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The Renaissance X System is indicated for precise positioning of surgical instruments or spinal implants during general spinal and brain surgery. It may be used in either open or minimally invasive or percutaneous procedures.

Renaissance X 3D imaging capabilities provide a processing and conversion of 2D fluoroscopic projections from standard C-Arms into volumetric 3D image. It is intended to be used whenever the clinician and/or patient benefits from generated 3D imaging of high contrast objects.

The modified Renaissance X System hosts guidance for spine and brain procedures and intra-operative 3D image processing capabilities. It enables the surgeon to precisely position surgical instruments and/or implants (in spinal surgery). The planning of the surgical procedure and virtual placement of surgical instruments and/or implants (e.g., a screw) can be achieved through pre-operation planning based on the patient's CT scan or intra-operative planning based on Renaissance X 3D Scan image or on a 3D image uploaded from an external 3D image acquiring system. The modified Renaissance X System enables accurate deployment of surgical accessories in the precise anatomical location according to predefined planning. With the imaging capabilities of the system, the user can also visualize the implants on the patients CT. The modified Renaissance X System is a device modification of the original Renaissance X System cleared in 510(k) K140167 and the original Renaissance System with Brain Application cleared in 510(k) K120812.

The provided document is a 510(k) premarket notification for the Mazor Robotics Renaissance X System. Unfortunately, it does not contain specific acceptance criteria, detailed study results, or information about sample sizes for test or training sets, expert qualifications, or ground truth establishment relevant at the level of detail requested for a typical AI/ML device.

Instead, this document focuses on confirming that the modified system is substantially equivalent to previously cleared predicate devices. The performance testing section broadly states that "Software validation testing," "Stability Testing," and "Usability Testing" were performed, but it lacks the granular data required to fill in your requested table and study details.

Therefore, I cannot provide a table of acceptance criteria and reported device performance, or details about sample sizes, ground truth, and expert involvement based on the provided text. The document confirms that the device meets its design requirements and maintains usability, but without specific metrics or comparative effectiveness data against the predicate.

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The Renaissance X System is indicated for precise positioning of surgical instruments or implants during general spinal surgery. It may be used in either open or minimally invasive or percutaneous procedures.

Renaissance X 3D imaging capabilities provide a processing and conversion of 2D fluoroscopic projections from standard C-Arms into volumetric 3D image. It is intended to be used whenever the clinician and/or patient benefits from generated 3D imaging of high contrast objects.

The Renaissance X System hosts guidance for spine procedures and intraoperative 3D image processing capabilities. It enables the surgeon to precisely position surgical instruments and/or implants. The planning of the surgical procedure and virtual placement of surgical instruments and/or implants (e.g., a screw) can be achieved through pre-operation planning based on the patient's CT scan or intra-operative planning based on Renaissance X 3D Scan image or on a 3D image uploaded from an external 3D image acquiring system. With the imaging capabilities of the system the user can also visualize the implants on the patients CT.

The provided text describes a 510(k) premarket notification for the Mazor Robotics Ltd.'s Renaissance System, but it does not contain information about acceptance criteria or a specific study proving the device meets those criteria, especially in the context of AI performance metrics like sensitivity, specificity, or AUC.

The document primarily focuses on regulatory approval, substantial equivalence to a predicate device, and general performance testing (software validation, accuracy/repeatability, rigidity, collision avoidance). These are device-level safety and functional tests, not typically clinical performance studies that establish detailed acceptance criteria for diagnostic or AI-driven performance.

Here's a breakdown of what can be extracted or inferred from the document and what's explicitly missing:

Missing Information (and why it's missing from this document):

• A table of acceptance criteria and the reported device performance: This document does not specify quantitative acceptance criteria for clinical performance (e.g., sensitivity, specificity, accuracy). The "Accuracy and Repeatability Testing" mentioned would likely have internal, engineering-level acceptance criteria (e.g., +/- X mm deviation), but these are not disclosed.
• Sample sized used for the test set and the data provenance: Not mentioned, as no specific clinical performance study for an AI component is described.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not mentioned.
• Adjudication method: Not mentioned.
• Multi Reader Multi Case (MRMC) comparative effectiveness study: Not mentioned. The focus is on the device's functionality, not how it improves human reader performance with AI assistance.
• Standalone (i.e. algorithm only without human-in-the loop performance) study: Not mentioned.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not mentioned.
• The sample size for the training set: Not mentioned.
• How the ground truth for the training set was established: Not mentioned.

What can be inferred/extracted from the document regarding "performance testing" (though not directly "acceptance criteria" in the AI sense):

The document lists "Performance Testing" undertaken, which includes:

• Software validation testing: "demonstrate that the modified software version meets its design requirements."
• Accuracy and Repeatability Testing: "demonstrate that the Renaissance X System has maintained the required accuracy, as specified in the device design requirement. These tests have established that the system is accurate every time as an integrated system, including the full assembly as will be performed in the Operating Room, and that it is able to repeatedly perform in the same standard when required." (No specific metrics or acceptance values are given).
• Rigidity Testing: "These tests demonstrate the mechanical integrity of the Renaissance X System. It shows that the system's design meets the functional requirements with safety margins."
• Collision Avoidance Testing: "these tests validates the system's ability to avoid collisions in the operating field, both with the patient and various objects used during the operation."

Summary of what's provided vs. requested:

The provided document details the regulatory clearance (510(k)) of the Renaissance System, emphasizing its substantial equivalence to a predicate device and its overall safety and functional performance tests. It states that "dence X 3D imaging capabilities provide a processing and conversion of 2D fluoroscopic projections from standard C-Arms into volumetric 3D image. It is intended to be used whenever the clinician and/or patient benefits from generated 3D imaging of high contrast objects." However, it does not delineate specific (AI-related) acceptance criteria or detailed study results for its imaging or surgical guidance capabilities in the way a clinical trial or AI validation study would. The performance tests listed are general engineering and software validation tests, not clinical performance metrics against a defined ground truth.

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The Renaissance System is indicated for precise positioning of surgical instruments or spinal implants during general spinal and brain surgery. It may be used in open or minimally invasive or percutaneous procedures.

Renaissance 3D imaging capabilities provide a processing and conversion of 2D fluoroscopic projections from standard C-Arms into volumetric 3D image. It is intended to be used whenever the clinician and/or patient benefits from generated 3D imaging of high contrast objects.

The Renaissance System is a computer controlled miniature medical image-guided surgery (IGS) system which serves as a technological platform for solutions that provide accuracy, precision and accessibility in performing general spinal and brain procedures. The Renaissance System is designed to assist surgeons in precisely guiding handheld surgical tools and/or implants (in spinal surgery) according to a computerized, image-based, pre-operative plan along a given traiectory. The Renaissance System Brain Application was developed based on the same principles of operation for image guided brain surgeries. The Renaissance System Brain Application processes MRI and CT images via proprietary algorithms and based on the preoperative plan the RBT Device is programmed to position its articulating arm and thus the surgical instrument at the desired coordinates. Using a special skull attachment component (i.e., the RBT Base), the RBT Device attaches to the skull in the area where the procedure is being performed, and assists surgeons in precisely positioning the handheld surgical tools according to the computerized, image-based, pre-operative plan.

The main components of the Renaissance System Brain Application include:

• A. The RBT Device
• B. Workstation Console in the OR and Planning PC Workstation in the physician's office
• C. Renaissance Brain Application Accessories including RBT Base, Skull Screws, Guiding Arm, Star Marker, Base Screwdriver, etc.

Here's a summary of the acceptance criteria and study information for the Renaissance System with Brain Application, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly state a specific numerical sample size for a "test set" in the traditional clinical trial sense. The performance evaluations were conducted using:

• Human head phantom: Used for the Registration Accuracy Test.
• Cadaver brain: Used for the Cadaver Accuracy Study.

The data provenance is not explicitly stated in terms of country of origin of the data. Both studies (phantom and cadaver) would be considered pre-clinical performance evaluations rather than retrospective or prospective clinical studies involving human patients.

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

The document does not mention the use of experts to establish ground truth for the test set. The ground truth for the accuracy studies (phantom and cadaver) would have been established by physical measurements and the known "targets" within these models.

4. Adjudication Method for the Test Set

Not applicable, as there is no mention of human-reviewed ground truth or a need for adjudication in these performance studies.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and the Effect Size of Human Reader Improvement

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. This submission focuses on the performance of the device itself (standalone accuracy and stability) rather than its impact on human reader performance.

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

Yes, standalone performance studies were done. The "Bench Accuracy Study" and "Cadaver Accuracy Study" directly assess the algorithmic and mechanical accuracy of the Renaissance System Brain Application without a human-in-the-loop interacting with the system's output for diagnostic or decision-making purposes. These studies measured the device's ability to precisely guide instruments to a target.

7. The Type of Ground Truth Used

The ground truth used for the performance studies was based on:

• Known physical measurements/targets: For the Bench Accuracy Study, the ground truth would be the pre-defined target coordinates and depth in the testing setup.
• Known anatomical locations/targets: For the Cadaver Accuracy Study, the ground truth would be specific, identifiable anatomical points or simulated targets within the cadaver brain.
• Imaging truth: For the Registration Accuracy Test, the ground truth would be the known spatial relationship between features in the different imaging modalities (MRI, CT) against which the fusion/registration accuracy is measured.

8. The Sample Size for the Training Set

The document does not provide information on the sample size for a training set. This submission is for a medical device that assists in surgery through image guidance and robotics, not a machine learning model that requires a separate training set in the typical sense. The "proprietary algorithms" mentioned would likely have been developed and refined using engineering principles and simulated (or possibly internal, unmentioned) data, but the specific "training set" size is not relevant or disclosed in this context.

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

Not applicable, as a conventional "training set" with established ground truth in the context of machine learning is not discussed or relevant to the information provided for this device submission. The underlying principles for the algorithms would have been established through engineering design, physics, and mathematical modeling, rather than through a labeled training dataset.

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The Renaissance System is indicated for precise positioning of surgical instruments or implants during general spinal surgery. It may be used in either open or percutaneous procedures.

Renaissance 3D imaging capabilities provide a processing and conversion of 2D fluoroscopic projections from standard C-Arms into volumetric 3D image. It is intended to be used whenever the clinician and/or patient benefits from generated 3D imaging of high contrast objects.

The Renaissance System hosts guidance for spine procedures and intra-operative 3D image processing capabilities. It enables the surgeon to precisely position surgical instruments and/or implants. The planning of the surgical procedure and virtual placement of surgical instruments and/or implants (e.g. a screw) can be achieved through pre-operation planning based on the patients' CT scan. The "Scan-and-Plan" new feature enables intra-operative planning on the 3D Scan (formerly the C-Insight) image or on a 3D image uploaded from an external 3D image acquiring system (e.g., Medtronic O-arm). The new "Scan-and-Plan" feature replaces the need for a pre-operative CT scan and pre-operative planning, although this feature is still available in the system.

The modified Renaissance System also allows retrieving DICOM files from the hospitals' PACS system for operation planning purposes.

In addition, the modified Renaissance System enables the user to download MRI data and fuse it with CT data in order to provide the user with additional information on the patient's anatomy during the pre-operative planning stage.

The Mazor Robotics Renaissance System (K113228) is a surgical guidance system for spinal procedures. The provided documentation does not explicitly list acceptance criteria in a table format with corresponding device performance values, nor does it detail a standalone human-in-the-loop study with specific accuracy metrics. However, it mentions performance testing and a comparison to a predicate device.

Here's an analysis of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not provide a table of acceptance criteria with quantitative performance metrics. It generally states that software validation and registration accuracy tests were performed.

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

The provided document does not specify the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective). The tests mentioned (software validation and registration accuracy) appear to be internal engineering tests rather than clinical studies on patient data.

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

The document does not mention using experts to establish ground truth for a test set. The performance tests described are related to software and system accuracy, which would typically be verified against known specifications or physical standards rather than expert consensus on medical images.

4. Adjudication Method

Since the document does not describe the use of experts or a test set requiring ground truth adjudication, there is no mention of an adjudication method.

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

The provided information does not indicate that a multi-reader multi-case (MRMC) comparative effectiveness study was performed, or any effect size related to human reader improvement with AI assistance. The device is a surgical guidance tool, not an AI diagnostic imaging system that would typically be evaluated in an MRMC study for reader performance enhancement.

6. Standalone Performance Study (Algorithm Only)

The document does not explicitly describe a standalone (algorithm only) performance study in terms of diagnostic accuracy or precise guidance error metrics. The performance tests mentioned pertain to software functionality and maintenance of accuracy according to design requirements. The system's core function is to guide a surgeon; its "performance" is inherently linked to its integration into a surgical workflow.

7. Type of Ground Truth Used

Based on the described tests:

• Software Validation: Ground truth would likely be the defined software design requirements and expected outputs.
• Registration Accuracy: Ground truth would be the known physical coordinates or measurements of a phantom or calibrated test object against which the system's registration capabilities are verified. There's no indication of pathology, outcomes data, or expert consensus being used as ground truth for these tests.

8. Sample Size for the Training Set

The document does not provide information regarding a training set sample size. The description of the device and its capabilities (e.g., 3D image processing, planning on CT/MRI) suggests that it's a rule-based or image processing system rather than a machine learning model that requires a distinct "training set" in the conventional sense.

9. How Ground Truth for the Training Set Was Established

Since there's no mention of a traditional machine learning training set, there is no information on how ground truth for a training set was established.

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The Renaissance System is indicated for precise positioning of surgical instruments or implants during general spinal surgery. It may be used in either open or percutaneous procedures.

Renaissance 3D imaging capabilities provide a processing and conversion of 2D fluoroscopic projections from standard C-Arms into volumetric 3D image. It is intended to be used whenever the clinician and/or patient benefits from generated 3D imaging of high contrast objects.

The Renaissance system is a device modification of the TenZing system (K102130). which is comprised of the original SpineAssist System and the C-InSight System.

The SpineAssist application enables the surgeon to precisely position surgical instruments or implants during general spinal surgery. This is achieved through preoperation planning and virtual placement of the surgical instrument or implant (e.g., a screw) based on the patients' CT data. During the surgical procedure the pre-planned instrument or implant positions are located and projected on Fluoroscopy images relative to the SpineAssist device position while the SpineAssist arm is then guided to the actual position. The SpineAssist is described in previously cleared 510(k) submissions K033413, K051676, K063607 and K073467.

The C-InSight application is a software based product, which converts a sequence of two-dimensional fluoroscopy images into a 3D volumetric image, intraoperatively. The C-InSight computer is connected to a traditional C-Arm in the operating room and grabs all images from the C-Arm. Using a tracking algorithm, the C-InSight software is able to convert a continuous scan around the region of interest into a 3D image, intra-operatively. The C-InSight is described in the previously cleared 510(k) submission K081672.

The TenZing System cleared in 510(k) submission K102130 is a workstation which contains both the C-InSight and SpineAssist components in one workstation. This allows the physician to perform SpineAssist procedures and C-InSight procedures as independent applications, but using the same workstation console. Furthermore, the TenZing System allows the surgeon to perform SpineAssist procedures and obtain an intra-overative 3D verification image using the C-InSight application. Thus, the surgeon can obtain real time feedback regarding instrument and/or implant positioning.

The modified system is called the Renaissance System. The device modifications include a newly designed workstation (hardware change), a slightly modified SpineAssist Guiding Device (with colored LED lights), and modified software with improved GUI for the original TenZing software (software change) and the ability to perform CT-Fluoroscopy registration using the fluoroscopy images obtained from the C-InSight application (i.e., CT to C-InSight registration).

The provided text describes the Renaissance System, a robotic surgical guidance system for spinal surgery. Here's a breakdown of the acceptance criteria and study information:

1. Table of Acceptance Criteria and Reported Device Performance

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

The provided text mentions "Performance Testing" but does not specify the sample size for the CT to C-InSight Registration Accuracy Testing. It also does not mention the data provenance (e.g., country of origin, retrospective or prospective) for any of the performance tests.

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

The document does not provide information regarding the number of experts, their qualifications, or their role in establishing ground truth for any of the performance tests. The focus is on technical compliance and equivalence.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method for the test set.

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

No. The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study, nor does it discuss the effect size of human readers with or without AI assistance. The testing described is primarily focused on the device's technical specifications and safety rather than its impact on human performance.

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

The performance testing listed ("Software validation testing," "CT to C-InSight Registration Accuracy Testing," "IEC 60601-1 Electrical and Mechanical Safety Testing," and "IEC 60601-1-2 Electromagnetic Compatibility Testing") primarily describes standalone algorithm and system performance in a technical context. The "CT to C-InSight Registration Accuracy Testing" would specifically assess the algorithm's ability to accurately register CT and C-InSight data without direct human intervention impacting the registration process itself.

7. The Type of Ground Truth Used

For the "CT to C-InSight Registration Accuracy Testing," the type of ground truth is implicit. Typically, such accuracy testing would involve:

• Phantom studies: Using a known physical phantom with measurable fiducials where the "true" positions are precisely known.
• Reference standard imaging: Using a gold-standard imaging modality (e.g., a highly accurate CT scan) as the 'true' representation against which the system's registration of fluoroscopic images to CT is compared.
  The document does not explicitly state whether expert consensus, pathology, or outcomes data were used as ground truth for any of the tests reported here.

8. The Sample Size for the Training Set

The document does not provide any information about a training set or its sample size. The focus is on verification and validation of the modified device rather than the development of a new AI model with training data.

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

Since no information on a training set is provided, there is no information on how its ground truth was established. This device submission is for a modification of an existing system (TenZing) and primarily focuses on technical and safety testing of the modifications, not the development of a new AI algorithm requiring a specific training dataset and ground truth establishment methodology.

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