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Synthes Lateral Entry Femoral Nail System is intended to stabilize femoral shaft fractures, subtrochanteric fractures, ipsilateral neck/shaft fractures, impending pathologic fractures, non-unions and malunions. The Synthes Lateral Entry Femoral Nail System also indicated for use with manual accessory instruments intended to be used with the Brainlab Trauma Navigation System

Synthes ALPS System is a plate and screw system intended to treat fractures of various long bones including radius, ulna, humerus, tibia, fibula, and femur. The Synthes ALPS is also indicated for use with manual accessory instruments intended to be used with the Brainlab Trauma Navigation System

Synthes LCP System is intended for buttressing multifragmentary distal femur fracture including supracondylar, intra-articular and extra-articular condylar fractures, fractures in normal or osteopenic bone, ad non -unions and malunions. The Synthes LCP is also indicated for use with manual accessory instruments intended to be used with the Brainlab Trauma Navigation System.

The Synthes Curved Broad Plates are intended for fixation of various long bones, such as the humerus, femur, and tibia. They are also for use in fixation of per-prosthetic fractures, osteopenic bone and nonunions or malunions. The Synthes Curved Broad Plates is also indicated for use with manual accessory instruments intended to be used with the Brainlab Trauma navigation system

The Synthes Curved Condylar Plates are intended for buttressing multi-fragmentary distal femur fractures, including: supracondylar, intra-articular and extra articular condylar fractures, peri-prosthetic fractures and fractures in normal or osteopenic bone, nonunions/malunions, and osteotomies of the femur. The Synthes Curved Condylar Plates is also indicated for use with manual accessory instruments intended to be used with the Brainlab Trauma Navigation System

Synthes LCP Distal Femur Plates are intended for buttressing multi-fragmentary distal femur fractures including: supracondylar, intra-articular and extra-articular condylar, periprosthetic fracture and fractures in normal osteopenic bone, non-unions and malunions, and osteotomies of the femur. The Synthes LCP Distal Femur Plate is also indicated for use with manual accessory instruments intended to be used with the Brainlab Trauma Navigation System.

The Synthes (USA) Brainlab Compatible Instruments consist of manual insertion handles and aiming arms which are used to facilitate the surgical technique related to the Synthes Lateral Entry Femoral Nail, LISS, LCP Distal Femur Plate, Locking Condylar Plate, and Curved Condylar Plate systems. The instruments primarily facilitate the manual insertion of the bone plate and intramedullary nail implants included in the predicate systems, but also include a design feature which allows attachment of system accessories to the Brainlab Trauma navigation system.

The provided document is a 510(k) summary for the "Synthes Brainlab Trauma Compatible Instruments". This submission is to modify existing cleared systems to introduce manual accessory instruments intended for compatibility with the Brainlab Trauma Navigation System.

The document does not contain acceptance criteria in the traditional sense of performance metrics (like accuracy, sensitivity, specificity) for a diagnostic or AI device. Instead, it focuses on demonstrating substantial equivalence to predicate devices. The study performed is a simulated use study and dimensional tolerance analyses, not a clinical study to assess performance against specific acceptance criteria for an AI or diagnostic tool.

Therefore, many of the requested items (e.g., sample size for test set, number of experts, adjudication method, MRMC study, standalone performance, training set details) are not applicable to this type of submission.

Here's a breakdown of the information that can be extracted or inferred from the provided text, while explicitly noting what is not present:

1. Table of Acceptance Criteria and Reported Device Performance

No explicit acceptance criteria or performance metrics (e.g., accuracy, sensitivity, specificity) are provided in the document. The submission is focused on demonstrating substantial equivalence, meaning the modified instruments perform as intended and do not raise new questions of safety or effectiveness compared to the predicate devices.

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

• Sample Size for Test Set: Not applicable. This was a simulated use study and dimensional tolerance analysis, not a test set of medical cases. The number of instruments or surgical simulations is not specified.
• Data Provenance: Not applicable. The study involved engineering analysis and simulated use, likely conducted in a lab environment. No information on country of origin for patient data or retrospective/prospective nature is relevant here.

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

• Number of Experts: Not applicable. Ground truth as typically defined for diagnostic or AI studies is not relevant here. The study involved engineering assessments and potentially surgical technique evaluations by product developers or surgical experts, but their number and specific qualifications for establishing 'ground truth' are not detailed.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. This type of study does not involve adjudication of expert readings or diagnoses.

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

• MRMC Study Done: No. This is not an AI or diagnostic device designed to improve human reader performance. It's a surgical instrument modification.
• Effect Size of AI vs. Without AI Assistance: Not applicable.

6. Standalone Performance Study (Algorithm Only)

• Standalone Study Done: Not applicable. This is a manual surgical instrument, not an algorithm or AI system.

7. Type of Ground Truth Used

• Type of Ground Truth: Not applicable in the context of diagnostic or AI performance. The "ground truth" for this submission would be defined by engineering standards, functional requirements for instrument compatibility and surgical technique, and safety assessments.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. This device does not involve machine learning or a training set.

9. How Ground Truth for Training Set Was Established

• How Ground Truth for Training Set Was Established: Not applicable.

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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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