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The Biomet Microfixation RibFix Blu Thoracic Fixation System is indicated for use in the stabilization and fixation of fractures in the chest wall including sternal reconstructive surgical procedures, trauma, or planned osteotomies. The system may be used in normal and poor bone to promote union.

The Biomet Microfixation RibFix Blu Thoracic Fixation System is composed of metallic locking bone plates and locking screws that provide rigid fixation to fractures of the chest wall. These implants are available in multiple sizes and manufactured from Commercially Pure Titanium and Titanium Alloy (Ti-6Al-4V).

This document is a 510(k) Premarket Notification from the FDA for the Biomet Microfixation RibFix Blu Thoracic Fixation System. It grants substantial equivalence to a previously cleared device. As such, it does not contain the detailed study information typically found when a novel device is being introduced and rigorous performance criteria need to be established and proven.

Here's why the requested information cannot be fully provided:

• No new acceptance criteria or study: The document explicitly states: "Non-clinical testing was not necessary for the determination of substantial equivalence." and "Clinical testing was not necessary for the determination of substantial equivalence." This means there were no new studies conducted to prove the current device meets acceptance criteria distinct from its predicate. The device is considered substantially equivalent because its indications, design, sterilization, and materials are identical to the predicate device (K152253).
• Focus on substantial equivalence: The purpose of a 510(k) is to demonstrate that a new device is "substantially equivalent" to a legally marketed predicate device. This typically means it has the same intended use and technological characteristics, or if there are differences, they do not raise new questions of safety or effectiveness.

Therefore, the requested information elements (acceptance criteria, study details, sample size, expert ground truth, adjudication, MRMC, standalone performance, training set details) are not present in this 510(k) summary because no new performance studies were required for this specific filing.

However, based on the principle of substantial equivalence, we can infer the acceptance criteria and performance are identical to the predicate device (K152253). Since this document doesn't provide those details, I cannot create the table or answer the specific questions about the study design.

If this were a new device requiring performance demonstration, the information would typically include:

1. A table of acceptance criteria and the reported device performance: This would list specific quantitative or qualitative measures (e.g., tensile strength, fatigue life, bending stiffness, biocompatibility results) with their targets and the actual measured performance from testing.
2. Sample size used for the test set and the data provenance: Details on how many devices were tested, if it was an in-vitro, ex-vivo, or in-vivo study, and where the data originated.
3. Number of experts and qualifications: If human interpretation or evaluation was involved (e.g., in a clinical study or imaging study), details about the physicians or specialists involved in assessments.
4. Adjudication method: How disagreements among experts were resolved.
5. MRMC comparative effectiveness study: Not typically applicable for hardware like fixation systems, but if comparing diagnostic performance, this would quantify improvements with AI assistance.
6. Standalone performance: The performance of the algorithm itself, without human input, if AI was involved.
7. Type of ground truth used: What was considered the definitive truth (e.g., histopathology, surgical confirmation, long-term outcomes).
8. Sample size for the training set: If machine learning was used, the size of the dataset used to train the algorithm.
9. How the ground truth for the training set was established: How accurate labels were assigned to the training data.

In summary, the provided document indicates that the Biomet Microfixation RibFix Blu Thoracic Fixation System is substantially equivalent to a previously cleared device, and thus, no new non-clinical or clinical performance data were deemed necessary for this 510(k) submission.

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The Biomet Microfixation RibFix Blu Thoracic Fixation System is indicated for use in the stabilization and fixation of fractures in the chest wall including sternal reconstructive surgical procedures, trauma, or planned osteotomies. The system may be used in normal and poor bone to promote union.

The Biomet Microfixation RibFix Blu Thoracic Fixation System is composed of metallic locking bone plates and locking screws that provide rigid fixation to fractures and osteotomies of the chest wall. These implants are available in multiple sizes and manufactured from Commercially Pure Titanium and Titanium Alloy (Ti-6Al-4V).

This document is a 510(k) summary for the Biomet Microfixation RibFix Blu Thoracic Fixation System. It describes a medical device, but it does not contain information about acceptance criteria or a study proving the device meets acceptance criteria in the context of an AI/ML algorithm or software.

Therefore, I cannot provide the requested information in the format of acceptance criteria and reported device performance because the document does not discuss these concepts for an AI/ML device.

Here's why the provided text is not relevant to your request for AI/ML device performance:

• Device Type: The Biomet Microfixation RibFix Blu Thoracic Fixation System is a physical medical device (metallic plates and screws for bone fixation). It is not an AI/ML algorithm or software.
• Performance Data: The document states, "Non-Clinical Performance Data: MRI simulation and physical testing were performed to support the MR Safety Information in the labeling. Clinical Performance Data: Clinical testing was not necessary for the determination of substantial equivalence." This refers to physical and material performance, not algorithmic performance.
• Lack of AI/ML Specifics: There is no mention of algorithms, machine learning, data sets (training or test), ground truth, experts for annotation, or any other terms relevant to AI/ML device evaluation.

If you have a document about an AI/ML medical device, I would be happy to analyze it according to your requested format.

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The Biomet Microfixation Thoracic Fixation System is indicated for use in the stabilization and fixation of fractures in the chest wall including sternal reconstructive surgical procedures, trauma, or planned osteotomies. The system may be used in normal and poor bone to promote union.

The Biomet Microfixation RibFix Blu Thoracic Fixation System is composed of metallic locking screws that are utilized with previously cleared metallic locking bone plates. These devices provide rigid fixation of fractures and osteotomies of the chest wall and sternum. The locking screws are offered in two diameters, 2.4mm and 2.7mm. The 2.4mm diameter screws are offered with two tip designs, self-drilling and self-tapping, and range in lengths from 7mm to 16mm. The 2.7mm diameter screws are offered with a self-tapping tip design and range in lengths from 7mm to 16mm. The locking screws are manufactured from Titanium Alloy (Ti-6Al-4V).

This document describes a 510(k) premarket notification for the Biomet Microfixation RibFix Blu Thoracic Fixation System. The submission does not provide information about acceptance criteria or a study that specifically proves the device meets acceptance criteria related to its performance in a clinical or AI-assisted context. The document's purpose is to demonstrate substantial equivalence to a legally marketed predicate device, not to report on an AI algorithm's performance.

Therefore, many of the requested categories are not applicable (N/A) based on the provided document.

Here's an assessment based on the available information:

1. Table of acceptance criteria and the reported device performance:
   Not applicable, as this document focuses on substantial equivalence for a medical device (bone fixation system), not an AI algorithm with specific performance metrics. There are no performance metrics or acceptance criteria for an AI system in this document.

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. The document states "Non-Clinical Performance Data: Non-clinical testing was not necessary for the determination of substantial equivalence." and "Clinical Performance Data: Clinical testing was not necessary for the determination of substantial equivalence." Therefore, no test set was used for this substantial equivalence determination in the context of device performance.

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. No test set was used, and thus no ground truth was established by experts for performance evaluation.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
   Not applicable. No test set was used.

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. This is a medical device submission for a physical fixation system, not an AI diagnostic or assistance tool. Therefore, no MRMC study or AI-related comparative effectiveness was conducted or reported.

6. If a standalone (i.e., algorithm only without human-in-the loop performance) was done:
   Not applicable. There is no AI algorithm involved with this device.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
   Not applicable. No ground truth was established for performance evaluation. The "ground truth" for this 510(k) submission relates to the functional and material characteristics of the device and its predicate, established through engineering design, material specifications, and regulatory guidelines, rather than clinical performance data.

8. The sample size for the training set:
   Not applicable. There is no AI algorithm to train.

9. How the ground truth for the training set was established:
   Not applicable. There is no AI algorithm to train, and therefore no training set or associated ground truth.

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