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The TL-HEX System is intended for limb lengthening by metaphyseal or epiphyseal distractions, fixation of open and closed fractures, treatment of non-union or pseudoarthrosis of long bones and correction of bony or soft tissue defects or deformities. Within this range, indications include:
• Post-traumatic joint contracture which has resulted in loss of range of motion
• Fractures and disease which generally may result in joint contractures or loss of range of motion and fractures requiring distraction
• Open and closed fracture fixation
• Pseudoarthrosis of long bones
• Limb lengthening by epiphyseal or metaphyseal distraction
• Correction of bony or soft tissue deformities
• Correction of bony or soft tissue defects
• Joint arthrodesis
• Infected fractures or non-unions

The Subject device is a multilateral external fixation system. The System can also be used with a web-based software component that is designed to be used to assist the physician in creating a patient adjustment schedule that assists in adjusting the six struts. The System can also be used with other existing Orthofix external fixation components and screws (such as TrueLok or X Caliber).
Components of the system include:
• Full and 5/8 aluminum Rings
• Adjustable struts
• Aluminum strut clips (number and direction)
• Stainless steel instrumentation such as hex drivers, wrenches, and pliers
• Implantable components such as half pins and wires
• Web-based software

The provided text describes a medical device, the Orthofix TL-HEX True Lok Hexapod System (TL-HEX), and its substantial equivalence determination by the FDA. However, it does not contain a study that proves the device meets specific acceptance criteria in the way a clinical or performance study for a diagnostic AI device would.

Instead, the document focuses on demonstrating substantial equivalence to predicate devices under the 510(k) pathway. This means proving that the new device is as safe and effective as a legally marketed device that does not require premarket approval (PMA). For the TL-HEX, this primarily involved mechanical and software testing to ensure the hardware components could withstand expected loads without failure and that the software performed as intended.

Therefore, the requested information based on a typical AI device performance study (e.g., sample size for test set, expert qualifications, MRMC study, ground truth establishment for training set) is not directly applicable or available in this document.

Here's an attempt to answer the questions based on the available information, noting where the information is absent due to the nature of the submission:

Acceptance Criteria and Study for Orthofix TL-HEX True Lok Hexapod System (TL-HEX)

The acceptance criteria for the Orthofix TL-HEX are primarily derived from the general safety and effectiveness requirements for external skeletal fixation devices and demonstrated through a comparison to predicate devices. The study performed was a series of mechanical and software tests to demonstrate substantial equivalence.

1. Table of Acceptance Criteria and Reported Device Performance

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

This information is not provided in the document. The testing described primarily involves mechanical and software validation, not a clinical "test set" in the sense of patient data. For mechanical testing, samples typically refer to units of the device components. For software testing, the "test set" would be a set of predefined inputs and scenarios used to verify software functionality, not patient data in the geographical sense.

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

This information is not applicable/provided. The evaluation focused on mechanical and software performance, not on establishing a "ground truth" based on expert interpretation of medical images or conditions for a diagnostic AI.

4. Adjudication Method for the Test Set

This information is not applicable/provided. Adjudication methods like 2+1 or 3+1 are used in clinical studies where multiple experts assess the same cases to derive a consensus ground truth. This type of methodology was not described for the mechanical and software testing performed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and effect size of how much human readers improve with AI vs without AI assistance

This information is not applicable/provided. The device is an external fixation system, including hardware and software for adjustment schedules. It is not an AI diagnostic tool primarily used for human reader assistance with image interpretation, so an MRMC study is not relevant to its stated function.

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

The document mentions a "web-based software component that is designed to be used to assist the physician in creating a patient adjustment schedule." This implies a human-in-the-loop interaction rather than a standalone AI diagnosis. However, "software verification and validation testing was completed," indicating that the software's performance was evaluated independently from a clinical human-in-the-loop scenario to ensure its functionality and accuracy in generating adjustment schedules. The results stated "the software performed as intended."

7. The Type of Ground Truth Used

For the mechanical testing, the "ground truth" would be established by the engineering specifications and expected performance under standardized test conditions (e.g., ability to withstand a certain load without deformation or fracture as defined by ASTM standards).

For the software, the "ground truth" for validation would involve comparing the software's calculated adjustment schedules against known correct calculations or engineering principles, as defined during the software design and development process. It's based on deterministic algorithms and mathematical models, rather than expert consensus, pathology, or outcomes data in the biological sense.

8. The Sample Size for the Training Set

This information is not applicable/provided. This device is not an AI system that relies on a "training set" of data for machine learning. Its software component generates adjustment schedules based on pre-programmed algorithms and patient-specific inputs, not on learning from a large dataset.

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

This information is not applicable/provided for the reasons stated above (not an AI device relying on a training set).

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