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Automatic Registration is a surgical device for image guided surgery intended to be used in combination with compatible Brainlab navigation systems. Automatic Registration provides an image registration for intraoperatively acquired 3D CT/ CBCT or fluoroscopic images. It consists of the software module Automatic Registration and hardware accessories.

The Subject Device is intended to be used in combination with compatible Brainlab navigation systems. Automatic Registration provides an image registration for intraoperatively acquired 3D CT/CBCT or fluoroscopic images. It consists of the software module Automatic Registration and hardware accessories.

In a spinal context, Automatic Registration serves as accessory to the Spine & Trauma navigation system.

The Matrices are reusable devices delivered in non-sterile condition. The devices makes the Correlation ("Registration") of Intraoperative acquired patient data to the surgical environment possible by determining its position in relation to the patient and the navigated instruments.

The document describes K203679, a premarket notification for the "Automatic Registration" device by Brainlab AG. The device is a surgical device for image-guided surgery intended to be used in combination with compatible Brainlab navigation systems, providing image registration for intraoperatively acquired 3D CT/CBCT or fluoroscopic images.

Here's an analysis of the acceptance criteria and study data provided:

Acceptance Criteria and Device Performance

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For spinal use the CIRQ Robotic Alignment Module is an accessory to the compatible Brainlab IGS Spinal software applications and is intended to be an intraoperative image guided localization system to achieve pre-planned trajectories with surgical instruments.

The medical indications for use of the CIRQ Robotic Alignment Module for spinal use is the treatment of diseases where the placement of spinal screws is indicated.

The device is an accessory to the compatible Brainlab IGS Spinal software applications (K183605) and is intended to be an intraoperative image guided localization system to support the surgeon to achieve pre-planned trajectories with surgical instruments.

The device consists of the Cirq Robotic Alignment Module which is connected to the Surgical Base System from Medineering. It serves to align instruments to a pre-planned trajectory during surgical procedures using the Cirq Robotic Application Software together with the Brainlab IGS Spinal software applications.

Infrared passive marker based tracking as provided by the optical tracking camera unit of the navigation platform is used to determine the instrument's and patient's position. The relation between the patient and the reference attached to the patient is realized with a registration (manually or automatically).

The device is manually pre-aligned roughly to the region of interest by opening the brakes of the Surgical Base System using its 7 degrees of freedom. Following this, the tracking information is used to automatically fine align a tracked guide attached to the Cirq Robotic Alignment Module to achieve a pre-planned trajectory controlled by the CIRQ Robotic Application Software. After finishing the alignment, the device remains in this position and the surgical instruments through the provided guide to perform the surgical steps intended without losing the trajectory.

The provided FDA 510(k) summary for the CIRQ Robotic Alignment Module outlines various tests conducted to demonstrate the device's safety and effectiveness. However, it does not contain specific details regarding acceptance criteria for quantitative performance metrics, nor does it describe a clinical study of the device against a defined ground truth, a multi-reader multi-case (MRMC) study, or statistical analyses of AI performance.

The information provided focuses on engineering and usability verification, with conclusions stating "successful" or "met." It confirms that the device is an accessory for achieving pre-planned surgical trajectories but does not present quantifiable performance data (e.g., accuracy, precision) against specific acceptance criteria for robotic alignment.

Therefore, based on the provided text, I cannot answer sections 1, 2, 3, 5, 6, 7, 8, and 9 of your request as they relate to quantitative device performance, clinical study design, and ground truth establishment in a medical AI context. The document describes a device (hardware/software integration for robotic alignment) rather than a diagnostic AI algorithm.

However, I can extract information relevant to the types of tests performed that would typically lead to acceptance criteria, even if the criteria themselves are not explicitly numeric in this summary:

Summary of Device Performance and Testing (Based on provided 510(k) summary):

The provided document describes engineering verification and validation activities for the CIRQ Robotic Alignment Module. It does not detail a study proving the device meets quantitative performance acceptance criteria in the way one would expect for an AI diagnostic device (e.g., sensitivity, specificity, or AUC). Instead, it focuses on functional, safety, and integration testing.

Here's a breakdown of the relevant information from the document, acknowledging that it doesn't fit the typical "acceptance criteria for an AI study" format:

1. A table of functional/safety criteria and reported device performance (interpreted from the "Test" table):

2. Sample size used for the test set and the data provenance:

• For the "Verification of general functions" and "Human factors / Usability Testing," the testing involved a "MIS Spine Training Model in a simulated clinical environment." The number of models or simulated cases is not specified.
• The "data provenance" is not explicitly mentioned as per country of origin. The tests were simulated clinical environments or engineering tests.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• For "Verification of general functions," "spinal surgeons" were involved.
• For "Human factors / Usability Testing," "surgeons and OR nurses" were involved.
• The specific number or detailed qualifications (e.g., years of experience) for these experts are not provided. The "ground truth" here is effective and safe usage in a simulated environment, rather than a clinical diagnosis ground truth.

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

• No adjudication method is described for the "test set" as this was not a diagnostic study with ambiguous interpretations requiring adjudication.

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 MRMC or comparative effectiveness study comparing human performance with and without AI assistance is described. This device is a robotic alignment module, an accessory for surgical guidance, not a diagnostic AI tool that assists human readers.

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

• The device is designed as a human-in-the-loop system (an accessory supporting the surgeon). Standalone performance in the diagnostic AI sense is not applicable or described. The tests described are on the functionality and safety of the integrated system including the robotic module.

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

• For functional and human factors testing, the "ground truth" was operational success and safety as evaluated by the users (spinal surgeons, OR nurses) in a simulated environment (MIS Spine Training Model for functional tests). This is a performance-based ground truth related to task execution and user experience, rather than a diagnostic accuracy ground truth based on pathology or clinical outcomes.

8. The sample size for the training set:

• This document describes the verification and validation of a medical device, not the development or training of a machine learning model. Therefore, no "training set" or "sample size for training set" is applicable in the context of an AI model's development.

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

• Not applicable, as this is not an AI model requiring a training set with established ground truth.

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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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