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The WishBone Medical Plate and Screw System is used for pediatric and adult patients as indicated for pelvic, small and long bone fracture fixation of bones that have been surgically prepared (osteotomy) for correction of deformity or arthrodesis.

Indications for use include fractures of the clavicle, scapula, humerus, ulna, radius, middle hand, metacarpals, pelvis acetabulum. femur. fibula. tibia. metatarsals and middle foot bones. and treatment of the calcaneus.

Specifically, the Straight Fibula Plates are in infant, child, and adolescent pediatric subgroups and adult patients.

The WishBone Medical 7-Hole Straight Fibula Plate and WishBone Medical 3.0mm Cortical Screws and Locking Screws are wrought titanium alloy plate and screws (per ASTM F136) that are being added to the existing WishBone Medical Plate and Screw System as a line extension.

The provided document is a 510(k) premarket notification for a medical device (WishBone Medical Plate and Screw System: 3.0mm Screws and 7-Hole Straight Fibula Plate). This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving the device meets specific acceptance criteria through a full-scale clinical trial or an AI algorithm's performance study.

Therefore, the document does NOT contain information relevant to the following points of your request:

• 1. A table of acceptance criteria and the reported device performance: This document does not establish performance criteria in terms of accuracy, sensitivity, or specificity for an AI algorithm.
• 2. Sample sized used for the test set and the data provenance: There is no "test set" in the context of an AI algorithm's performance study described here. The "testing" refers to mechanical and engineering analyses.
• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Ground truth establishment by experts is relevant for AI model training and validation, which is not discussed here.
• 4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable for this type of device submission.
• 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: This is relevant for AI-assisted diagnostic devices, which this is not.
• 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: No AI algorithm is described.
• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable for the type of testing described (mechanical/engineering analyses).
• 8. The sample size for the training set: There is no AI model or training set discussed.
• 9. How the ground truth for the training set was established: Not applicable, as there is no AI model or training set.

What the document does describe regarding "acceptance criteria" and "proof":

The "acceptance criteria" for a 510(k) submission generally revolve around demonstrating substantial equivalence to a predicate device. The "proof" is provided through non-clinical (engineering) testing and analysis.

Here's a breakdown of the relevant information provided in the document:

Acceptance Criteria (Implied by Substantial Equivalence Framework):

The device is considered "accepted" if it is demonstrated to be substantially equivalent to a predicate device, meaning:

• Same intended use: Yes, stated multiple times ("The subject device and predicate systems have the same intended use").
• Same indications for use: Yes, stated multiple times ("The subject device and predicate systems have the same intended use" and specific indications for use are listed and align with the predicate's general category).
• Same materials: Yes, "The subject device is manufactured from the same materials." (wrought titanium alloy per ASTM F136).
• Similar design features: Yes, "The subject device design is similar to the predicates."
• Same sterilization method and dose: Yes, "The subject and predicate devices have the same sterilization method and dose."
• No new or increased risks identified: This is the overarching goal of the performance data.

Study Proving Device Meets Acceptance Criteria (Non-Clinical Testing):

The "study" in this context refers to engineering analyses and non-clinical testing.

• Type of Testing: Engineering analyses were conducted to demonstrate substantial equivalence with the predicate device.

  • Axial Pullout Strength (per FDA Guidance "Orthopedic Non-Spinal Metallic Bone Screws and Washers - Performance Criteria for Safety and Performance Based Pathway" issued December 11, 2020).
  • Engineering analysis for Screw Torsion (per ASTM F543) - no new worst case.
  • Engineering analysis for Bending Fatigue (per ASTM F382) - no new worst case.
  • Engineering analysis for Insertion and Removal - no new worst case.
  • Engineering analysis for Pull-through Strength - no new worst case.
  • Engineering analysis for Fretting and Corrosion (per ASTM F897) - no new worst case.
  • Engineering analysis for Biocompatibility Risk Assessment (per ISO 10993-1 and FDA Guidance "Use of International Standard ISO 10993-1, "Biological evaluation of medical devices - Part 1: Evaluation and testing within a risk management process" issued September 2020).

• Reported Device Performance (Results of Non-Clinical Testing):

  • "The devices performed within the intended use."
  • "No new questions of safety or efficacy were raised."
  • "The devices were found to be substantially equivalent to predicate devices."

• Sample Size: Not specified for each mechanical test, but implies sufficient samples were tested to conduct the engineering analyses.

• Data Provenance: The testing is laboratory-based engineering analysis, not patient data.

• Clinical Testing: "Clinical testing was not deemed necessary to demonstrate substantial equivalence."

In summary, this document is a regulatory submission for a traditional medical device (bone plate and screws), not an AI/software as a medical device. Therefore, the questions related to AI algorithm performance are not applicable. The device's "acceptance criteria" are met by demonstrating substantial equivalence through a series of specific engineering tests and analyses, indicating no new safety or effectiveness concerns compared to already approved devices.

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