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The Uro-G HD Flexible Cystoscope is intended for endoscopic diagnosis and infusion of irrigating fluids within the bladder and urethra.

Generally recognized indications for cystoscopy include:
• Symptomatic voiding dysfunction
• Hematuria
• Bladder tumor surveillance
• Recurrent lower urinary tract infections
• Pelvic pain syndromes

The Uro-G HD Flexible Cystoscope System is a handheld, battery-operated portable cystoscope consisting of a disposable steerable endoscopic cannula with a 2.2 mm channel for fluid infusion and instrument advancement, and a reusable handle with a video monitor. The fully assembled Uro-G HD Cystoscope System—cannula and handle—has an overall length of 630 mm (approximately 25 inches). The disposable cannula's working length is 380.2 mm (approx. 15 inches) and a total length of 406 mm, from tip to connector. The cannula's maximum width is 5.6 mm and it has a connector for attaching and detaching to the handle.

This document is a 510(k) Premarket Notification for the Uro-G HD Flexible Cystoscope, seeking to prove substantial equivalence to a predicate device (Uro-G Flexible Cystoscope, K202921). As such, the "acceptance criteria" and "study that proves the device meets the acceptance criteria" refer to the demonstration that the modified device (Uro-G HD) is as safe and effective as the predicate device, not necessarily a performance study against a specific clinical endpoint. The primary method for proving this is through comparative testing and analysis of technological characteristics.

Here's an analysis of the provided text in the context of acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly stated as quantitative targets in a table format within the provided text. Instead, the document uses a comparative approach against a predicate device. The "reported device performance" is essentially the Uro-G HD's characteristics, and "meeting acceptance criteria" means demonstrating that these characteristics are equivalent to or improve upon the predicate without introducing new risks.

Here’s a table summarizing the key characteristics and the comparison to the predicate device, highlighting what could be implicitly considered an "acceptance criterion" (i.e., equivalence or improvement):

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

The document primarily describes bench testing and comparative analysis of the device's technological characteristics. It does not refer to a "test set" in the sense of a dataset of clinical cases for an AI algorithm. The performance data listed (e.g., measurement of outer diameter, image quality, image distortion) are conducted on the device hardware itself.

• Sample Size for Test Set: Not applicable in the context of an AI/algorithm-based "test set." The testing involved physical devices and their components. The number of physical units tested is not specified but is typically determined by manufacturing quality control and regulatory submission requirements for non-AI devices.
• Data Provenance (Country of Origin, Retrospective/Prospective): Not applicable for the type of testing described (bench testing of a medical device). This is a hardware device submission, not an AI/software submission that relies on clinical image data.

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

• Number of Experts: Not applicable. Ground truth, in the context of diagnostic performance typically associated with AI/algorithm submissions, is not directly established through expert consensus on clinical cases for this device. The "ground truth" for this device's performance data relates to engineering specifications and physical measurements (e.g., true dimensions, known test chart patterns).
• Qualifications of Experts: Not applicable beyond the implicit expertise of engineers and technicians performing the stated bench tests, and the FDA's regulatory reviewers.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. There is no clinical "test set" requiring adjudication by multiple readers.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and its Effect Size.

• MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study was not performed. This device is a hardware cystoscope, not an AI-assisted diagnostic tool for which such studies are typically conducted to evaluate reader performance with and without AI assistance.
• Effect Size of Human Readers Improvement: Not applicable, as no MRMC study was conducted.

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

• Standalone Performance: Not applicable. This is a hardware device. While it contains software, the "software verification testing" mentioned is to ensure the software properly controls the device's functions (e.g., displaying HD images, recording). There is no standalone algorithm whose diagnostic performance is being evaluated independent of human use.

7. The Type of Ground Truth Used

The "ground truth" for this submission is based on:

• Engineering Specifications: The design parameters and expected performance of the device (e.g., specified focal length, field of view, angulation range, lumen cross-section, electrical safety standards).
• Physical Measurements: Direct measurements of the device's components against design specifications (e.g., outer diameter, image quality against a known test chart, image distortion against a known pattern).
• Biocompatibility Standards: Compliance with ISO 10993 requirements, verified through specific biocompatibility tests (Cytotoxicity, Irritation, Sensitization, Systemic Toxicity).
• Predicate Device Performance: The established performance and safety characteristics of the legally marketed predicate device (Uro-G Flexible Cystoscope, K202921) serve as the primary reference for demonstrating equivalence.

8. The Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. This submission is for a hardware medical device with embedded software controlling its functions, not an AI/machine learning algorithm trained on a "training set" of data.

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

• Ground Truth for Training Set: Not applicable, as there is no training set for an AI/ML model for this hardware device.

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