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510(k) Data Aggregation
(63 days)
The intended use of the external fixation device is the same as that expressed in the predicate device 510k.
The intended use of the external fixator is unilateral external fixation, device which is intended for use in the treatment of bone conditions including limb lengthening, osteotomies, arthrodesis, fracture fixation and other bone conditions amenable to treatment by use of the external fixation modality.
The external fixator like the predicate device includes various size frames, bars, pin clamps, pins, accessories and instruments. The pin clamps enable the frame to be coupled to bone by securing the pins for the intended use. The various components within the system are provided to accommodate various anatomies and injuries.
This is a 510(k) premarket notification for an external fixator device, which is a hardware device. The provided text is a regulatory submission for a medical device and does not contain information about software algorithms, AI, or performance studies in the context of digital health or AI/ML-enabled devices. Therefore, I cannot extract the requested information.
Here's why:
- Device Type: The document describes an "External Fixator," which is a physical, mechanical medical device used for orthopedic conditions. It is not a software device or an AI/ML system.
- Approval Process: The 510(k) summary explicitly focuses on demonstrating substantial equivalence to a predicate device (another external fixator) based on similar design characteristics and intended use. This is a common pathway for traditional medical devices.
- Missing Information: The document does not discuss:
- Acceptance criteria related to AI/ML performance metrics (e.g., sensitivity, specificity, AUC).
- Any study involving a "test set," "training set," "ground truth," "experts," "adjudication," or "human readers."
- "Multi reader multi case (MRMC) comparative effectiveness study" or "standalone performance."
In summary, the provided text is for a physical medical device and does not contain the kind of information requested about AI device performance studies.
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