicated for use in surgical spinal procedures, in which the use of stereotactic surgery may be appropriate, and where reference to a rigid anatomical structure, such as the pelvis or a vertebrae can be identified relative to the acquired image (CT, MR, 2D fluoroscopic image or 3D fluoroscopic image reconstruction) and/or an image data based model of the anatomy. These procedures include but are not limited to spinal fusion during the navigation of polyaxial screws (T1-T3).

The AIS S4 Navigation Instruments are manual surgical instruments which are designed to interface with BrainLAB's already cleared surgical navigation systems. Instruments in this system may be pre-calibrated or manually calibrated to already cleared systems using manufacturers' instructions. These instruments are intended to be used in spine applications to perform general or manual functions within the orthopedic surgical environment.

1. Table of Acceptance Criteria and Reported Device Performance:

The document explicitly states: "The AIS S4 Navigation Instruments met the performance requirements. No safety or effectiveness issues were raised by the performance testing." However, specific numerical acceptance criteria (e.g., accuracy thresholds, precision values) are not provided in this submission. The nature of the device (surgical navigation instruments designed to interface with other cleared systems) suggests that the performance requirements likely relate to the accuracy and reliability of tracking and spatial localization when used with the BrainLAB navigation systems.

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

The document states "BrainLAB conducted validation activities including usability testing with the AIS S4 Navigation Instruments." However, no information regarding the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective) is provided.

3. Number of Experts Used to Establish Ground Truth and Their Qualifications:

The document does not describe the specific ground truth establishment process for the performance data. Therefore, the number of experts and their qualifications are not mentioned. Given that the performance data appears to be from "validation activities including usability testing," it's plausible that healthcare professionals were involved in assessing the usability and functionality, but their specific roles in establishing a quantifiable ground truth are not detailed.

4. Adjudication Method:

No adjudication method is described in the provided text.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

No MRMC comparative effectiveness study is mentioned. The submission focuses on the standalone performance and equivalence of the AIS S4 Navigation Instruments when used with existing BrainLAB navigation systems, rather than comparing human readers with and without AI assistance.

6. Standalone Performance (Algorithm Only without Human-in-the-Loop Performance):

This submission is about surgical navigation instruments, which are physical tools that assist a surgeon; they are not an AI algorithm in the typical sense that would have "algorithm-only" performance without human interaction. The "performance data" described refers to "validation activities including usability testing" of the instruments themselves. Therefore, while technically these instruments are used "standalone" in the sense that they are physical tools, their performance is inherently tied to human use and their interface with the BrainLAB navigation system. The study described focuses on their functional performance in this context, rather than a quantifiable, algorithm-only output.

7. Type of Ground Truth Used:

The document mentions "validation activities including usability testing," and states that the instruments "met the performance requirements." This suggests the ground truth was likely based on functional assessment and verification against predefined specifications for accuracy, precision, and usability when integrated with the BrainLAB navigation systems. It is not explicitly stated to be based on expert consensus, pathology, or outcomes data in the traditional sense, but rather on the technical performance and usability of the instruments.

8. Sample Size for the Training Set:

This device is a set of physical surgical instruments, not an AI or machine learning algorithm that requires a "training set" of data. Therefore, this concept is not applicable, and no training set sample size is provided.

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

As the device is a set of physical surgical instruments and not an AI algorithm, there is no training set and therefore no ground truth establishment for a training set.

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The AIS S4 Cervical Navigation Instruments are intended to assist the surgeon in precisely locating anatomical structures in either open, minimally invasive, or percutaneous procedures. They are indicated for use in surgical spinal procedures, in which the use of stereotactic surgery may be appropriate, and where reference to a rigid anatomical structure , such as the pelvis or a vertebrae can be identified relative to the acquired image (CT, MR, 2D fluoroscopic image or 3D fluoroscopic image reconstruction) and/or an image data based model of the anatomy. These procedures include but are not limited to spinal fusion during the navigation of pedicle screws (T1-T3).

The AIS S4 Cervical Navigation Instruments are manual surgical instruments which are designed to interface with BrainLAB's already cleared surgical navigation systems. Instruments in this system may be pre-calibrated or manually calibrated to already cleared systems using manufacturers' instructions. These instruments are intended to be used in spine applications to perform general or manual functions within the orthopedic surgical environment.

The provided text describes the Aesculap S4 Cervical Navigation Instrumentation, which is a set of manual surgical instruments designed to interface with BrainLAB's surgical navigation systems. The submission is a Traditional 510(k) Premarket Notification.

Here's the breakdown of the acceptance criteria and study information:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly list specific quantitative acceptance criteria for the device's performance (e.g., a certain level of accuracy in millimeters). Instead, it describes a more qualitative assessment.

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

• Sample Size: The document states that "BrainLAB conducted validation activities including usability testing with the AIS Navigation Instruments." However, it does not specify the sample size (e.g., number of users, number of cases tested) for this usability testing or any other performance testing.
• Data Provenance: The document does not specify the country of origin of the data. The testing appears to be conducted by BrainLAB, a company with international operations, but the specific location of the testing is not mentioned. It is also not explicitly stated whether the data was retrospective or prospective, though usability testing typically involves prospective data collection.

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

• The document mentions "usability testing with the AIS Navigation Instruments." Usability testing typically involves end-users (surgeons) but does not specify the number or qualifications of these experts for establishing ground truth related to navigational accuracy or effectiveness. The study relies on the outcome of the usability testing and performance testing rather than expert-established ground truth in a clinical or imaging sense.

4. Adjudication Method for the Test Set

• The document does not mention any adjudication method for the test set. Given the nature of the testing described (usability and performance requirements), it's unlikely a formal adjudication process (like 2+1 or 3+1 consensus) would be used as it would be in an imaging diagnostic study. The assessment would likely be based on whether the instruments appropriately facilitated the surgical steps and met performance specifications.

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

• No MRMC comparative effectiveness study was done. The document states, "Clinical data was not needed for the AIS Navigation Instruments." The submission focuses on substantial equivalence based on technological characteristics and performance testing.

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

• This question is not applicable as the device (AIS S4 Cervical Navigation Instruments) is a set of manual surgical instruments designed to interface with surgical navigation systems. It is not an AI algorithm or a standalone software. The performance testing would inherently involve human interaction with the instruments and the navigation system.

7. The Type of Ground Truth Used

• The document implies that the ground truth for "performance requirements" would be established by the functional specifications and design requirements of the instruments when used with the BrainLAB navigation systems. For usability testing, the "ground truth" would be whether the instruments are usable and meet the functional needs of the surgeons. There is no mention of expert consensus, pathology, or outcomes data being used as ground truth for this submission, as clinical data was not required.

8. The Sample Size for the Training Set

• This question is not applicable. The device is a set of manual surgical instruments; it is not an AI algorithm that requires a training set.

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

• This question is not applicable as there is no AI algorithm or training set involved.

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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

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.

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