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The Rescue™ Pulmonary Grasping Forceps are intended to retrieve foreign body and/or excised tissue endoscopically within the upper airways and tracheobronchial tree.

The Rescue™ Pulmonary Grasping Forceps is sterile, single-use devices. The Rescue™ Pulmonary Grasping Forceps can retrieve foreign body and/or excised tissue endoscopically within the upper airways and tracheobronchial tree.

The Rescue™ Pulmonary Grasping Forceps is composed of thumb ring, spool and sliding handle at proximal end and the middle section of the Rescue™ Pulmonary Grasping Forceps is flexible shaft with a working length of 120 cm that connects with the distal end jaw assembly with an inner jaw opening of 4.5 mm. The Rescue™ Pulmonary Grasping Forceps is designed to pass through a 2.0 mm or greater working channel of a bronchoscope.

To operate the device, the user slides the spool back and forth over the handle body to open and close the iaws. The spool simultaneously actuates the pull wire which runs the length of the device and terminates with a connection to the jaws. Using Rescue™ Pulmonary Grasping Forceps the users can grasp excised tissue or foreign body by opening and then closing the jaws. The users can then retrieve the excised tissue or foreign body by pulling the Rescue™ Pulmonary Grasping Forceps with bronchoscope out of patient.

The provided text is a 510(k) Pre-market Notification for a medical device called "Rescue™ Pulmonary Grasping Forceps." This document details the device's characteristics, intended use, and performance data to establish substantial equivalence to a predicate device.

However, the document does not contain the information requested regarding acceptance criteria and study details for an AI-enabled medical device. The "Rescue™ Pulmonary Grasping Forceps" is a physical medical instrument, and the performance data described relates to its physical and material properties, not an AI algorithm.

Therefore, I cannot provide the requested information. The document does not describe:

• Acceptance criteria for an AI algorithm.
• Device performance in terms of AI metrics (e.g., sensitivity, specificity).
• Sample sizes for test sets in an AI study.
• Data provenance for AI training or testing.
• Number or qualifications of experts for AI ground truth.
• Adjudication methods for AI test sets.
• MRMC comparative effectiveness study for AI.
• Standalone performance for an AI algorithm.
• Type of ground truth for an AI algorithm.
• Sample size or ground truth establishment for an AI training set.

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