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The Disposable Endoscopic Cutter Stapler and Cartridge has applications in abdominal, gynecologic, pediatric and thoracic surgery for resection and creation of anastomosis. The Disposable Endoscopic Cutter Stapler and Cartridge is not to be used in the transection and resection of solid organs.

Disposable Endoscopic Cutter Stapler and Cartridge place two, triple-staggered rows of progressive titanium staples and simultaneously divides the tissue from central line. The devices are available in 30mm, 45mm and 60mm three lengths with cartridge in 2.0/2.5/3.0, 3.0/3.5/4.0, 4.0/4.5/5.0 three progressive staple sizes to accommodate carious tissue thickness. The device may be reloaded and fired up to 12 times in a single procedure.

The provided document is a 510(k) summary for a medical device (Disposable Endoscopic Cutter Stapler and Cartridge), not a report for an AI/ML-based medical device. Therefore, the information required to answer your specific questions about acceptance criteria, study details, expert involvement, and ground truth for an AI device is NOT present in this document.

The document demonstrates the substantial equivalence of the new stapler to a predicate device through:

• Non-clinical tests: These include biocompatibility testing (ISO 10993 series, USP standards), sterilization validation (ISO 11135), and bench tests on porcine tissue (Pressure Resistance, Closed Staple Height, Staple Formation, Force Required to Fire Stapler).
• Comparison table (Table 1): This table directly compares various characteristics of the proposed device and the predicate device, often stating "SE" (Substantially Equivalent) if they are sufficiently similar or providing a qualitative "SE Analysis" if there are differences.

There is no mention of:

• AI/ML algorithms.
• Data sets for training or testing AI models.
• Human readers, expert consensus, or specific ground truth methodologies for evaluating an AI system.
• MRMC studies or effect sizes for human improvement with AI assistance.

Therefore, I cannot populate the requested table and answer the specific questions related to AI/ML device validation based on this document.

The document specifically states under "7. Clinical Test Conclusion": "No clinical study is included in this submission." This further confirms the absence of the type of information you are seeking regarding device performance in a clinical or reader study context for an AI device.

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• Simple, compound first- and second-degree tibial shaft fractures
• Pseudarthrosis and delayed union

The WASTON Metallic Intramedullary Nail System is a temporary fixation intramedullary nail designed for fracture fixation and stabilization of the tibia. The system consists of intramedullary nail, locking screw, end cap and instruments.

The intramedullary nail is available in a variety of lengths to meet assorted anatomical needs. Each of the nails is secured by a series of screws that pass through holes manufactured into the proximal and distal sections.

All implants of Metallic Intramedullary Nail System are manufactured from Ti-6Al-4V alloy that meets the requirements of ASTM F-136.

The proposed devices are provided un-sterilized, but shall be sterilized via autoclave method to achieve Sterility Assurance Level of 10-6 by hospital prior to use.

This document describes a 510(k) premarket notification for the "WASTON Metallic Intramedullary Nail System." It is a medical device submission, and therefore, the acceptance criteria and study data provided are related to the substantial equivalence of the proposed device to a predicate device, rather than a standalone clinical AI or diagnostic performance study.

Here's an analysis of the provided information:

1. Table of Acceptance Criteria and Reported Device Performance

For medical devices seeking 510(k) clearance, the "acceptance criteria" isn't typically phrased as a specific numerical performance target, but rather as demonstrating substantial equivalence (SE) to a legally marketed predicate device. This is achieved by showing that the new device has the same intended use and technological characteristics as the predicate, or if there are differences, that those differences do not raise new questions of safety and effectiveness.

The document presents a comparison between the proposed device and the predicate device (K132078) to demonstrate this substantial equivalence.

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

This document explicitly states: "No clinical study is included in this submission."

Therefore, there is no "test set" in the context of clinical or diagnostic performance data. The "tests" mentioned are non-clinical (mechanical, material, sterilization) and are typically performed on samples of the device components (e.g., a certain number of nails, screws). The sample sizes for these non-clinical tests are not detailed in this summary. The data provenance would be from the manufacturing and testing facilities of Changzhou Waston Medical Appliance Co., Ltd. in China.

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. As stated, no clinical study or ground truth establishment based on expert review was performed or included in this 510(k) submission.

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

Not applicable. No clinical test set requiring adjudication was used.

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 metallic intramedullary nail system for fracture fixation, not an AI or diagnostic imaging device involving human readers.

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

Not applicable. This device is a physical implant, not an algorithm.

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

Not applicable for clinical ground truth. For non-clinical tests, the "ground truth" would be established by reference to international consensus standards (e.g., ASTM, ISO) for material properties and mechanical performance.

8. The sample size for the training set

Not applicable. This is not an AI or machine learning device requiring a training set.

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

Not applicable. This is not an AI or machine learning device.

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