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The FUJIFILM Surgical System includes a laparoscope, video processor and light source and is intended to be used with a monitor, hand instruments, electrosurgical unit, and other ancillary equipment for minimally invasive laparoscopic observation, diagnosis and treatment in general abdominal and gynecologic areas.

The FUJIFILM Surgical System is comprised of:

• . EL-580FN Video Laproscope with RC-4440FN Relay Connector
• . EPX-4440FN Digital Video Processor with light source
  • o VP-4440FN Video Processor
  • o XL-4450FN Light Source
  • o DK-4440E Data Keyboard

The EL-580FN Video Laparoscope is comprised of a rigid insertion portion, cable portion. video connector and light guide connector. An optical system, CCD image sensor and electrical circuits are located within the distal end portion of the laparoscope. The video signal lines from the CCD sensor and the light guide fiber bundles are connected to the video connector and light guide connector respectively through the laparoscope and video cables.

The EL-580FN Video Laparoscope is used with the EPX-4440FN Digital Video processor which consists of the VP-4440FN Processor and the XL-4450FN Light Source. The video connector is connected to the video processor via the RC-4440FN Relay Connector and the light connector is connected to the XL-4450FN Light Source. The VP-4440FN Video Processor relays the image from a laparoscope to a video monitor. The processor also controls the light projected to the body cavity. The XL-4450FN Light Source employs a Xenon lamp with an emergency back-up Halogen lamp. Brightness control is performed by the user.

The provided text describes the Fujifilm Surgical System, a laparoscope and video processor, and demonstrates its substantial equivalence to predicate devices. However, the document does not contain specific acceptance criteria or a detailed study report with performance metrics, sample sizes, expert qualifications, or ground truth establishment relevant to AI/ML device evaluation.

The information provided focuses on demonstrating safety, effectiveness, and performance through compliance with recognized consensus standards and general comparisons with predicate devices, which is typical for 510(k) clearances for non-AI medical devices.

Therefore, many of the requested elements for an AI/ML device study, such as specific performance metrics (e.g., sensitivity, specificity, AUC), sample sizes for test sets, data provenance, number of experts for ground truth, adjudication methods, MRMC studies, standalone performance, and training set details, cannot be extracted from this document.

Based on the provided text, here is what can be inferred and what cannot:

1. A table of acceptance criteria and the reported device performance

• Cannot be provided: The document does not list quantitative acceptance criteria in terms of specific performance metrics (e.g., sensitivity, specificity, accuracy) nor does it report specific numerical performance results against such criteria. The "acceptance criteria" mentioned are related to meeting recognized consensus standards for safety and performance (e.g., electrical safety, EMC, biocompatibility) rather than performance characteristics of an AI algorithm. The statement "met all the acceptance criteria" refers to these general safety and performance standards.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Cannot be provided: The document does not describe a "test set" in the context of an AI/ML study. It discusses "performance testing" to demonstrate compliance with standards and compare technological characteristics with predicate devices. No sample size, data provenance, or retrospective/prospective nature of data for a clinical performance evaluation is mentioned.

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)

• Cannot be provided: Since there is no "test set" or explicit ground truth establishment process described for an AI/ML algorithm, information about experts or their qualifications is absent.

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

• Cannot be provided: No adjudication method is mentioned as there is no specific clinical performance test set described.

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, an MRMC study was not done (or at least not described): The document does not mention any MRMC study or any evaluation of human reader performance with or without AI assistance. This device is not described as an AI-powered diagnostic or assistive tool.

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

• Cannot be provided: The device is a surgical system (laparoscope and video processor), not an AI algorithm designed to operate in standalone mode.

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

• Cannot be provided: No ground truth in the context of an AI/ML algorithm's output is mentioned. The ground truth for this device would relate to engineering specifications and performance against physical standards (e.g., resolution, field of view, safely) rather than clinical diagnostic accuracy.

8. The sample size for the training set

• Cannot be provided: This document does not describe an AI/ML algorithm that would require a training set.

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

• Cannot be provided: As above, no training set for an AI/ML algorithm is described.

Summary based on the document:

This document is a 510(k) summary for a non-AI medical device: the Fujifilm Surgical System (laparoscope and video processor). The primary method of demonstrating substantial equivalence relies on:

• Comparing its intended use, principles of operation, and technological characteristics to established predicate devices.
• Compliance with recognized consensus standards (e.g., electrical safety, biocompatibility, endoscopic equipment standards, risk management).

The "performance data" section primarily lists the standards the device complies with and generally states that the device "met all the acceptance criteria" in terms of technological characteristics compared to the predicate. This is typical for a traditional medical device submission where the focus is on engineering performance, safety, and equivalence rather than the diagnostic or predictive performance of an AI/ML algorithm.

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