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Ureteral Stents (AF-D series) are used for temporary internal drainage from the ureteropelvic junction to the bladder. Ureteral Stents have been used to relieve obstruction in a variety of benign, malignant, and post-traumatic conditions. The stents may be placed using endoscopic, percutaneous, or open surgical techniques. The indwelling time should not exceed 30 days.

Ureteral Stents (AF-D series) are a set of ureteral stents used for temporary internal drainage from the ureteropelvic junction to the bladder. The subject ureteral stent is a flexible, tubular double pigtail stent composed of thermoplastic polyurethane with hydrophilic coating. Depending on configurations, the device may include a ureteral stent only, or a stent with an introducer and a clamp, or a stent with an introducer, a guidewire and a clamp.
Ureteral Stents are available in 4.0 to 7.0 French (Fr) diameter, with lengths ranging from 8.0 to 28.0 centimeters (cm). The device is supplied sterile, intended for single use only, and is available for prescription use only.
Ureteral Stents are not intended as a permanent indwelling device. The subject stent is labeled for indwell time not to exceed thirty (30) days only.

The provided FDA 510(k) clearance letter and summary for the Ureteral Stents (AF-D series) do not describe a study involving an AI/Machine Learning device or software. The information focuses on the substantial equivalence of a physical medical device (Ureteral Stents) to a predicate device based on material properties, design, and physical performance characteristics.

Therefore, it is impossible to extract the requested information (acceptance criteria, details of a study proving the device meets criteria, sample sizes, expert involvement, ground truth, MRMC study, training sets) as these pertain specifically to the validation of AI/ML models, not traditional medical devices like ureteral stents.

The document covers:

• Acceptance Criteria (Implicitly): The acceptance criteria are implicitly that the device performs as safely and effectively as the predicate device across various bench tests and biocompatibility assessments.
• Study Proving Acceptance (Bench Testing): The document states: "A battery of bench testing based on the FDA guidance 'Guidance for the Content of Premarket Notification for Ureteral Stents' (1993) was conducted on the subject, predicate and reference stents using established methods and standards to demonstrate the performance substantial equivalence to the predicate device."
  • Tests listed: Appearance and Dimensions, Curl Retention Test (pigtails), Break Strength & Elongation, Flow Rate, Dynamic Friction Force, Kink Stability Test, Simulated Stent Insertion and Removal Test, Chemical Performance, Guidewire: Radiopacity, bending test, compatibility with stent and introducer.
  • Biocompatibility Testing: Cytotoxicity, Sensitization, Intracutaneous Reactivity, Acute Systemic Toxicity, Material-Mediated Pyrogenicity, Subacute Systemic Toxicity, Muscle Implantation, Genotoxicity.
  • Sterility and Shelf Life: SAL of 10⁻⁶ (ISO 11135:2014), packaging integrity, accelerated aging study, and simulated transportation study.
• Sample Size: The document does not specify the sample size for the physical device testing. It refers to "representative device models" for shelf life testing and "a battery of bench testing," which implies multiple units were tested but no specific number is given.
• Data Provenance: Not applicable in the context of AI/ML. The "data" here refers to physical test results from the manufactured stents.
• Experts and Ground Truth: Not applicable in the context of clinical AI/ML validation involving human readers. For physical device testing, adherence to established standards and methods serves as the "ground truth."
• MRMC, Standalone Performance, Training Set details: These are all concepts relevant to AI/ML device validation and are not discussed as this is a physical medical device.

In summary, the provided document describes the regulatory clearance of a physical medical device (Ureteral Stents) through a substantial equivalence pathway, not an AI/Machine Learning device. Therefore, the specific questions related to AI/ML validation cannot be answered from this document.

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