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Orthocon Montage Flowable Settable Bone Paste is a self-setting calcium phosphate cement indicated for use in the repair of neurosurgical burr holes, contiguous cranial defects with a surface area no larger than 25cm2. Montage Flowable Settable, Resorbable Bone Paste should be used only in skeletally mature individuals.

Montage Flowable Settable, Resorbable Bone Paste is a sterile, biocompatible, resorbable material for use in repair of cranial defects. The Montage Flowable device comprises two separate components of putty consistency containing granular calcium phosphate, calcium stearate, vitamin E acetate, a triglyceride, polyalcohols and a mixture of a lactide-diester and polyester-based polymers. When mixed together, the components of the Montage Flowable device form a cohesive putty-like material that adheres to the bone surface and remains in place following application. The resulting hardened, resorbable material is primarily calcium phosphate. Montage Flowable components must be mixed immediately prior to use.

The provided document is a 510(k) summary for the Orthocon Montage Flowable Settable, Resorbable Bone Paste. This document is a premarket notification for a medical device and is not a study proving the device meets acceptance criteria for an AI/ML-based medical device.

The document discusses:

• The device's name, regulation, and product code.
• Its intended use (repair of neurosurgical burr holes, cranial defects).
• Comparisons of technological characteristics with a predicate device (Stryker HydroSet Injectable Cement).
• Biocompatibility and performance testing (bench testing and in-vivo animal testing).
• A clear statement that no clinical studies have been conducted in support of this 510(k).

Therefore, the requested information about acceptance criteria and a study proving device performance for an AI/ML-based medical device cannot be extracted from this document, as it pertains to a different type of medical device (a bone paste) and the regulatory submission is not based on AI/ML performance.

To answer your prompt, I would need a document describing the regulatory submission for an AI/ML-based medical device, which would typically include detailed information on clinical performance studies, ground truth establishment, expert adjudication, and sample sizes for training and test sets.

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Orthocon MONTAGE-OS Settable Bone Putty is a self-setting calcium phosphate cement indicated for use in the repair of neurosurgical burr holes, contiguous cranial defects with a surface area no larger than 25cm². MONTAGE-QS Settable, Resorbable Bone Putty should be used only in skeletally mature individuals.

MONTAGE-QS Settable, Resorbable Bone Putty is a sterile, biocompatible, resorbable material for use in repair of cranial defects. The MONTAGE-QS device comprises two separate components of putty consistency containing granular calcium phosphate, calcium stearate, vitamin E acetate, a triglyceride, polyalcohols and a mixture of a lactide-diester and polyesterbased polymers. When mixed together, the components of the MONTAGE-QS device form a putty-like material. The resulting hardened, resorbable material is primarily calcium phosphate. MONTAGE-QS components must be mixed immediately prior to use.

The provided text describes a medical device, MONTAGE-QS Settable, Resorbable Bone Putty, and its substantial equivalence to a predicate device. However, it does not explicitly detail acceptance criteria and a study proving the device meets those criteria in the context of typical AI/software device evaluation.

Based on the provided information, here's an attempt to answer your request, focusing on the information that is present and noting where information is absent for an AI device.

1. A table of acceptance criteria and the reported device performance

The document does not explicitly state "acceptance criteria" in the way one would for an AI model's performance metrics (e.g., AUC, sensitivity, specificity thresholds). Instead, the performance evaluation is based on demonstrating equivalence to a predicate device through various tests. The "performance" for MONTAGE-QS is a qualitative assessment of its material properties and biological response compared to the predicate.

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

• Test Set Sample Size:
  • Efficacy Evaluation (Cranioplasty): The study was conducted in a "rabbit cranial critical-sized defect model." The specific number of rabbits or defects studied is not provided.
  • Bench Testing: The sample sizes for each bench test are not specified.
  • In-Vivo Testing: Similar to the efficacy evaluation, it refers to an "animal model" without giving specific numbers.
• Data Provenance: The document states "rabbit cranial critical-sized defect model" and "in-vivo animal testing," indicating the data is from preclinical animal studies. The country of origin and whether it was retrospective or prospective data are not specified.

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)

• The "ground truth" for this device's evaluation (e.g., whether bone defects were filled, or histopathological assessments) would have been established by experts in veterinary pathology or relevant scientific fields. However, the document does not state the number of experts or their qualifications. The assessments are described as "histology-based assessments" and "histopathologic evaluation."

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

• The document does not describe any adjudication method for establishing ground truth or evaluating the test results.

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 a submission for a bone putty, not an AI or software device. Therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is not applicable and was not performed.

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

• This is a submission for a bone putty, not an AI or software device. Therefore, a standalone algorithm performance study is not applicable and was not performed.

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

• For the efficacy and in-vivo testing, the ground truth was based on histopathologic evaluation in an animal model. For bench testing, it was based on measurable physical and chemical properties against predetermined specifications.

8. The sample size for the training set

• This is a submission for a physical medical device (bone putty), not an AI or software device. Therefore, the concept of a "training set" in the context of machine learning is not applicable. The device's performance is demonstrated through testing against specifications and comparison to a predicate, not by training an algorithm.

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

• As a training set is not applicable for this type of device, the method for establishing its "ground truth" is not applicable.

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