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The SI-TECHNOLOGY® SI-DESIS® Device is intended for sacroiliac joint fusion for conditions including sacroiliac (SI) joint disruptions and degenerative sacroiliitis.

The SI-TECHNOLOGY® SI-DESIS® Sacroiliac Joint Fusion Screw System consists of screws designed to assist in the healing of sacroiliac joints for fusion by providing fixation of large bones and large bone fragments of the pelvis. The screws are not intended to replace normal body structures. The screws are offered in 6.5 mm diameter cannulated, partially threaded or fully threaded configurations in various lengths. Additionally, optional supplemental 5 mm diameter screws are offered in various lengths and are to be used only in conjunction with the 6.5 mm screws for supplemental screw fixation to accommodate patient anatomy. The screws are manufactured from titanium alloy per ASTM F136.

This document describes the premarket notification (510(k)) for the SI-TECHNOLOGY® SI-DESIS® Screws, a medical device for sacroiliac joint fusion. The information provided is for device clearance, not for an AI/ML powered device. Therefore, many of the requested fields are not applicable or cannot be extracted from the provided text.

Here's the breakdown of the information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The "acceptance criteria" for this traditional medical device clearance are typically defined by recognized standards and comparison to predicate devices, rather than specific performance metrics like accuracy, sensitivity, or specificity that would be used for an AI/ML device. The "reported device performance" refers to the results of non-clinical testing demonstrating equivalence to predicate devices.

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

This information is not applicable as the document describes non-clinical engineering testing of a physical medical device (screws), not a diagnostic or prognostic AI/ML system that would use a test set of data. The testing involves physical samples of screws, not patient data.

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

This information is not applicable for a physical device like screws. "Ground truth" in this context would refer to the established engineering standards (ASTM F543, ASTM F2193) and the documented performance of the predicate devices, which are objective and do not require expert consensus in the way an AI diagnostic system would.

4. Adjudication Method for the Test Set

This information is not applicable for the non-clinical testing of a physical device. Adjudication methods like 2+1 or 3+1 are used for human expert review of clinical cases to establish ground truth for AI algorithms.

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

This information is not applicable as the document describes the clearance of a physical medical implant (screws), not an AI/ML system. Therefore, no MRMC study looking at human reader improvement with AI assistance was performed.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

This information is not applicable as the document is for a physical medical device, not an AI/ML algorithm.

7. The Type of Ground Truth Used

For the non-clinical engineering testing, the "ground truth" is established by:

• Recognized Industry Standards: ASTM F543 (Standard Specification for Metallic Medical Bone Screws) and ASTM F2193 (Standard Test Method for Static and Fatigue Testing of Sacroiliac Joint Fusion Implants). These standards define the mechanical properties and testing methodologies.
• Predicate Device Performance: The performance of legally marketed predicate devices (K112028 – Globus SI-LOK Sacroiliac Joint Fixation System, K021932 - Synthes 6.5 mm Cannulated Screw) served as a benchmark for comparison.

8. The Sample Size for the Training Set

This information is not applicable. There is no "training set" in the context of clearing a physical medical device through non-clinical engineering tests. Training sets are relevant for machine learning algorithms.

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

This information is not applicable as there is no training set for a physical medical device clearance based on non-clinical testing.

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