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The Intelligent Articulating Endoscopic Linear Cutters Reverse with Reloads have applications for general and endoscopic surgery in gastrointestinal, gynecological, general abdomihal and thoracic surgical procedures for resection, transection, and creation of anastomoses.

The Intelligent Articulating Endoscopic Linear Cutter Reverse with Reloads has identical technological features as the predicate device, i45/160 (K071708). The modification made to the device was that the pivot point for the jaws was translated distally, which enables the device to open in Reverse. Both the subject device and the predicate device deliver stapling/cutting action to create anastomoses.

The provided FDA 510(k) summary (K083527) is for a medical stapling device, the "Intelligent Articulating Endoscopic Linear Cutter Reverse with Reloads." This submission details modifications to an existing predicate device (K071708) and asserts substantial equivalence based on identical technological features except for a pivot point translation.

Based on the provided document, the typical detailed acceptance criteria and study data for a device with an AI component are not present. This is because the device described is a mechanical stapling device, not an AI/software-as-a-medical-device (SaMD) or AI-augmented device in the modern sense. The term "Intelligent" in its name likely refers to its design or features at the time of its 2008 submission, rather than AI capabilities for diagnosis, prediction, or data analysis.

Therefore, many of the requested points related to AI performance, ground truth, expert consensus, and comparative effectiveness studies are not applicable or cannot be extracted from this document, as they are typically associated with devices that use algorithms for analytical or interpretive functions.

However, I can still extract the information relevant to a traditional medical device submission, focusing on the device's functional equivalence and indications for use.

Acceptance Criteria and Device Performance (for a Mechanical Device)

For a device like this, the "acceptance criteria" and "performance" would typically revolve around mechanical tests, bench testing, and potentially animal or cadaver studies to demonstrate:

• Tissue compression force
• Staple formation consistency and strength
• Cutting performance
• Leakage rates (if relevant to the application)
• Durability and fatigue testing of components
• Biocompatibility of materials

The provided 510(k) summary does not contain specific numerical acceptance criteria or detailed study results for these types of mechanical performance, as is common in a 510(k) summary which focuses on demonstrating substantial equivalence rather than providing full study reports. The summary notes that the modified device has "identical technological features as the predicate device" (K071708) and "functions the same as the predicate device."

Table of Acceptance Criteria and Reported Device Performance (Conceptual, as details are not provided in the document):

Information (Not Applicable or Not Provided for an AI Device)

2. Sample size used for the test set and the data provenance: Not applicable/provided. This device is mechanical; performance is typically demonstrated through bench testing or animal studies, not a "test set" of patient data for an algorithm.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth as typically defined for AI models (e.g., expert radiological reads, pathology reports) is not relevant for this mechanical stapler.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.

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 is not an AI-assisted diagnostic or interpretive device.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable in the context of an AI device. For a mechanical device, "ground truth" might refer to physical measurements of staple line integrity or tissue compression, demonstrated through standard engineering tests.

8. The sample size for the training set: Not applicable. There is no AI model that requires a training set.

9. How the ground truth for the training set was established: Not applicable.

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