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The Insufflator (OPTO-IFL1000) is intended to generate and maintain pneumoperitoneum by filling the abdominal cavity with gas to distend it during diagnostic or therapeutic laparoscopic procedures.

Insufflator (OPTO-IFL1000) is a CO2 insufflation device for creating and maintaining a pneumoperitoneum during laparoscopic examinations and operations. It is capable of establishing the surgical field of view and operating space. CO2 gas can be injected into abdominal cavity by the device, and the gas separates the abdominal wall from the internal organs of the abdominal cavity, forming a space for the operation and visual field. The device is to be used with the following insufflation tubes:

• 1. OPTO-T1000H (with heating function)
• 2. OPTO-T1000 (without heating function)

The provided document is a 510(k) summary for the Insufflator (OPTO-IFL1000) from Guangdong OptoMedic Technologies, Inc. It describes the device, its indications for use, and a comparison to a predicate device. However, it does not contain the detailed information required to answer many of the specific questions about the device's acceptance criteria and the study proving it meets those criteria, particularly for an AI/ML-based medical device.

The document states: "Performance testing were also conducted and demonstrate that the proposed system performs according to specifications and functions as intended. And the test result shows that the preset acceptance criteria are met." It then lists several performance tests, but it does not specify what those "preset acceptance criteria" are numerically, nor does it provide the reported device performance values against these criteria. Furthermore, it does not describe a study involving "human readers" or "AI assistance," as this is an insufflator, not an imaging analysis AI.

Therefore, many of the questions cannot be answered from the provided text. I will answer what is available and indicate where information is missing.

Acceptance Criteria and Study for Insufflator (OPTO-IFL1000)

Based on the provided document, the device is an Insufflator (OPTO-IFL1000), which is a physical medical device designed to create and maintain pneumoperitoneum during laparoscopic procedures, not an AI/ML-based diagnostic or imaging device. Therefore, questions related to AI performance, human readers, ground truth establishment by experts, and training/test set sample sizes for AI models are not applicable to the information presented for this specific device.

The document discusses "Performance data" and lists various tests conducted to verify the device met all design specifications and is substantially equivalent to a predicate device. The "acceptance criteria" are generally implied to be satisfaction of these test specifications and compliance with relevant standards.

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

The document lists performance tests but does not provide a table with specific numerical acceptance criteria or the numerically reported device performance for these tests. It only states that "the preset acceptance criteria are met."

Below is a table of the performance tests mentioned, with the understanding that specific numerical criteria and results are not detailed in the provided text.

2. Sample size used for the test set and the data provenance

For a physical device like an insufflator, "test set" and "data provenance" (country of origin, retrospective/prospective) are typically not relevant in the same way they are for AI/ML models using patient data. The "test set" would refer to the specific Insufflator (OPTO-IFL1000) unit(s) used for the described performance and safety testing. The document does not specify the number of units tested. The provenance of the testing itself is implied to be conducted by the manufacturer, Guangdong OptoMedic Technologies, Inc., in China.

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

This question is applicable to AI/ML devices that interpret data (e.g., medical images) where expert consensus is used to establish ground truth. For a physical device like an insufflator, "ground truth" pertains to its functional performance characteristics, which are measured using validated test methods and equipment, not by human expert interpretation of device output in the same way. Therefore, this information is not applicable and not provided.

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

Not applicable for a physical device performance test.

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 an insufflator, not an AI-assisted diagnostic tool for human readers.

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

Not applicable. This device is not an algorithm. Its performance is inherent to its physical and software functionality.

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

For this physical device, "ground truth" would be established by:

• Compliance with recognized standards: Such as ANSI/AAMI ES 60601-1, IEC 60601-1-2, IEC 60601-1-8, AAMI TIR 30, AAMI TIR 12.
• Engineering specifications and measurements: Direct measurement of pressure, flow, temperature, alarm triggers, etc., against predefined engineering tolerances and clinical requirements.
• Software verification and validation: Testing to ensure the software functions as intended to control the device and its safety features.

The document states that "The software verification and validation testing were conducted and the test results demonstrated the software function met the requirements. The software for this device was considered a 'Major' level of concern." This indicates that software functionality, which dictates much of the device's "ground truth" operation, was rigorously tested.

8. The sample size for the training set

Not applicable. This is not an AI/ML device that requires a training set of data.

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

Not applicable.

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