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510(k) Data Aggregation
(40 days)
Orthofix True Lok Hexapod System VI.3
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, 5/8 and 3/8 aluminum Rings
Double Row Footplates
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
This document is a 510(k) premarket notification for a medical device called the Orthofix TL-HEX True Lok Hexapod System (TL-HEX) V1.3. It's a submission to the FDA to demonstrate that the new version of the device is substantially equivalent to a previously cleared predicate device.
The prompt asks for details about acceptance criteria and a study proving the device meets these criteria as if this were an AI/software device. However, this document is for a mechanical external fixation system and its associated software for calculating adjustments, not an AI or diagnostic imaging device. Therefore, many of the requested categories (like "human readers improve with AI," "standalone performance," "ground truth type," "training set size") are not applicable to this type of device and submission.
The "study" in this context refers to non-clinical performance testing to demonstrate the safety and effectiveness of the mechanical components and the proper functioning of the software.
Here's an attempt to answer the questions based on the provided document, adapting where necessary given the nature of the device:
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria (based on tests and standards) | Reported Device Performance |
|---|---|
| Mechanical Performance: Withstand expected loads without failure (conformance to ASTM F 1541-02 (2007)e1 "Standard Specification and Test Methods for External Skeletal Fixation Devices") | "A review of the mechanical data indicates that the hardware components of the Subject device are capable of withstanding expected loads without failure." "All testing met or exceeded the requirements as established by the test protocols and applicable standards." |
| Software Performance: Software functions as intended (conformance with FDA's guidance document "General Principles of Software Validation; Final Guidance for Industry and FDA Staff," including Software IQ, OQ, PQ) | "Additionally, software verification and validation testing was completed in conformance with [FDA guidance]. The results of software testing indicate that the software performed as intended." |
| Risk Management: Potential hazards evaluated and controlled (conformance with a Risk Management Plan) | "The potential hazards have been evaluated and controlled through a Risk Management Plan." |
| Substantial Equivalence: Maintain intended use, indications for use, technological characteristics, and labeling compared to the predicate device, considering the extended range of components and software update. These are the underlying "acceptance criteria" for a 510(k) in general. | "Documentation was provided to demonstrate that the subject device is substantially equivalent to the Predicate Orthofix TL-HEX True Lok Hexapod System (TL-HEX) (K141078). The subject device is substantially equivalent to the predicate device in intended use, indications for use, technological characteristics, and labeling. The subject device includes an extended range of rings, footplates, struts, and a consequent update of the software." "Based on the indications for use, technological characteristics, materials, and comparison to predicate devices, the Subject Orthofix TL-HEX True Lok Hexapod System (TL HEX) has been shown to be substantially equivalent to legally marketed predicate device, and is safe and effective for its intended use." |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Test Set Sample Size: Not explicitly stated as a "sample size" in the context of patient data, as this is primarily a mechanical and software validation. The mechanical testing would have involved a specific number of device components tested per protocol (e.g., several samples of each new component type). Software testing would involve comprehensive test cases to cover all functionalities.
- Data Provenance: Not applicable in the sense of patient data origin or clinical study type. The reference to "ASTM F 1541-02 (2007)e1" for mechanical testing implies standard laboratory testing. Software testing is internal verification and validation.
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)
- This question is not applicable to this type of device submission. "Ground truth" in a diagnostic or AI context typically refers to clinical diagnosis or expert annotations. For this submission, standards and protocols (like ASTM F 1541-02 for mechanical, and FDA guidance for software validation) served as the "ground truth" for evaluating performance and compliance. The experts involved would be engineers conducting the tests and quality/regulatory personnel reviewing the results against the established acceptance criteria.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Not applicable. This type of submission does not involve adjudication of clinical cases or expert opinions. Performance is assessed against predefined engineering and software validation criteria.
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 not an AI diagnostic imaging device. It's an external fixation system with software to assist in calculating strut adjustments for limb reconstruction. Clinical data was explicitly stated as "not needed to support the safety and effectiveness" for this 510(k) submission, as substantial equivalence was demonstrated through non-clinical testing.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- The software component of the device is described as "designed to be used to assist the physician in creating a patient adjustment schedule." This implies it's an assistive tool for a human-in-the-loop process. The software's performance was validated in a standalone capacity (i.e., its calculations and functionalities were verified independently) as part of "software verification and validation testing" to ensure it performs "as intended." However, it's not an "algorithm only" in the sense of a fully automated diagnostic or treatment decision system.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
- For mechanical testing: The "ground truth" is adherence to the specified performance requirements of the ASTM standard (ASTM F 1541-02 (2007)e1), which define acceptable load-bearing capacities and failure modes for skeletal fixation devices.
- For software testing: The "ground truth" is that the software performs its calculations correctly, adheres to its functional specifications, and meets the requirements of the FDA's "General Principles of Software Validation" guidance. This would be established through a series of defined test cases with known expected outputs.
8. The sample size for the training set
- Not applicable. This is not an AI/machine learning device that requires a "training set" in the conventional sense. The software's logic is deterministic (calculating adjustments based on user input and pre-programmed algorithms), not learned from data.
9. How the ground truth for the training set was established
- Not applicable, as there is no "training set" for this device. The software "ground truth" for its development would be based on biomechanical principles, engineering equations, and clinical requirements for hexapod system adjustments.
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