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The MedShape, Inc., ExoShape Duo Soft Tissue Fastener is intended for fixation of soft tissue to bone in the shoulder, foot/ankle, knee, hand/wrist and elbowing procedures:

• Shoulder. Rotator Cuff Repairs, Bankart Repair, SLAP Lesion Repair, Biceps Tenodesis, Acromio-Clavicular Separation Repair, Deltoid Repair. Capsular Shift or Capsulolabral Reconstruction
• Foot/Ankle: Lateral Stabilization, Medial Stabilization, Achilles Tendon Repair, Hallux Valgus Reconstruction, Mid-foot Reconstruction, Metatarsal Ligament Repair. Flexor Hallucis Longus for Achilles Tendon Reconstruction and Tendon Transfers
• Knee: Cruciate Ligament Repair, Medial Collateral Ligament Repair, Lateral Collateral Ligament Repair. Patellar Tendon Repair. Posterior Oblique Ligament Repair, Iliotibial Band Tenodesis
• Elbow. Biceps Tendon Reattachment and Ulnar or Radial collateral Ligament Reconstruction
• Hand/Wrist Scapholunate Ligament Reconstruction, Ulnar Collateral Ligament Reconstruction. Radial Collateral Ligament Reconstruction. Carpometacarpal Joint Arthroplasty (basal thumb joint arthroplasty), Carpal Ligament Reconstructions and Repairs and Tendon Transfers

The proposed ExoShape® Duo Soft Tissue Fastener is a sterile, single use, orthopedic implant intended to be used for fixation of tissue including ligament or tendon to bone and bone tendon bone. The ExoShape® Duo Soft Tissue Fastener is designed to use the principles of both interference fit and bearing area to reattach soft tissue intended for insertion into a hole created in bone,
The ExoShape® Duo Soft Tissue Fastener body is comprised of two interlocking PEEK. Both components are expanded into the bone hole. compressing the soft tissue against the bone wall and locking the implant to the bone; fastening the assembly into place.

The provided FDA 510(k) summary for the MedShape ExoShape® Duo Soft Tissue Fastener describes the device and its indications for use, but it does not contain the detailed performance study information requested to fill out the table and answer all the questions.

The document states: "functional performance testing has been conducted in Sawbone® bone analoque. This testing included monotonic soft tissue fixation strength (pull-to-failure) and other dimensional verification and material safety testing (both bio and MRI compatibility). Analysis of the results supports the conclusion that the proposed device is substantially equivalent to the predicate devices."

However, it does not provide:

• Specific acceptance criteria values (e.g., minimum pull-out strength in Newtons).
• Reported device performance values against those criteria.
• Details about the study design beyond "monotonic soft tissue fixation strength (pull-to-failure)".
• Sample sizes used for testing.
• Information about expert involvement for ground truth, adjudication methods, or comparative effectiveness studies with human readers, as this is a physical medical device, not an AI/software device.
• Specific ground truth types (other than implied physical measurements).
• Any information regarding training sets, as it's a physical device, not a machine learning model.

Therefore, I can only provide a partial answer based on the available text.

Acceptance Criteria and Device Performance

The document broadly mentions functional performance testing to establish substantial equivalence. However, it does not explicitly list quantitative acceptance criteria or the specific numerical performance results of the ExoShape® Duo Soft Tissue Fastener in a table format. It simply states that analysis of the results supports substantial equivalence.

• Monotonic soft tissue fixation strength (pull-to-failure) meeting or exceeding predicate devices. | Implied:
• Met or exceeded the performance of predicate devices in monotonic soft tissue fixation strength. |
  | Expected:
• Dimensional verification | Implied:
• Dimensions were verified to be appropriate. |
  | Expected:
• Material safety (biocompatibility) | Implied:
• Found to be biocompatible. |
  | Expected:
• MRI compatibility | Implied:
• Found to be MRI compatible. |

Study Details (Based on available information)

1. Sample size used for the test set and the data provenance:

   • Sample Size: Not specified in the provided text.
   • Data Provenance: The testing was conducted in "Sawbone® bone analogue," which implies a laboratory, in-vitro setting, rather than human or animal data. No country of origin for the data is mentioned. The study is experimental (performance testing), not retrospective or prospective in a clinical sense.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not applicable as this is a physical device being tested for mechanical properties, not an AI/software requiring expert adjudication for ground truth.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

   • Not applicable as this is a physical device. Ground truth would be based on direct measurement of physical properties.

4. 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 a physical orthopedic implant, not an AI-assisted diagnostic tool.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • Not applicable. This is a physical device performing a mechanical function. Its performance is inherently standalone in that it's the device itself being tested, not an algorithm.

6. The type of ground truth used:

   • The ground truth would be based on physical measurements of performance (e.g., force required for pull-out, dimensional accuracy) derived from the "monotonic soft tissue fixation strength (pull-to-failure)" and "dimensional verification" tests.

7. The sample size for the training set:

   • Not applicable. This is a physical device, not an AI/machine learning model, so there is no training set.

8. How the ground truth for the training set was established:

   • Not applicable. As above, there is no training set for a physical device.

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