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The PENTAX Medical ED-3490TK Video Duodenoscope is intended to provide optical visualization (via a video monitor) of, and therapeutic access to the Biliary Tract via the Upper GI Tract. This anatomy includes, but is not restricted to, the organs; tissues; and subsystems: Esophagus, Stomach, Common Bile, Hepatic, and Cystic Ducts. This instrument is introduced via the mouth when indications consistent with the need for procedure are observed in adult and pediatric populations.

The PENTAX Medical ED-3490TK Video Duodenoscope is used with a PENTAX compatible Video Processor (software controlled device). The duodenoscope has a flexible insertion tube, a control body and umbilicus The umbilicus provides connection to the video processor. The control body includes controls for up/down/left/right angulation, air/water delivery, suction and an accessory inlet port. The devices contain light carrying bundles to illuminate the body cavity and a charge couple device (CCD) to collect image data. The instruments contain a working channel through which biopsy devices or other devices may be introduced.

This appears to be an FDA 510(k) clearance letter and summary for a medical device, specifically a duodenoscope. The core of this submission is not about an AI/ML-driven device's performance in terms of diagnostic accuracy or effect on human readers, but rather about an updated version of an existing device, primarily focusing on revisions to its reprocessing instructions for use (RIFU) based on human factors testing.

Therefore, many of the standard questions regarding AI/ML device performance (like MRMC studies, standalone algorithm performance, training set details, ground truth establishment for diagnostic tasks) are not applicable to this document. The "device performance" here refers to the device's physical and functional characteristics, and the "study" is a human factors study on the RIFU, not a clinical trial assessing medical outcomes or AI diagnostic accuracy.

Given the document provided, here's an attempt to address the relevant points and explain why others are not applicable:

Acceptance Criteria and Study for PENTAX Medical ED-3490TK Video Duodenoscope (K192280)

This 510(k) submission primarily focuses on improved "Reprocessing Instructions for Use (RIFU)" for the PENTAX Medical ED-3490TK Video Duodenoscope, not on the diagnostic/imaging performance of an AI/ML component. The "study" mentioned is a human factors study to ensure the RIFU are clear and lead to proper reprocessing, thereby enhancing safety and effectiveness in that aspect.

1. Table of Acceptance Criteria and Reported Device Performance

For this specific submission, the "performance" is related to the usability and clarity of the Reprocessing Instructions for Use (RIFU), assessed through human factors testing.

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

The document states a "Human Factors study" was conducted to revise the RIFU.

• Sample Size: The specific sample size for the human factors test set is not provided in this 510(k) summary. Human factors studies typically involve a defined number of representative users.
• Data Provenance: The data provenance (e.g., country of origin, retrospective/prospective) is not specified in this summary. Human factors studies are inherently prospective, as they involve users interacting with the instructions and device in a controlled setting.

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

This question is not applicable in the traditional sense of diagnostic ground truth for an AI model.

The "ground truth" here would be the correct/intended reprocessing steps. The human factors study evaluates whether users can correctly follow those steps based on the RIFU. This involves observing user performance. The "experts" would be the designers of the RIFU and the human factors specialists who designed and analyzed the study, not annotators for image data. The document does not specify the number or qualifications of these internal experts.

4. Adjudication Method for the Test Set

This question is not applicable as there is no diagnostic "ground truth" being adjudicated between multiple human readers or an AI. The human factors study involved evaluating user performance based on whether they correctly followed the revised RIFU. The evaluation method would be specified in the human factors study protocol but is not detailed here.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and Effect Size of AI vs. Without AI Assistance

Not applicable. This submission is for a physical medical device (duodenoscope) and revised instructions for its reprocessing, not for an Artificial Intelligence (AI) or Machine Learning (ML) driven diagnostic or assistive device. Therefore, no MRMC study comparing human readers with and without AI assistance was performed.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) was done

Not applicable. See point 5. There is no standalone algorithm being evaluated.

7. The Type of Ground Truth Used

The "ground truth" in the context of this human factors study is the correct and complete set of reprocessing steps as defined by the manufacturer and relevant standards. The study sought to confirm that the revised RIFU enabled users to achieve this "ground truth" through their actions. It is not an expert consensus, pathology, or outcomes data in the medical diagnostic sense.

8. The Sample Size for the Training Set

Not applicable. This is not an AI/ML device where a "training set" of data is used to train an algorithm. The "training" here refers to the user's ability to learn and follow the instructions, which is what the human factors study assesses.

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

Not applicable. See point 8.

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