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The EPX-4440FN Digital Video Processor with Light Source is used for endoscopic or laparoscopic observation, diagnosis, treatment, and image recording. It is intended to process electronic signals transmitted from a video endoscope or laparoscope (a video camera in an endoscope or laparoscope). This product may be used on all patients requiring endoscopic examination or laparoscopic surgical procedures and when using a FUJIFILM medical endoscope or laparoscope, light source, monitor, recorder and various peripheral devise. FICE (Flexible spectral-Imaging Color Enhancement) is an adjunctive tool for gastrointestinal endoscopic examination which can be used to supplement FUJIFILM white light endoscopy. FICE is not intended to replace histopathologic sampling as a means of diagnosis.

The EPX-4440FN Digital Video Processor with Light Source is comprised of:

• o VP-4440FN Video Processor with DK-4440E Data Keyboard
• o XL-4450FN Light Source
  The EPX-4440FN Digital Video Processor with Light Source consists of the VP-4440FN Video Processor and the XL-4450FN Light Source. The VP-4440FN Video Processor relays the image from a laparoscope or endoscope 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 is a 510(k) premarket notification for a medical device called the EPX-4440FN Digital Video Processor with Light Source. It does not include acceptance criteria for device performance in the form of specific metrics (e.g., sensitivity, specificity, accuracy for a diagnostic task), nor does it describe a study proving the device meets particular clinical or diagnostic performance criteria.

Instead, this document focuses on demonstrating substantial equivalence to predicate devices based on intended use, technological characteristics, and compliance with recognized consensus standards for safety and essential performance. The "Performance Data" section describes engineering-level bench testing to ensure the device's basic functionality, safety, and electromagnetic compatibility.

Therefore, I cannot fulfill the request as the necessary information (acceptance criteria for clinical performance and a study proving those criteria are met) is not present in the provided text.

The "Performance Data" section states:
"Additionally, performance testing showed that the EPX-4440FN Digital Video Processor with Light Source met all the acceptance criteria."

However, it does not list what those acceptance criteria were in terms of specific performance metrics relevant to an AI/diagnostic device (e.g., accuracy, sensitivity, specificity, positive predictive value, negative predictive value). It also doesn't describe any studies that would typically be associated with evaluating such criteria.

The studies mentioned are related to:

• Compliance with recognized consensus standards:
  • ANSI/AAMI ES60601-1:2005/(R)2012 and A1:2012 (Medical electrical equipment - General requirements for basic safety and essential performance)
  • IEC 60601-1-2:2007 (Electromagnetic compatibility)
  • IEC 60601-2-18 Edition 3.0 2009-08 (Particular requirements for endoscopic equipment)
  • ISO 14971:2007 (Medical Device Risk Management)
  • IEC 62471: 2006 (Photobiological safety of lamps and lamp systems)
  • AAMI ANSI IEC 62304:2006 (Medical device software - Software lifecycle processes)

These are primarily engineering and safety standards, not studies designed to assess clinical diagnostic performance or how an AI algorithm (if present as a core diagnostic component) performs against a clinical ground truth.

Regarding your numbered points, based on the provided text:

1. A table of acceptance criteria and the reported device performance: Not present. The document states "met all the acceptance criteria" but does not define these criteria or report specific performance values.
2. Sample size used for the test set and the data provenance: Not applicable, as no clinical performance study involving a test set is described. The "performance testing" referenced is likely bench testing for engineering compliance.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable, as no clinical performance study involving ground truth establishment is described.
4. Adjudication method for the test set: Not applicable.
5. If a multi reader multi case (MRMC) comparative effectiveness study was done: No. The device is a "Digital Video Processor with Light Source" and its FICE function is described as an "adjunctive tool... not intended to replace histopathologic sampling as a means of diagnosis." This indicates it's not a primary AI-based diagnostic tool requiring MRMC studies for comparative effectiveness of human readers with/without AI assistance.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: No, this device is not presented as a standalone diagnostic algorithm.
7. The type of ground truth used: Not applicable.
8. The sample size for the training set: Not applicable, as this device's description does not suggest a machine learning model that would require a "training set" in the sense of AI/diagnostic algorithm development.
9. How the ground truth for the training set was established: Not applicable.

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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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The InterLock Trocar has applications in abdominal, thoracic, and gynecological minimally invasive surgical procedures to establish a path of entry for endoscopic instruments. The trocar may be used with or without visualization for primary and secondary insertions.

The InterLock Trocar is comprised of a minimally invasive surgical trocar (outer sheath sleeve, inner trocar and optical obturator) and accessories (lens cleaning syringe and lens cleaning tube). There are two tubes with a luer lock connector attached to the InterLock Trocar. The tube connected to the Outer sheath sleeve is used for abdominal insufflation and the other attached to the Inner trocar is used for the objective lens cleaning function. The outer diameter of the insertion portion of the outer sheath is approximately 18.5 mm and the overall length of the trocar is approximately 180 mm. The trocar provides two instruments (a 3.8 mm laparoscope and a 5 mm standard, currently marketed hand instrument) with access to the abdominal cavity at the same time. The design of the trocar allows the laparoscope to be in direct alignment with and to follow the hand instrument. This feature provides a surgeon with self-visualization control, meaning that the camera is in direct alignment or parallel to the hand instrument. Therefore, the hand instrument is always in the field of view of the laparoscope. The laparoscope does not need to be held by a surgeon. The trocar holds the laparoscope and no additional surgeon or assistant is required to control operation of the laparoscope. The trocar also provides features for insufflating the peritoneal cavity and cleaning the 3.8 mm laparoscope lens without removing it from the trocar. The InterLock Trocar is for exclusive use with a 3.8 mm laparoscope.

This looks like a 510(k) summary for a medical device called "InterLock Trocar." It focuses on demonstrating substantial equivalence to a predicate device rather than presenting detailed acceptance criteria and a study that proves the device meets those criteria in the way a clinical trial or algorithm validation typically would.

Therefore, many of the requested categories (like sample size for test set, number of experts, adjudication method, MRMC studies, standalone performance, training set details) are not applicable or not provided in this type of regulatory document, as it's not an AI/algorithm-based device and the approval pathway is based on equivalence to an existing device.

Here's an attempt to extract the information based on your request, with significant clarification regarding what is and isn't present in the provided text:

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

The document does not explicitly state acceptance criteria with numerical targets in the same way an algorithm performance study would. Instead, performance is demonstrated through bench testing comparing the investigational device to a predicate device and design verification testing to ensure it meets its own product requirements. The criteria are implicit: the InterLock Trocar must perform comparably to the predicate device and meet its own design specifications.

• Retention force
• Insufflation fill rate
• Insufflation leak rate |
  | Cleaning Function Effectiveness (InterLock Specific) | Bench testing established that the lens cleaning technological characteristic is effective. |
  | Product Requirements & Design Inputs (InterLock Specific) | Design verification testing demonstrated that the InterLock Trocar predictably met all product requirements and design inputs when tested under nominal conditions. Testing included: - Physical measurements/tests
• Obturator performance
• Trocar performance
• Destructive tests |
  | Biocompatibility | All patient contacting materials meet applicable biocompatibility standards per ISO 10993-1:2009. |
  | Sterilization | Follows ANSI/AAMI/ISO 11137-2:2013 (VDmax25) to achieve a Sterility Assurance Level of 10^-6 for Gamma Irradiation Sterilization. |
  | Packaging & Shelf Life | - Packaging design qualified and validated per ISO 11607-1 to meet established performance requirements and specifications.
• Shelf life of 6 months substantiated by verifying product and package performance following aging, in accordance with ISO 11607-1. |

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

• Sample Size: Not specified. The document refers to "bench testing" and "design verification testing." This typically involves a defined number of units or prototypes tested to perform the specified mechanical, physical, and functional assessments. It is not patient data.
• Data Provenance: Not applicable in the context of patient data. The tests are laboratory-based, performed on manufactured devices.

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)

• Not Applicable. This is a physical medical device, not an AI or diagnostic imaging device that requires expert interpretation for ground truth. Verification is based on engineering and performance testing.

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

• Not Applicable. This is a physical medical device, not an AI or diagnostic imaging device that requires human interpretation or adjudication for ground truth.

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

• Not Applicable. This device is a surgical trocar, not an AI-assisted diagnostic tool.

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

• Not Applicable. This device is a physical surgical tool and does not have an "algorithm-only" mode.

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

• The "ground truth" for this device's performance would be defined by engineering specifications, international standards (e.g., ISO, ANSI/AAMI), and the performance characteristics of the predicate device. For example, a "ground truth" for insertion force would be a measurable force value, not an expert opinion.

8. The sample size for the training set

• Not Applicable. This device uses traditional engineering design and manufacturing, not machine learning or AI that requires a "training set."

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

• Not Applicable. See point 8.

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