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The DePuy Mitek MILAGRO BR Interference Screws are designed to attach soft tissues to bone in orthopedic surgical procedures for following indications: Shoulder: Proximal Biceps Tenodesis, Acromio-Clavicular Repair Elbow: Distal Biceps Tenodesis, Ulnar Collateral Ligament repair Knee: Collateral Ligament Repair

The DePuy Mitek Milagro® Interference Screws are absorbable, tapered, cannulated, threaded fasteners for use in interference fixation of soft tissue grafts or bone-tendon grafts. The Interference Screw is made from a composite made of absorbable Poly (lactide-co-glycolide) polymer and Tricalcium Phosphate (TCP). Modular Drivers, Ratchet Handle, Guidewires, a Tap and Notcher are provided separately as reusable accessories to assist in the placement of the MILAGRO BR Screw. The proposed DePuy Mitek Milagro® Interference Screws are offered in diameters of 5, 6mm and in the length of 12mm.

The provided text describes the DePuy Mitek Milagro® Interference Screws and their substantial equivalence to predicate devices, but it does not contain information about acceptance criteria or a study designed to prove the device meets specific acceptance criteria in the context of an AI/ML model for medical diagnosis or prognosis.

The document is a 510(k) summary for a medical device (interference screws) seeking clearance from the FDA. The "acceptance criteria" and "study" described in the prompt refer to the typical evaluation process for AI/ML-driven medical devices, often involving performance metrics like sensitivity, specificity, AUC, and comparison to human expert performance. This document, however, focuses on the physical and mechanical properties of the interference screws and their intended use.

Here's a breakdown of why the requested information cannot be extracted from the provided text, followed by what can be extracted:

Why the Requested Information (for an AI/ML Model) Cannot Be Provided:

• No AI/ML Model: The document describes a physical medical device (implants), not a software algorithm or AI/ML model. Therefore, concepts like "acceptance criteria for an AI model," "sample size for test set," "data provenance," "number of experts for ground truth," "adjudication method," "MRMC study," "standalone performance," "training set," or "ground truth for training set" are not applicable to the content provided.
• Focus on Substantial Equivalence: The primary objective of this 510(k) submission is to demonstrate "substantial equivalence" to existing, legally marketed predicate devices. This involves comparing technological characteristics, materials, and indications for use, often supported by non-clinical (bench) testing, rather than clinical efficacy studies in the way an AI/ML device would be evaluated.

Information Extracted from the Provided Text (Relevant to the Medical Device itself):

Since the prompt asks for a table of acceptance criteria and reported device performance, and the document is about a physical device's non-clinical testing, I will interpret "acceptance criteria" as the performance metrics evaluated during the non-clinical testing and "reported device performance" as the implication that these tests demonstrated substantial equivalence. However, specific numerical targets (e.g., "pullout strength > X N") are not provided.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document states, "Results of performance and safety testing have demonstrated that the proposed device is substantially equivalent to the predicate devices." It does not provide specific numerical values for the acceptance criteria or the device's performance, but rather confirms that the device met the necessary standards for substantial equivalence based on these non-clinical tests.

The remaining numbered points (2 through 9) are explicitly related to evaluating an AI/ML model, which this document does not describe. Therefore, the answer for those points is that the information is Not Applicable or Not Provided in the context of this medical device submission.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not Applicable / Not Provided. This document describes non-clinical (bench) testing of a physical implant, not an AI/ML model evaluated with a test set of data. The "test set" would be the screws themselves and the materials used in the bench tests (e.g., bone surrogates). The sample sizes for these bench tests are not specified, nor is the provenance of the materials used.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not Applicable / Not Provided. This concept relates to human expert annotation for AI/ML ground truth. For this physical device, "ground truth" would be the objective results of the mechanical tests, not expert consensus on images or other data.

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

• Not Applicable / Not Provided. Adjudication methods are used to resolve disagreements among human experts providing ground truth for AI/ML models. This is not relevant to the non-clinical testing of an orthopedic implant.

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

• Not Applicable / Not Provided. An MRMC study evaluates the performance of human readers, sometimes with and without AI assistance. This document pertains to a physical medical device and does not involve human readers or AI assistance in its evaluation.

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

• Not Applicable / Not Provided. This refers to the standalone performance of an AI algorithm. The device is a physical implant, not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• Objective Mechanical Test Results. For the non-clinical testing described (Insertion Torque, Anchor Pullout, Torque to Failure), the "ground truth" would be the objective, quantifiable measurements obtained from the mechanical tests performed on the implant, not expert consensus or pathology in a clinical context.

8. The sample size for the training set

• Not Applicable / Not Provided. There is no "training set" as this is not an AI/ML model.

9. How the ground truth for the training set was established

• Not Applicable / Not Provided. There is no "training set" or corresponding ground truth establishment process for this physical device.

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The Milagro® BR interference screws are designed to attach soft tissues to bone in orthopedic surgical procedures. The screws may be used for interference fixation of soft tissues (such as ligaments or tendons) to bone, when the implant sizes offered are patient appropriate. The implant operates, in conjunction with the appropriate postoperative immobilization, throughout the healing period.

The DePuy Mitek Milagro® Interference Screws are absorbable, tapered, cannulated, threaded fasteners for use in interference fixation of soft tissue grafts or bone-tendon grafts. The Interference Screw is made from a composite made of absorbable Poly (lactide-co-glycolide) polymer and Tricalcium Phosphate (TCP).

The provided document is a 510(k) Summary for a medical device (Milagro® Interference Screws) seeking substantial equivalence to predicate devices. It focuses on demonstrating that the new device shares similar technological characteristics and performance to already legally marketed devices. As such, it does not involve the typical assessment of a new diagnostic algorithm or AI system, which would require the specific details outlined in your request.

Therefore, many of the requested items (e.g., sample sizes for test/training sets, expert qualifications, MRMC studies, standalone performance of an algorithm, ground truth methods for AI training) are not applicable to this type of submission.

However, I can extract the relevant information regarding the device's acceptance criteria and the studies performed to demonstrate its performance relative to its predicates.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

The submission doesn't explicitly state "acceptance criteria" in the same way a diagnostic algorithm's F1 score or AUC would be presented. Instead, its acceptance is based on demonstrating "substantial equivalence" through nonclinical testing. The "acceptance criteria" can be inferred as meeting the performance levels demonstrated by the predicate devices or demonstrating acceptable mechanical performance for the intended use.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not specified in the provided summary. Nonclinical testing typically involves a set number of physical samples (e.g., screws) tested under laboratory conditions.
• Data Provenance: Not applicable in terms of human patient data. The tests are "in-vitro physiological aging" and mechanical tests on the physical devices. This is not retrospective or prospective human clinical data.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications

• Not Applicable. This document describes the mechanical testing of a medical device, not a diagnostic algorithm that requires expert-established ground truth.

4. Adjudication Method for the Test Set

• Not Applicable. No human-based adjudication is mentioned for these nonclinical, mechanical tests.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• No. This type of study is relevant for diagnostic imaging or interpretation systems with human readers, not for the mechanical performance of a physical interference screw.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

• Not Applicable. This is a physical medical device, not an algorithm.

7. The Type of Ground Truth Used

• Not Applicable. For mechanical tests, the "ground truth" is typically the measured physical properties based on standardized testing methods, comparing against established benchmarks or predicate device performance. It doesn't involve expert consensus, pathology, or outcomes data in the way a diagnostic study would.

8. The Sample Size for the Training Set

• Not Applicable. As this is a physical medical device and not an AI algorithm, there is no "training set" in the context of machine learning.

9. How the Ground Truth for the Training Set Was Established

• Not Applicable. Same as above.

In summary:

This 510(k) summary is designed to demonstrate "substantial equivalence" of a new version of an interference screw to existing, legally marketed devices. The "study" involves nonclinical, mechanical, and material characteristic tests to show that the new device performs similarly and is as safe and effective as its predicates. It does not involve human subjects, imaging interpretation, or AI algorithms, which are the contexts where most of your specific questions would apply.

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