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The Indications for Use for The Acumed Wrist Fixation System / Plates, is to provide fixation of fractures, fusions, osteotomies, and nonunions of the distal radius and ulna.

The Indications for Use for The Acumed Wrist Fixation System/ Screws, is to provide fixation of fractures, fusions, osteotomies, and nonunions of the distal radius and ulna.

The Acumed Wrist Fixation System is a new system designed for distal radius and ulna fracture fixation. The system is comprised of plates, screws, and instruments designed for aid in implantation of the distal. The intended use of The Acumed Wrist Fixation System is to provide fixation of fractures, fusions, osteotomies, and nonunions of the distal radius and ulna. The implants are manufactured from Titanium Alloy per ASTM F136-13(2021)e1 and Cobalt Chrome per ASTM F1537-20. The implants are provided sterile and non-sterile and are for single use.

This FDA 510(k) clearance letter is for a device, the Acumed Wrist Fixation System, which comprises plates and screws, not an AI/ML powered medical device. Therefore, the information requested in your prompt regarding acceptance criteria, study details, and AI-specific metrics (like MRMC studies, ground truth establishment for training sets, or expert qualifications for test sets) is not applicable to this document.

The document primarily focuses on demonstrating substantial equivalence to predicate devices through technological comparison, material safety, and mechanical performance testing, rather than studies involving human readers or AI algorithms.

Here's a breakdown of why this document does not contain the requested information:

• Device Type: This is a physical orthopedic implant (plates and screws), not a software or AI-driven diagnostic or therapeutic device.
• Acceptance Criteria/Performance: The "acceptance criteria" for such a device are typically defined by engineering standards (e.g., ASTM standards for mechanical properties, material biocompatibility), and the "performance" refers to how the device meets these physical and mechanical benchmarks. The document lists the specific ASTM standards used for testing (e.g., F382-24 for bone plates, F543-23 for bone screws) and states that "all generated passing results." However, it does not provide specific numerical criteria or reported values in a table as requested for AI performance metrics (e.g., sensitivity, specificity).
• Study Design (Test Set, Data Provenance, Experts, Adjudication, MRMC, Standalone): These concepts are fundamentally linked to evaluating AI algorithms for diagnostic or prognostic purposes, which involve processing data (images, signals) and making predictions that need to be compared against a human-established ground truth. Since this is a physical implant, these types of studies are not relevant.
• Ground Truth Type: For a physical implant, the "ground truth" relates to its material properties, biocompatibility, and mechanical integrity, which are assessed through engineering tests, not expert consensus on medical findings or pathology.
• Training Set Size/Ground Truth for Training: These are exclusively relevant to machine learning models, where a dataset is used to train the algorithm. A physical device does not have a "training set."

Summary of available information relevant to "studies" for this device:

1. Acceptance Criteria and Reported Device Performance:

   • Acceptance Criteria Basis: Compliance with various ASTM (American Society for Testing and Materials) standards and FDA Guidance Documents for orthopedic fracture fixation devices.
   • Specific Standards Mentioned:
     • ASTM F382-24: Standard Specification and Test Method for Metallic Bone Plates (for static and simulated dynamic 4-point bending testing of plates).
     • ASTM F543-23: Standard Specification and Test Methods for Metallic Medical Bone Screws (for torsional strength, driving torque, and theoretical axial pullout strength calculation of screws).
     • ASTM F384-17: Standard Specifications and Test Methods for Metallic Angled Orthopedic Fracture Fixation Devices (for single-cycle bend testing of pegs).
     • ASTM F3044-20: Standard Test Method for Evaluating the Potential for Galvanic Corrosion for Medical Implants (for corrosion testing).
     • ASTM F2052, F2213, F2182, F2119: Standards for MR safety evaluations (magnetically induced force, torque, radio frequency induced heating, and image artifact evaluations).
   • Reported Device Performance: The document states that evaluations "have all generated passing results," indicating conformity to the specified standards and guidance documents. Specific numerical performance data (e.g., exact bending strength values, torque values) are not provided in this summary but would be detailed in the full submission.

2. Sample Size/Data Provenance for Test Set:

   • Not applicable as this involves mechanical and material testing, not data analysis on a "test set" in the AI sense. The "samples" would be physical devices tested according to engineering protocols.

3. Number of Experts/Qualifications for Ground Truth:

   • Not applicable. Ground truth for mechanical testing is established by adherence to standardized testing protocols and measurement science, not expert clinical consensus.

4. Adjudication Method:

   • Not applicable.

5. MRMC Comparative Effectiveness Study:

   • Not applicable. This is not an AI-powered device.

6. Standalone Performance:

   • The "performance" described is inherently "standalone" in the sense that it refers to the physical device's characteristics under controlled laboratory conditions, without human interaction in a diagnostic loop.

7. Type of Ground Truth Used:

   • Engineering and material science standards (e.g., ASTM specifications for mechanical properties, material composition, and biocompatibility).

8. Sample Size for Training Set:

   • Not applicable. This is not an AI/ML device.

9. How Ground Truth for Training Set was Established:

   • Not applicable.

In conclusion, the provided FDA 510(k) clearance letter pertains to a non-AI medical device and therefore does not contain the specific information requested about acceptance criteria, study design, and performance metrics typically associated with AI/ML-powered medical devices. The "studies" for this device were non-clinical, mechanical, and material integrity tests to ensure safety and effectiveness in comparison to predicate devices.

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