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HIP7 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 either on a patient's preoperative image data being processed by Brainlab IGS platforms, or 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 considered to be safe and effective, and where a reference to a rigid anatomical structure, such as a long bone or vertebra, can be identified relative to a CT, X-Ray, or MR-based model of the anatomy. The system aids the surgeon to accurately navigate a hip endoprosthesis to the preoperatively or intraoperatively planned position.

Example orthopedic surgical procedures for the Computer Assisted Total Hip Replacement – HIP7 use case include but are not limited to:

• Total Joint Replacement
• Minimally invasive orthopedic surgery

The HIP7 system is intended to enable navigation in orthopedic hip replacement surgery. It links a surgical instrument, tracked by flexible passive markers to virtual computer image space on an individual three-dimensional model of the patient's bone, which is generated through acquiring multiple landmarks on the bone surface or soft tissue. The HIP7 system uses the registered landmarks to navigate the necessary surgical tool, i.e., cup inserter, to the desired orientation. Additionally, it enables to intra-operatively measure the changes in leg length and offset.

The HIP7 system provides a three-dimensional reconstruction of the relevant anatomical axes and planes of the femur and pelvis to aid the alignment of the implants and to determine leg length and offset parameters. Based on the selected procedure, the HIP7 system loads implant and instrument data that has been provided by the implant manufacturer. The implant is selected according to the available license and is then shown in the software in relation to the determined anatomical structures. A preoperative x-ray may optionally me loaded. The HIP7 system does not require CT-based imaging.

The provided document does not contain details about specific acceptance criteria, study design, or performance metrics for the HIP7 device in the format requested. The document is primarily a 510(k) summary for regulatory clearance, focusing on demonstrating substantial equivalence to a predicate device rather than presenting a detailed clinical or performance study report.

Therefore, most of the requested information cannot be extracted directly from this document.

However, based on the nature of the device and the presented "SE Table" (Substantial Equivalence Table), we can infer general aspects:

The document clearly states: "The HIP7 system has been verified and validated according to Brainlab's procedures for product design and development." This implies that internal studies were conducted to ensure the device meets its design requirements.

It also mentions under "Cup Navigation": "Acceptance criteria for measuring inclination and anteversion had been defined for Hip 6.0. Verification tests have shown that these acceptance criteria are also met when inclination and anteversion are measured in relation to a functional plane (including automatic calculation of pelvic tilt)." This is the only direct reference to "acceptance criteria" and "verification tests" in the provided text.

Here's a breakdown of what can be inferred or stated as not available:

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

   • Acceptance Criteria Mentioned: "Acceptance criteria for measuring inclination and anteversion had been defined for Hip 6.0." (Specific values are not provided).
   • Reported Device Performance: "Verification tests have shown that these acceptance criteria are also met when inclination and anteversion are measured in relation to a functional plane (including automatic calculation of pelvic tilt)." (Specific performance values or metrics are not provided).

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

   • This information is not provided in the document. The "verification tests" mentioned are likely internal engineering verification, not a clinical trial with a defined sample size as typically reported for AI/diagnostic devices.

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 applicable as this is a surgical navigation system, not a diagnostic AI system requiring expert image interpretation, and the details of "verification tests" are not specified.

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

   • Not applicable; details of verification tests are not provided.

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, this is an intraoperative image-guided localization system, not a diagnostic AI system involving human "readers" in that context. The document does not describe such a study.

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

   • Given it's a "localization system" and "aids the surgeon," its performance inherently involves integration into a surgical workflow. "Verification tests" for navigation systems typically assess accuracy of tracking and display, which can be seen as an 'algorithm only' performance component, but specific details are not provided.

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

   • For a navigation system, ground truth typically involves a highly accurate measurement system (e.g., optical tracking system with known accuracy, physical phantoms) to assess the accuracy of instrument positioning and display against a known reference. The document does not specify the ground truth mechanism used in their "verification tests."

8. The sample size for the training set:

   • This device is not described as an AI/ML model that would require a separate "training set" in the context of deep learning. It's a navigation system. If there are underlying algorithms that were "trained," those details are not provided.

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

   • Not applicable, as a distinct training set for an AI/ML model is not mentioned.

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