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The ig4™ Image Guided System is a stereotactic accessory for Computed Tomography (CT) and endoscopic bronchoscope systems. The ig4 System is indicated for displaying:

• . An interventional instrument such as a biopsy needle, an aspiration needle, or ablation needle on a computer monitor that also displays a CT-based model of the target organ(s).
• . Images of the tracheobronchial tree to aid a physician in guiding endoscopic tools, catheters or guidewires, in the pulmonary tract.

The ig4™ System compensates for the patient's respiratory phases.

The ig4™ System is intended for use in clinical interventions and for anatomical structures where computed tomography and/or endoscopic bronchoscopy are currently used for visualizing such procedures.

The SPiN Drive® is an accessory for a CT system that utilizes electromagnetic tracking technology to locate and navigate endoscopic tools, catheters and guidewires relative to a CT-based model of the tracheobronchial tree. Due to system use to locate structures in soft tissue, the system incorporates a method of gating the location information on soft tissue to the patient's respiration. The SPiN Drive® consists of an EM tracking accessory, a patient referencing system, an EM field generator and tracking system, software, a computer system, and a pulmonary pathway reconstruction and planning workstation. The EM tracking accessories that can be used with the SPiN Drive® include Veran's Always-On Tip Tracked™ guidewire, steerable catheter, sheath, aspiration needle, brush or forceps.

Here's an analysis of the provided text regarding the SPiN Drive® device, outlining the acceptance criteria and study details based on the given information:

Note: The provided document is a 510(k) summary for a medical device which typically focuses on demonstrating substantial equivalence to a predicate device rather than providing extensive details about specific acceptance criteria and detailed clinical studies as would be found in a full clinical trial report or a performance study for a novel device. As such, some of the requested information (like specific numerical acceptance criteria, detailed test set characteristics, expert qualifications, effect size of MRMC studies, or training set ground truth establishment) is not present in this type of regulatory document.

1. Table of Acceptance Criteria and Reported Device Performance

Acceptance Criteria: The document primarily focuses on demonstrating substantial equivalence to a predicate device (ig4™ Endobronchial, K091934). The core acceptance criteria revolve around showing that the SPiN Drive® is "as safe and effective" as the predicate for its intended use. Specific, quantitative acceptance criteria for performance metrics are not explicitly stated in this summary.

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

• Test Set Sample Size: Not explicitly stated for any of the tests. The bench accuracy testing was on a "static phantom," and airway segmentation validation was performed, but the number of phantoms or datasets used is not specified.
• Data Provenance: Not specified. Given the nature of bench testing and phantom studies, it's likely lab-generated data rather than patient data from a specific country. The study is retrospective in the sense that the data used for bench testing would be pre-defined inputs for the system's performance evaluation.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.
• Adjudication Method: Not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified.

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

• MRMC Study Done? No, a multi-reader multi-case (MRMC) comparative effectiveness study was not stated to have been done. The document explicitly states: "Clinical tests were not required to demonstrate the safety and effectiveness of the device." This implies no studies involving human readers and clinical cases were performed for this submission.

6. Standalone (Algorithm Only) Performance Study

• Standalone Study Done? Yes, the performance tests described appear to be standalone algorithm/device performance studies:
  • "Bench accuracy testing was completed to demonstrate navigation accuracy on a static phantom."
  • "Airway segmentation validation was completed to demonstrate pulmonary planning function equivalence."
  • "Functional instrumentation testing was completed on new instrumentation."
    These types of tests evaluate the device's intrinsic performance against a known ground truth (e.g., precise phantom measurements, pre-segmented airway models) without human intervention in the loop.

7. Type of Ground Truth Used

• Type of Ground Truth:
  • For navigation accuracy: The ground truth was based on a "static phantom," implying known, precise locations or trajectories within the phantom that the device's navigation was compared against. This would likely be metrology-based or precisely engineered phantom features.
  • For airway segmentation validation: The ground truth for "pulmonary planning function equivalence" would likely be a precisely defined "ideal" segmentation of an airway model, possibly generated manually by experts or from high-resolution scans.

8. Sample Size for the Training Set

• Training Set Sample Size: Not specified. The document does not discuss the training of any machine learning models, as it predates the widespread regulatory focus on AI/ML. The "planning workstation" improvements are presented as incremental, suggesting refinement of existing algorithms rather than a new AI model requiring a dedicated training set.

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

• Ground Truth Establishment for Training Set: Not applicable/not specified. The document does not describe any machine learning components that would necessitate a dedicated training set with established ground truth. The technical changes are described as "incrementally improved planning workstation" and "additional navigated endoscopic instrumentation," which implies engineering improvements and functional validation rather than AI model training.

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