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This product is suitable for making invisible orthodontic appliances by additive manufacturing (light curing 3D printing) process. The orthodontic appliance is designed for orthodontic treatment. It uses continuous gentle force to adjust tooth position, correct malocclusion, and maintain the results of completed orthodontic treatment.

It can also be used for printing splints and night guards.

Additively Manufactured Aligner Resin is custom plastic aligner system which are a series of doctor prescribed clear removable aligners that are used as alternative treatment for the alignment of maloccluded or misaligned teeth. This series of aligners gently move the patient's teeth in small increments from their original state to a treated state.

Additively Manufactured Aligner Resin is a light-curing resin used to print orthodontic appliances. The resin is a light yellow transparent liquid. The appliance printed by a 3D printer is de-supported, cleaned and post-cured to obtain an orthodontic appliance that can correct the patient's malocclusion. This product is a light-curing 3D printing resin composed of acrylate resin oligomers and acrylate monomers as well as initiators and additives.

Additive manufacturing (light cured) orthodontic resin is a light cured acrylic resin commonly used in the manufacture of orthodontic appliances. The resin is stored in a black HDPE bottle according to the weight of the resin liquid. The color of the resin is colorless or yellowish liquid, polymerized by UV light at 385 nm or 405 nm.

The provided FDA 510(k) clearance letter details the clearance of an "Additively Manufactured Aligner Resin." This document is for a material, not a diagnostic AI device. Therefore, the information typically requested in a description of an AI device's acceptance criteria and study proving its performance (e.g., sample sizes for test and training sets, number of experts establish ground truth, MRMC studies, standalone performance) is not applicable to this submission.

The acceptance criteria and supporting studies for this material device primarily focus on bench testing (physical and mechanical properties), biocompatibility, sterility, and shelf-life. The purpose of these tests is to demonstrate that the new aligner resin is safe and effective for its intended use, comparable to already marketed predicate devices.

Here's an interpretation of the relevant information provided:

Acceptance Criteria and Performance of "Additively Manufactured Aligner Resin"

As this is a material device, the "acceptance criteria" are based on meeting established international standards for dental materials and demonstrating comparable or superior performance to existing predicate devices. The "study that proves the device meets the acceptance criteria" refers to the non-clinical bench testing, biocompatibility testing, and shelf-life testing performed.

1. Table of Acceptance Criteria and Reported Device Performance

The primary standard referenced for mechanical characteristics is ISO 20795-2:2013 Dentistry – Base polymers – Part 2: Orthodontic base polymers. The acceptance criteria for each property are implicitly defined by the requirements of this standard, and the device's performance is reported as meeting these requirements or being comparable to predicate devices.

*Note: For Flexural Modulus and Ultimate Flexural Strength, the subject device's performance was statistically significantly higher than the predicate, which is considered an improvement and not an adverse difference in terms of safety or effectiveness for the material properties. For Water Solubility and Sorption, slight differences were observed but all conformed to the ISO standard.

2. Sample size used for the test set and the data provenance

The document does not specify the exact sample sizes for each mechanical property test (e.g., number of specimens tested for flexural strength). This level of detail is typically found in the full testing report, not the 510(k) summary. However, tests were conducted as "bench testing" meaning in a laboratory setting. The data provenance is implied to be from the manufacturer's internal testing facilities, Aidite (Qinhuangdao) Technology Co., Ltd., which is based in China. The testing is retrospective in the sense that it's pre-market validation performed on manufactured material samples.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

This question is not applicable as this is a material device and not an AI or diagnostic device that requires expert-established ground truth for a test set. Ground truth for material properties is established by objective physical and chemical testing methods adhering to international standards (e.g., ISO, ASTM).

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This question is not applicable as there is no human interpretation or subjective assessment being performed that would require an adjudication method. The testing results are quantitative measurements.

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

This question is not applicable. An MRMC study is relevant for diagnostic AI devices where human readers (e.g., radiologists) interpret images with and without AI assistance. This submission is for a dental material.

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

This question is not applicable. There is no algorithm or software for standalone performance evaluation in this material device submission.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

The "ground truth" for this material device is established by objective, standardized laboratory testing methods against pre-defined specifications and international material standards (e.g., ISO 20795-2:2013). This is analogous to a "gold standard" for material properties.

8. The sample size for the training set

This question is not applicable. This is a material device, not an AI/machine learning model, and therefore does not have a "training set" in the computational sense.

9. How the ground truth for the training set was established

This question is not applicable for the same reason as point 8.

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