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OsteoDetect analyzes wrist radiographs using machine learning techniques to identify and highlight distal radius fractures during the review of posterior-anterior (PA) and lateral (LAT) radiographs of adult wrists.

OsteoDetect is a software device designed to assist clinicians in detecting distal radius fractures during the review of posterior-anterior (PA) and lateral (LAT) radiographs of adult wrists. The software uses deep learning techniques to analyze wrist radiographs (PA and LAT views) for distal radius fracture in adult patients.

1. Table of Acceptance Criteria and Reported Device Performance

Standalone Performance

MRMC (Reader Study) Performance - Aided vs. Unaided Reads

2. Sample Size and Data Provenance for Test Set

Standalone Performance Test Set:

• Sample Size: 1000 images (500 PA, 500 LAT)
• Data Provenance: Retrospective. Randomly sampled from an existing validation database of consecutively collected images from patients receiving wrist radiographs at the (b) (4) from November 1, 2016 to April 30, 2017. The study population included images from the US.

MRMC (Reader Study) Test Set:

• Sample Size: 200 cases.
• Data Provenance: Retrospective. Randomly sampled from the same validation database used for the standalone performance study. The data includes cases from the US.

3. Number of Experts and Qualifications for Ground Truth

Standalone Performance Test Set and MRMC (Reader Study) Test Set:

• Number of Experts: Three.
• Qualifications: U.S. board-certified orthopedic hand surgeons.

4. Adjudication Method for Test Set

Standalone Performance Test Set:

• Adjudication Method (Binary Fracture Presence/Absence): Majority opinion of at least 2 of the 3 clinicians.
• Adjudication Method (Localization - Bounding Box): The union of the bounding box of each clinician identifying the fracture.

MRMC (Reader Study) Test Set:

• Adjudication Method: Majority opinion of three U.S. board-certified orthopedic hand surgeons. (Note: this was defined on a per-case basis, considering PA, LAT, and oblique images if available).

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was an MRMC study done? Yes.
• Effect Size (Improvement of Human Readers with AI vs. without AI assistance):
  • The least squares mean difference between the AUC for OsteoDetect-aided and OsteoDetect-unaided reads is 0.049 (95% CI, (0.019, 0.080)). This indicates a statistically significant improvement in diagnostic accuracy (AUC) of 4.9 percentage points when readers were aided by OsteoDetect.
  • Sensitivity: Improved from 0.747 (unaided) to 0.803 (aided), an improvement of 0.056.
  • Specificity: Improved from 0.889 (unaided) to 0.914 (aided), an improvement of 0.025.

6. Standalone (Algorithm Only) Performance Study

• Was a standalone study done? Yes.

7. Type of Ground Truth Used

Standalone Performance Test Set:

• Type of Ground Truth: Expert consensus (majority opinion of three U.S. board-certified orthopedic hand surgeons).

MRMC (Reader Study) Test Set:

• Type of Ground Truth: Expert consensus (majority opinion of three U.S. board-certified orthopedic hand surgeons).

8. Sample Size for Training Set

The document does not explicitly state the sample size for the training set. It mentions "randomly withheld subset of the model's training data" for setting the operating point, implying a training set existed, but its size is not provided.

9. How Ground Truth for Training Set Was Established

The document does not explicitly state how the ground truth for the training set was established. It only refers to a "randomly withheld subset of the model's training data" during the operating point setting.

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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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