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The Disposable Trocars have application in a variety of endoscopic procedures to provide a port of entry for endoscopic instruments and/or endoscope.

The Disposable Trocars includes Normal trocars (model A), Balloon trocars (model B), Thread trocars (model C) and Single-port trocars (model E). They are generally composed of cannula and obturator, with or without filter/cleaning ring/protector. The Disposable Trocars have application in a variety of endoscopic procedures to provide a port of entry for endoscopic instruments and/or endoscope. If needed, the CO2 can be infused through the valve. The product is sterilized by Ethylene oxide and is a single-use product.

The provided text is a 510(k) summary for a medical device (Disposable Trocars) seeking FDA clearance. It outlines the substantial equivalence to predicate devices based on various characteristics and non-clinical testing. However, it does not contain information about a study proving the device meets acceptance criteria related to AI/algorithm performance, human reader improvement with AI assistance, or the establishment of ground truth by expert consensus.

The document primarily focuses on bench testing and material biocompatibility for traditional medical device components, not software or AI performance. The details requested in the prompt, such as sample sizes for test sets, data provenance for AI studies, number of experts for ground truth, adjudication methods, MRMC studies, standalone algorithm performance, and training set details, are not present in this type of FDA submission for a device like a trocar.

Therefore, I cannot provide the detailed information requested in the prompt based on the given text. The text describes the testing of the physical device's performance properties (e.g., obturator compatibility, air leakage, balloon rigidity) and biocompatibility, not the performance of an AI or algorithmic component.

If the request refers to the performance criteria for the physical device itself (the trocars), then I can extract some relevant information, though it won't align perfectly with the AI/algorithm-focused questions in the prompt.

Based on the provided text, and assuming "device performance" refers to the physical attributes and functionalities of the Disposable Trocars, here's what can be extracted, acknowledging that many of your specific questions are not addressed by this type of document:

The Disposable Trocars are physical medical instruments used in endoscopic procedures. The "acceptance criteria" and "device performance" discussed in this document relate to their physical and biological properties, not AI or algorithmic performance.

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

The document states that "All the test results were 'PASS'." This implies that the device met pre-defined acceptance criteria for each test. However, the specific quantitative acceptance criteria values are not explicitly listed, only the type of tests performed.

2. Sample sized used for the test set and the data provenance:

• Sample Size: Not specified in the provided text. The document mentions "performance studies and bench testing" but does not give the number of units tested.
• Data Provenance: Not applicable in the context of clinical data for AI. The tests are bench tests performed by the manufacturer, Hangzhou Kangji Medical Instrument Co., Ltd., which is based in China. The data would be prospective in the sense that the company specifically conducted these tests for the submission.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable. This document describes testing of a physical medical device. Ground truth as understood in AI/image analysis (e.g., expert radiologist consensus) is not relevant here. The ground truth for these tests would be established by validated test methods and measurement standards for physical properties. The tests were evaluated by their "Design Engineer."

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

• Not applicable. This is for AI/image analysis. The "adjudication" for these physical tests would be the measurement against a pre-defined specification or standard.

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. This is a physical medical device, not an AI or software device that assists human readers.

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

• No. This is a physical medical device.

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

• For biocompatibility: ISO and USP standards (e.g., cell cultures for cytotoxicity, animal models for systemic toxicity).
• For performance: Engineering specifications and test methods for physical properties (e.g., force measurements for insertion, pressure measurements for air leakage).

8. The sample size for the training set:

• Not applicable. This is a physical medical device, not an AI model requiring a training set.

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

• Not applicable.

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The Disposable Veress Needles are intended for percutaneous insertion into the peritoneal cavity for the purpose of insufflation with carbon dioxide to establish pneumoperitoneum prior to the placement of trocars opic procedures.

The Disposable Veress Needles are composed of needle, inner core, handle and made of medical grade stainless steel, PC, PE, ABS, and Model II has a silicone sleeve on the handle. The device is packed in Tyvek dialysis bag with 4058B Tyvek dialysis paper and ESE film. The contact duration is less than 24 hours. The device is intended for percutaneous insertion into the peritoneal cavity for the purpose of insufflation with carbon dioxide to establish pneumoperitoneum prior to the placement of trocars during laparoscopic procedures. The device is sterilized by EO and is intended for single-use only. The proposed device is available in a variety of needle lengths. There are two models for Disposable Veress needles: I, II. The difference between the two models is that the model II has a blue silicone sleeve outside the handle.

The provided text describes a 510(k) premarket notification for a medical device called "Disposable Veress Needles." This document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study proving that the device meets specific acceptance criteria in the context of an AI/ML algorithm.

Therefore, the requested information about acceptance criteria and a study proving device performance (especially points relevant to AI/ML such as test set size, data provenance, expert ground truth adjudication, MRMC studies, standalone performance, training set details, and ground truth establishment) cannot be extracted from this document, as it pertains to a different type of device and regulatory submission.

The document primarily discusses non-clinical testing for physical properties, biocompatibility, sterilization, and shelf-life of a physical medical instrument (a Veress needle), not an AI/ML diagnostic or predictive tool.

Here's a breakdown of what can be extracted, and where the requested information is absent:

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

• Acceptance Criteria: The document states, "The tests listed below have demonstrated that the subject device performs as well as the predicate device based on the acceptance criteria." However, the specific numerical or qualitative acceptance criteria (e.g., "puncture force must be within X to Y Newtons") are not explicitly detailed in the provided text. Instead, categories of tests are listed.
• Reported Device Performance: The document only states that the device "performs as well as the predicate device" after these tests. No specific performance results (e.g., actual measured tip pull force, switch operation timing, etc.) are provided.

Regarding points 2-9, which are highly relevant for AI/ML device evaluations, the information is not present in this document because it describes a physical medical device, not an AI/ML algorithm.

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

• Not applicable / Not provided. This document does not describe a study involving data sets for an AI/ML algorithm. The "test set" here refers to physical units of the device subjected to non-clinical tests. The number of samples for each physical test (e.g., how many needles were tested for tip pull) is not specified.

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 / Not provided. Ground truth definition by experts is not relevant for the non-clinical physical and biological tests described for a Veress needle.

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

• Not applicable / Not provided. Adjudication methods are relevant for expert consensus on AI/ML ground truth, not for physical device testing.

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 / Not provided. This is explicitly for AI-assisted diagnostic devices and does not apply to a standalone physical surgical instrument like a Veress needle.

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

• Not applicable / Not provided. This is for AI algorithms, not a physical medical device.

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

• Not applicable / Not provided. The "ground truth" for the physical device tests would be objective measurements against an engineering standard or predicate device performance, not expert consensus on medical findings.

8. The sample size for the training set

• Not applicable / Not provided. There is no AI/ML algorithm, and thus no training set.

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

• Not applicable / Not provided. There is no AI/ML algorithm, and thus no training set or ground truth establishment method for it.

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