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The LightForce Orthodontic System is a treatment planning software and orthodontic appliance system used to correct malocclusions in orthodontic patients using patient-matched orthodontic appliances.

The LightForce Orthodontic System (LFO System) is a treatment planning software and orthodontic appliance system used to correct malocclusions in orthodontic patients using patient-matched orthodontic appliances. The LFO System consists of patient-specific ceramic brackets, patient-specific placement jigs, and TPS that allows the orthodontist to view, measure, and diagnose cases.

The patient-specific brackets are available with slot sizes to orthodontic wires up to 0.022" in height and come in a variety of hook options colors and shades. The patient-specific placement jig is an optional-use lab device to aid orthodontists in bracket placement and bonding and is composed of an FDA cleared, class 1 certification material. The LFO system does not contain commercially available or patient-specific shaped arch wires, ligatures, or adhesives that affix the brackets to the teeth. The LFO system packaging consists of a single product offering box with indirect bonding trays (IDB trays) loaded with brackets as well as a secondary set of individual brackets and, where applicable IDB trays and/or bite turbo indirect bonding travs. The LFO system may also include non-patient-specific brackets for temporary use with predetermined torque, tip, offset and base contour, packaged and labeled separately.

The inclusion of an optional self ligating metal clip is to ligate a wire to the bracket by encapsulating the wire in the slot of the bracket.

This document is a 510(k) summary for the LightForce Orthodontic System (LFO System). The purpose of this summary is to demonstrate substantial equivalence to legally marketed predicate devices. It primarily focuses on the addition of an optional self-ligating nitinol clip to the existing LFO System.

Here's an analysis of the provided information concerning acceptance criteria and the study proving the device meets them, organized as requested:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of quantitative acceptance criteria with corresponding performance metrics for the overall device. Instead, it describes performance testing conducted to assess the differences introduced by the new self-ligating clip.

The key performance characteristics evaluated and their qualitative "performance" against the predicate are:

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

• Sample Size for Test Set: The document does not specify the sample sizes (e.g., number of brackets, number of tests) used for the non-clinical performance testing (tie-wing fracture, wire friction, wire escapement).
• Data Provenance: The data is from "Non-clinical Performance Testing" and "Biocompatibility," implying lab-based, retrospective testing performed by the manufacturer to support the 510(k) submission. There is no mention of country of origin of the data, but presumably it was conducted in the US or under standards accepted by the US FDA.

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

This section is not applicable as the testing described is non-clinical performance testing of physical characteristics and biocompatibility, not a clinical study involving human patients or expert interpretation of medical images/diagnoses. The "ground truth" here is established by the results of the physical and chemical tests based on established engineering and materials science principles and standards.

4. Adjudication Method for the Test Set

This section is not applicable for the same reasons as point 3. Adjudication methods are typically used in clinical studies or studies involving human judgment to establish a consensus "ground truth."

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

• No MRMC study was done. The document explicitly states: "No clinical performance testing was conducted on LFO System brackets."
• No AI component that would involve human readers. This device is a physical orthodontic system (brackets and treatment planning software) for correcting malocclusions, not an AI-powered diagnostic imaging tool that would assist human readers in interpreting images.

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

• No standalone algorithm performance study was done in the context of an AI/ML algorithm. The "LFO System" includes "TPS [treatment planning software] that allows the orthodontist to view, measure, and diagnose cases." However, the 510(k) summary focuses on the mechanical and material changes to the brackets (specifically the addition of the self-ligating clip) and does not describe performance testing related to the software's diagnostic or planning capabilities as a standalone algorithm. The software is part of the system that "allows the orthodontist to view, measure, and diagnose cases," implying a human-in-the-loop for diagnosis, not a standalone automated diagnosis.

7. The Type of Ground Truth Used

• For non-clinical performance testing (tie-wing fracture strength, wire friction, wire escapement): The ground truth is defined by the physical measurements obtained from mechanical testing rigs, compared against predefined engineering specifications or performance of the predicate device.
• For biocompatibility: The ground truth is established by the results of standardized biological assays and chemical characterization, evaluated against the requirements of international standards (ISO 10993-1:2018) and US FDA guidance.

8. The Sample Size for the Training Set

This section is not applicable. The device is an orthodontic appliance system, not an AI/ML device that requires a training set for model development. The "training" in this context refers to manufacturing processes, material selection, and design validation, not machine learning model training.

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

This section is not applicable for the same reasons as point 8.

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