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The da Vinci SP Firefly Imaging System is intended to provide real-time endoscopic visible and near-infrared fluorescence imaging. The da Vinci SP Firefly Imaging System enables surgeons to perform minimally invasive surgery using standard endoscopic visible light as well as visual assessment of vessels, blood flow and related tissue perfusion, using near-infrared imaging.

The da Vinci SP Firefly Imaging System consists of a near-infrared laser light source located in the Endoscope Controller and a connected endoscope. The Firefly imaging system uses near-infrared light in conjunction with the imaging agent Indocyanine Green (ICG), to create fluorescent images of tissue. ICG is administered to the patient according to its manufacturer's labeling, and the system is switched to Firefly imaging. Two modes are available in Firefly imaging: Standard mode and Sensitive mode. When Firefly is active in Standard mode, the system displays the resulting images as a fluorescent (green) overlay on a gray-scale background image. In Sensitive mode, the gray-scale background is no longer illuminated, resulting in increased sensitivity to fluorescent signal.

This FDA 510(k) clearance letter pertains to the da Vinci SP Firefly Imaging System, focusing on a new "Sensitive Firefly" imaging mode.

Based on the provided document, here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state specific quantitative acceptance criteria for the device's performance, nor does it provide a table of reported device performance metrics against such criteria. It generally states that "Verification and validation testing on the subject device confirmed that no issues of safety or effectiveness, analogous to the results of the predicate device verification and validation testing." This implies that the new mode performs comparably to the predicate device, but specific metrics are not detailed.

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

The document states "clinical validation testing with an animal model" was performed.

• Sample Size: Not specified.
• Data Provenance: Animal model; implies prospective (testing performed specifically for this submission). The country of origin is not specified.

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

Not specified in the document.

4. Adjudication Method for the Test Set

Not specified in the document.

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

The document does not mention a Multi-Reader Multi-Case (MRMC) comparative effectiveness study, nor does it quantify any improvement for human readers with AI assistance. The device description is for an imaging system, not an AI-assisted diagnostic tool in the typical sense that would involve human reader performance comparisons.

6. Standalone (i.e., algorithm only without human-in-the-loop performance) Study

The device is an imaging system designed for surgeons to visualize tissues in real-time. It's not an "algorithm only" device in a standalone diagnostic capacity. The testing described (bench and animal model) supports its functionality as an imaging tool to be used by a surgeon.

7. Type of Ground Truth Used

The document mentions "clinical validation testing with an animal model" to confirm "no issues of safety or effectiveness." This suggests the ground truth was likely based on observed physiological responses in the animal model, evaluating whether the system accurately visualized vessels, blood flow, and tissue perfusion using ICG, as intended. However, the specific method for establishing this ground truth (e.g., direct observation, histopathology) is not detailed.

8. Sample Size for the Training Set

The document does not explicitly mention a "training set" or its sample size. Given the description, the device appears to be an imaging hardware system with a new mode, rather than a machine learning model that would typically have a distinct training phase with a labeled dataset. The verification and validation testing focus on confirming the functionality of the imaging system itself.

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

As no training set is mentioned for an algorithm, the method for establishing its ground truth is not applicable or described in this document.

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