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UroNav is a stereotaxic medical device intended to assist the clinician with planning and guidance for clinical, interventional and/or diagnostic procedures for biopsy and/or soft tissue ablation. It provides 2D and 3D visualization of Ultrasound (U/S) images and the ability to fuse and register these images with those from other imaging modalities such as Magnetic Resonance (MR). It also provides the ability to display a simulated image of a tracked insertion tool such as a biopsy needle, guidewire, grid plate or probe on a computer monitor screen that shows images of the target organ and the current and the projected future path of the interventional instrument taking into account patient movement. Other software features include patient data management, multi-planar reconstruction, segmentation, image measurements and 2D/3D image registration. UroNav is indicated for medical conditions that require interventional and/or diagnostic procedures of the prostate gland. The software is not intended for diagnosis. The software is not intended to predict ablation volumes or predict ablation success.

The system assists physicians in guiding interventional instruments to pre-defined targets, which may be identified pre-procedurally or intra-procedurally using imaging data or relative to a designated position on the patient.

As an image fusion system, UroNav combines pre-procedural imaging, such as Magnetic Resonance Imaging (MRI), with real-time intra-procedural imaging from ultrasound (US) systems. This fusion capability enables precise localization of areas of interest detected in one imaging modality and maps them onto another. The system also facilitates spatial registration between two-dimensional (2D) and three-dimensional (3D) imaging datasets and between imaging data and physical patient space.

The UroNav 4 System includes the following components:

• Electromagnetic Measurement System (EMMS): Comprising a Field Generator, System Control Unit, and System Interface Unit(s) for electromagnetic tracking.
• System Unit: Incorporating a central processing unit (CPU), monitor, medical-grade power supply, mobile cart, and UroNav software.
• Field Generator Stand: Ensuring stability for electromagnetic tracking during procedures.
• Software Features: Patient data management, multiplanar reconstruction, segmentation, image measurement tools, and 3D image registration capabilities.
• Compatible Devices: Compatible with commercially available ultrasound machines, probes, needle guides, biopsy devices, and applicators.

The system reconstructs 3D ultrasound images from live 2D video streams connected to ultrasound systems. Clinicians can select targets and navigate instruments with precision through its interactive interface, which features a keyboard, mouse, and visual display. The UroNav system supports diverse clinical environments, such as hospital operating rooms, outpatient surgery centers, ultrasound suites, and procedure rooms.

The UroNav 4 design ensures compatibility with existing clinical workflows by utilizing commercially available ultrasound machines and accessories.

Additional software functionalities include patient data management, multiplanar reconstruction of images, segmentation tools for enhanced visualization, image measurements, and 3D image registration.

The UroNav 4 System is intended to aid physicians in performing minimally invasive diagnostic and therapeutic procedures by providing real-time image-guided navigation. It is not intended to replace physician judgment.

The provided FDA 510(k) clearance letter for UroNav 4 primarily focuses on demonstrating substantial equivalence to a predicate device based on similar indications for use, technological characteristics, and non-clinical testing. It explicitly states that the software is "not intended for diagnosis" and is instead a "stereotaxic medical device intended to assist the clinician with planning and guidance for clinical, interventional and/or diagnostic procedures."

This type of device is not an AI diagnostic tool that directly interprets medical images for diagnosis. Therefore, the typical acceptance criteria and study designs associated with AI algorithms for diagnostic tasks (e.g., sensitivity, specificity, MRMC studies, ground truth based on pathology) do not directly apply to UroNav 4 as described. The provided document details non-clinical testing to ensure the device's functionality, usability, safety, and accuracy in its guidance and planning capabilities.

Based on the provided text, a table for acceptance criteria and device performance for diagnostic accuracy metrics cannot be generated because the device is not intended for diagnosis. Instead, the acceptance criteria are related to its performance as a guidance and planning tool.

Here's an analysis based on the provided text, addressing the points where information is available and explaining why other points are not applicable or not detailed in this specific document:

Acceptance Criteria and Device Performance (Based on Non-Clinical Testing)

The document describes non-clinical testing to ensure the device meets predefined criteria for its intended use as a guidance and planning tool.

Study Details (Based on Non-Clinical Testing described)

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

   • The document describes non-clinical testing, not a clinical study with a patient test set in the traditional sense of diagnostic performance. The "test set" would refer to the various tests conducted on the software and hardware components.
   • Specific numbers of test cases, simulated data, or system configurations used for functionality, usability, compatibility, and accuracy testing are not specified in the provided text.
   • Data provenance is not applicable in the context of clinical patient data, as this concerns non-clinical validation. The testing was conducted to validate the device's functional and safety requirements.

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

   • This question is not directly applicable as the device is not intended for diagnosis, and thus, "ground truth" derived from expert image interpretation for diagnostic purposes (like presence/absence of disease) is not the focus of its validation.
   • The "acceptance criteria" here are based on engineering specifications, software requirements, and usability standards. Experts (e.g., engineers, usability specialists, potentially clinical users for usability feedback) would have defined these criteria and evaluated compliance, but the document does not specify their number or qualifications for establishing "ground truth" on a test set of medical images for diagnostic accuracy.

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

   • Adjudication methods (like 2+1, 3+1) are typically used in clinical diagnostic studies where multiple readers interpret images, and a consensus or tie-breaking mechanism is needed to establish ground truth for a diagnostic outcome. This is not applicable to the non-clinical functional and safety testing described for UroNav 4.

4. 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 study is described in the provided text. The device is a "stereotaxic medical device intended to assist the clinician with planning and guidance," not an AI-powered diagnostic tool that interprets images to provide a diagnosis or improve human reader diagnostic accuracy for image interpretation. Therefore, a comparative effectiveness study showing improvement in reader performance is not included in this clearance overview, nor would it typically be required for this type of device functionality.

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

   • The document confirms that non-clinical testing on the software functionality, EM tracking accuracy, and advanced annotation workflows was performed. These tests would evaluate the algorithm's performance in specific functionalities (e.g., calculation accuracy, tracking precision) independent of human operators, but still within the context of the device's role as a guidance system, not a diagnostic system. Specific quantitative results (e.g., EM tracking accuracy in mm) are not provided in this summary.

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

   • For the non-clinical testing described, the "ground truth" would be established by engineering specifications, known input parameters for tests, and adherence to validated functional requirements and standards. For example:
     • For EM tracking accuracy, the ground truth would be precise, physically measured positions and orientations.
     • For annotation workflow validation, the "ground truth" would be the expected, mathematically correct dimensions, volumes, or coverage estimations based on predefined inputs.
     • For software functionality, the ground truth is whether the software performs the intended function as designed and specified (e.g., data migration occurs without error, security measures are effective).
   • This is distinct from clinical ground truth like pathology or patient outcomes, which are not mentioned as part of the validation for UroNav 4 in this document.

7. The sample size for the training set:

   • Training sets are typically relevant for machine learning or AI models that learn from data. While UroNav 4 uses "AI" in the product code QTZ ("Radiological image processing software for ablation therapy planning and evaluation"), the specifics of any AI/ML components and their training are not detailed in this document. The focus is on the device's functionality as a guidance system. Therefore, the size of a training set is not specified.

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

   • As the training set size is not specified, how its ground truth was established is also not detailed in this document. If machine learning components are present (suggested by product code QTZ), their training ground truth would have been established through methods appropriate for the specific task (e.g., expert annotations for segmentation, simulated data for specific calculations), but this information is not provided.

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