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The MedCAD® AccuStride™ System is intended to be used as a surgical instrument to assist in preoperative planning and/or in guiding the marking of bone and/or guide surgical instruments in nonacute, non-joint replacing osteotomies in the foot for adult and pediatric patients 12 years of age and older. The MedCAD® AccuStride™ System surgical guides are intended for single use only. The MedCAD® AccuStride™ System surgical guides should only be used when the anatomic landmarks necessary for pre-operative planning can be clearly identified on the patient's radiographic images (i.e., CT).

The MedCAD® AccuStride™ System is a collection of two individual pieces of software and associated additive manufacturing equipment intended to provide a variety of outputs to support non-acute, nonjoint replacing osteotomies in the foot. The system uses electronic medical images of the patient's anatomy or with input from the physician, to manipulate original patient images for planning and executing surgery. The patient specific outputs from the system includes anatomical models, surgical guides, and patient-specific case reports.

Following the MedCAD® Quality System and specific Work Instructions, trained employees utilize Commercial Off-The-Shelf (COTS) software to manipulate 3-D medical Computed Tomography (CT) images to create patient-specific physical and digital outputs. The process requires clinical input and review from the physician during and prior to delivery of the final outputs. It should be noted that the system is operated only by trained MedCAD employees, and the physician does not directly input information. The physician provides input for model manipulation and interactive feedback through viewing of digital models of system outputs that are modified by the engineer during the planning session.

The FDA 510(k) summary for the MedCAD® AccuStride™ System provides information about the device's acceptance criteria and the studies conducted. However, it does not provide detailed acceptance criteria and performance metrics for the software's ability to perform preoperative planning or guide marking, as typically expected for pure AI/ML software. Instead, the performance testing focuses on the physical components (surgical guides and anatomical models) produced by the system.

Here's an attempt to extract and infer the requested information based on the provided text, while also highlighting the limitations:

1. Table of Acceptance Criteria and Reported Device Performance

Important Note: The document focuses on the outputs of the system (surgical guides and anatomical models) rather than the standalone performance of the software in performing planning or marking. The "acceptance criteria" presented here are high-level outcomes of physical tests related to these outputs. There are no explicit metrics for the accuracy or efficacy of the "surgical instrument to assist in preoperative planning and/or in guiding the marking of bone" as a software component itself.

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

• Wear Debris Testing: No specific sample size is provided beyond "Cutting / drilling instruments were used on a worst-case titanium surgical guide."
• Simulated Use Cadaver Validation Testing: No specific sample size is given for the number of cadavers used. It mentions "Guides were created to support bunion and metadductus, metatarsal 2+3, akin, and calcaneal osteotomies," implying a variety of cases were tested.
• Data Provenance: The cadaver validation testing is experimental ("simulated surgeries in cadavers") rather than derived from retrospective or prospective patient data. There is no information on the country of origin.

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

The document states, "The process requires clinical input and review from the physician during and prior to delivery of the final outputs." However, it does not specify the number of experts, their qualifications, or how they established the ground truth for the performance testing. The input and review from physicians appear to be part of the operational process rather than a formal ground truth establishment for a validation study.

4. Adjudication Method for the Test Set

No specific adjudication method (e.g., 2+1, 3+1) is mentioned for the performance testing. The "clinical input and review from the physician" is described as part of the normal workflow.

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

No MRMC comparative effectiveness study is mentioned. The performance testing focuses on the physical guides in a simulated cadaver environment, not on human readers' improvement with or without AI assistance.

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

The device description explicitly states: "the system is operated only by trained MedCAD employees, and the physician does not directly input information. The physician provides input for model manipulation and interactive feedback through viewing of digital models of system outputs that are modified by the engineer during the planning session." This indicates that the system is not a standalone AI algorithm without human-in-the-loop. It's a system where trained MedCAD employees (a human element) use software to develop outputs based on physician input and feedback. The performance testing reflects this human-in-the-loop process for generating the physical guides.

7. The Type of Ground Truth Used

• Wear Debris Testing: The ground truth for this test is implicit in "less than that reported in the literature to be safe." This refers to established safety thresholds for wear debris.
• Simulated Use Cadaver Validation Testing: The ground truth for this test appears to be "pre-surgical plans" for angle and position data, and subjective evaluation of "usability and fit." This is an engineering/design validation approach rather than pathology, expert consensus, or outcomes data from clinical cases.

8. The Sample Size for the Training Set

The document does not provide any information regarding a training set size. The system uses "Commercial Off-The-Shelf (COTS) software to manipulate 3-D medical Computed Tomography (CT) images." This suggests the core image manipulation algorithms might be pre-trained components within the COTS software, or the system does not involve machine learning in a way that requires a specifically defined "training set" for the AccuStride™ System's unique functionalities.

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

Since no training set is described for the MedCAD® AccuStride™ System functionality, there is no information on how its ground truth was established.

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