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The HDH Treatment Planning System is intended for use as a medical front-end device providing tools for management of orthodontic models, systematic inspection, detailed analysis, treatment simulation and virtual design of a series of dental casts, which may be used for sequential aligner trays or retainers, based on 3D models of the patient's dentition. The use of the HDH Treatment Planning System requires the necessary training and domain knowledge in the practice of orthodontics, as well to have received a dedicated training in the use of the software.

The HDH Treatment Planning system is a software system for orthodontic diagnosis and treatment simulation utilized by dental professionals. The software imports patient 3D digital scans serving as a base for diagnosing the orthodontic treatment needs, analyzing, inspecting, measuring, and simulating tooth movements, and allows the user to develop a virtual treatment plan. The output of the treatment plan may be downloaded as files in STL format, a standard stereolithographic file format, or OBJ format, a standard 3D image format, for fabrication of dental casts, which may be used to fabricate sequential aligner trays or retainers.

The provided text is related to the FDA clearance of the "HDH Treatment Planning System," a software device used for orthodontic treatment planning. The application focuses on demonstrating "substantial equivalence" to a predicate device, rather than proving a specific performance level against pre-defined acceptance criteria for a medical imaging AI.

Therefore, the document does not contain the detailed information typically found in a study proving a device meets acceptance criteria for an AI/ML medical device, such as:

• A table of acceptance criteria and reported device performance (in terms of clinical metrics like sensitivity, specificity, AUC, etc.).
• Sample sizes for test sets used to assess clinical performance.
• The number and qualifications of experts used for ground truth establishment.
• Adjudication methods.
• Results of multi-reader multi-case (MRMC) studies.
• Standalone (algorithm-only) performance.
• Type of ground truth used (pathology, outcomes data).
• Sample size for the training set.
• How ground truth for the training set was established.

Instead, the document states:

• Performance Data: "Software verification and validation testing was performed in accordance with the FDA Guidance Document 'Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices' (issued May 11, 2005). All test results met acceptance criteria, demonstrating the HDH Treatment Planning System performs as intended and is substantially equivalent to the predicate devices." (Page 5)

This statement indicates that the "acceptance criteria" referred to are likely related to software verification and validation (e.g., functional testing, performance under various loads, error handling, etc.), rather than clinical performance metrics for an AI system. The basis of clearance is "substantial equivalence" to a predicate device (3Shape Ortho System K152086), meaning it performs similarly and raises no new safety or effectiveness concerns.

In summary, because this is a 510(k) submission for a non-AI/ML software device (as indicated by the application date and the "substantial equivalence" pathway description), the detailed information requested for a study proving an AI device meets acceptance criteria is not present. The "acceptance criteria" here refer to software engineering and validation standards, not clinical performance benchmarks for an AI.

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The HIT Clear Aligner system is indicated for the treatment of malocclusion in patients with permanent dentition. The HIT Clear Aligner system positions teeth by way of continuous gentle force.

A dental health care professional (e.g., orthodontist or dentist), using a standard personal computer prescribes the HIT Clear Aligner system based on an assessment of the patient's teeth, determines a course of treatment with the system, takes molds of the patient's teeth, and completes a prescription form.

The modification of the primary predicate device is a software system for orthodontic diagnosis and treatment simulation, HDH Treatment Planning software, utilized by CDB to design a series of plastic trays intended to gradually realign the patient's teeth in accordance with the physician's prescription. The software uses 3D scanned orthodontic models of a patient's dentition as input files serving as a base for diagnosing the orthodontic treatment needs, analyzing, inspecting, measuring, and simulating tooth movements, thereby allowing to create virtual treatment plans. The output of the treatment plan may be downloaded as files in STL format, a standard stereolithographic file format, or OBJ format, a standard 3D image format, for fabrication of dental casts, which may be used to fabricate sequential aligner trays or retainers.

The prescribing physician reviews and approves the treatment plan before the molds are produced. Once approved, CDB produces the trays, which are formed of clear, thin, thermoformed, copolyester plastic. The trays sent back to the dental health care professional, who then provides them to the patient, confirming fit and design. Over a period of months, additional trays are provided sequentially to the patient by the dental health professional to gradually move the target teeth to the desired position. The dental care professional monitors treatment from the moment the first aligner is delivered to when the final aligner is delivered. The trays are held in place by pressure and can be removed by the patient at any time.

This FDA 510(k) summary provides limited information regarding the acceptance criteria and the study proving the device meets those criteria, as it focuses on demonstrating substantial equivalence to a predicate device rather than presenting a standalone performance study with specific acceptance criteria.

However, based on the provided text, here's what can be extracted and inferred:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics like sensitivity, specificity, accuracy, or quantitative measurements. Instead, it describes "performance testing" in a more general sense, specifically a "process qualification" to verify the accuracy of the final product.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

The document does not specify a distinct "test set" sample size or data provenance for a performance study. The "process qualification" and "verification and validation testing" are mentioned, but details about the size and nature of the data used for these tests are absent. The focus is on the impact of the software modification rather than a new clinical performance study.

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 information is not provided. The study described is a "process qualification" and software functional verification, not a clinical study involving expert interpretation for ground truth establishment. The prescribing physician reviews and approves the treatment plan, but this is part of the clinical use, not specifically ground truth establishment for a test set.

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

This information is not provided, as a formal clinical test set requiring adjudication of ground truth is not described.

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

A multi-reader multi-case (MRMC) comparative effectiveness study is not mentioned in this document. The device is a system for producing aligners, and the software component is for treatment planning, not for aiding human readers in diagnosis or interpretation that would typically be the subject of an MRMC study.

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

A standalone performance evaluation of the algorithm itself is implicitly suggested by the "Software testing has been performed within the process qualification procedure to verify the function of the HIT Clear Aligner". However, the document does not provide details on specific standalone metrics (e.g., accuracy of tooth movement prediction in a simulated environment) or quantitative results for the algorithm without human oversight. The software provides "calculation displaying the recommended sequential aligners staging" but its standalone predictive accuracy isn't quantified.

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

For the "process qualification" and "verification and validation testing," the "ground truth" would implicitly be the preestablished specifications from the original submission (K191823) and the intended function of the software as designed. The software uses 3D scanned orthodontic models as input, and the output (STL/OBJ files for fabrication) is reviewed and approved by the "prescribing physician." Therefore, a combination of design specifications and expert (physician) review/approval forms the basis of verifying the system's output.

8. The sample size for the training set

This information is not provided. The document describes a software system for planning, not explicitly a machine learning model that would require a distinct "training set." If the software uses machine learning, the details of its training are not disclosed.

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

This information is not provided, as a training set for a machine learning model is not explicitly mentioned or described as part of this submission.

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The Clear-Aligner system is indicated for the treatment of malocclusion in patients with permanent dentition. The Clear-Aligner system positions teeth by way of continuous gentle force.

A dental health care professional (e.q. orthodontist or dentist), using a standard personal computer prescribes the Clear-Aligner system based on an assessment of thee patient's teeth, determines a course of treatment with the system, takes molds of the patient's teeth, and completes a prescription form. Utilizing standard dental software used for tooth alignment, CDB then designs a series of plastic trays intended to gradually realign the patient's teeth in accordance with the physician's prescription. The prescribing physician reviews and approves the treatment plan before the molds are produced. Once approved, CDB produces the trays, which are formed of clear, thin, thermoformed, copolyester plastic. The trays sent back to the dental health care professional, who then provides them to the patient, confirming fit and design. Over a period of months, additional trays are provided sequentially to the patient by the dental health professional to gradually move the target teeth to the desired position. The dental care professional monitors treatment from the moment the first aligner is delivered to when the final aligner is delivered. The trays are held in place by pressure and can be removed by the patient at any time. This technology is essentially identical to that used by a number of sequential alignment systems, including the predicates referenced in Table 1.

The provided text describes the acceptance criteria and study for the "Clear-Aligners" device. However, it does not detail a multi-reader multi-case (MRMC) comparative effectiveness study or a standalone algorithm performance study, as the device is a physical product (orthodontic aligners) rather than a diagnostic AI. The "performance testing" section refers to bench testing of the manufacturing process, not clinical performance for diagnostic accuracy.

Here's a summary of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly mention a "test set" in the context of clinical data, as this is a device for physical tooth alignment, not a diagnostic algorithm. The performance testing refers to bench testing for manufacturing and fit. Therefore, information on data provenance (country of origin, retrospective/prospective) for a clinical test set is not applicable or provided.

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

Not applicable. The performance testing involves "dental health professional" assessment of aligner fit, and manufacturing process consistency, not diagnostic expert consensus for a clinical ground truth.

4. Adjudication Method for the Test Set

Not applicable. There is no mention of a diagnostic test set requiring adjudication.

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 such study was conducted or is applicable to this device, as it is a physical orthodontic aligner, not an AI-powered diagnostic tool. The document focuses on the substantial equivalence of the aligners themselves to predicate devices in terms of function, materials, and manufacturing process.

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

Not applicable. The Clear-Aligners are a physical medical device. The software mentioned is for ordering and treatment planning, not for standalone diagnostic performance.

7. The Type of Ground Truth Used

For the functional characteristics (fit, process consistency), the "ground truth" seems to be the pre-established internal specification and acceptance criteria, along with assessment by a dental health professional. For biocompatibility, the ground truth is established by the results of standardized biological tests (Cytotoxicity, Sensitization, Irritation).

8. The Sample Size for the Training Set

Not applicable. There is no "training set" in the context of an AI/ML algorithm for this physical device.

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

Not applicable.

Overall Study Information:

The study described is primarily focused on bench testing to ensure the manufacturing accuracy and functional characteristics of the Clear-Aligners, and biocompatibility testing of the materials used. The approach taken is to demonstrate substantial equivalence to already legally marketed predicate devices (Clear Correct and Invisalign) based on similar indications for use, technological characteristics, mechanism of action, materials, and performance. The document explicitly states: "Further testing was not performed given there is sufficient information available from the scientific literature and from the predicate devices to demonstrate that sequential aligners provide reasonable assurance of substantial equivalence."

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The CDB Self Ligating 8F bracket is indicated for orthodontic movement of uncompromised natural teeth, excluding mandibular bicuspid.

CDB Self Ligating Bracket 8F is bonded to teeth to apply pressure to the tooth, transmitted through a flexible orthodontic wire to alter the tooth position. The modified orthodontic ceramic bracket has both aesthetic and self-ligating qualities. The modifications were aimed at facilitating easier orthodontic wire placement and removal through self-ligation and enhancing the bonding and debonding characteristic of the bracket.

The provided document is a 510(k) premarket notification for a medical device (CDB Self Ligating Bracket 8F), which primarily demonstrates substantial equivalence to existing predicate devices rather than proving independent performance against acceptance criteria through a clinical study.

Therefore, the document does not contain the information requested for a detailed breakdown of acceptance criteria and the study that proves the device meets them, especially in the context of an AI/algorithm-driven device.

Here's why and what information is available:

• Type of Device: The CDB Self Ligating Bracket 8F is an orthodontic bracket, a physical medical device. It is not an AI or algorithm-driven diagnostic or therapeutic device. The performance testing conducted is engineering-focused (e.g., comparative shear bond strength testing), not clinical performance metrics like sensitivity, specificity, or reader improvement.
• Regulatory Pathway: A 510(k) submission establishes substantial equivalence to a legally marketed predicate device. This typically involves demonstrating that the new device has the same intended use, similar technological characteristics, and is as safe and effective as the predicate. It usually does not require a full de novo clinical performance study against pre-defined acceptance criteria for diagnostic accuracy, as would be expected for an AI device.

However, based on the limited information about "performance testing" provided, here's what can be extracted, reformatted to align with your request, but emphasizing the lack of clinical study data for AI:

1. Table of Acceptance Criteria and Reported Device Performance (as inferred from the document's limited scope):

Regarding the other requested points (which are highly relevant to AI/algorithm studies but not explicitly described for this physical device's 510(k) submission):

2. Sample size used for the test set and the data provenance: Not applicable in the context of this device's "performance testing." The "comparative shear bond strength testing" likely involved a certain number of brackets tested in a lab, but the document does not specify the sample size or provenance of "data" in the sense of clinical cases for an AI model.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for an orthodontic bracket's performance is typically established through engineering standards and physical measurements, not expert clinical interpretation of images or patient outcomes for diagnostic accuracy.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable.
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, as this is not an AI device.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable, as this is not an AI device.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): For the "performance testing," the ground truth was likely engineering measurements (e.g., force required to shear the bond).
8. The sample size for the training set: Not applicable. There is no AI model or "training set" for this physical device.
9. How the ground truth for the training set was established: Not applicable.

In summary, the provided document is a regulatory submission for a physical medical device (orthodontic bracket) under the 510(k) pathway, focusing on demonstrating equivalence to predicate devices rather than a clinical trial proving performance against predefined clinical acceptance criteria for an AI or diagnostic algorithm.

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The CDB Flip-Slide-Clip Bracket is indicated for orthodontic movement of natural teeth.

CDB Flip-Slide-Clip Brackets are bonded to teeth to apply pressure to the tooth, transmitted through a flexible orthodontic wire, to alter the tooth position. The modified orthodontic ceramic bracket has both aesthetic and self-ligating qualities. The modifications were aimed at facilitating easier orthodontic wire ligation

This FDA 510(k) submission for the "CDB Flip-Slide-Clip Bracket" is a premarket notification for a Class II medical device. As such, it focuses on demonstrating substantial equivalence to existing legally marketed predicate devices, rather than proving the device meets specific performance acceptance criteria through clinical studies. Therefore, the information you're requesting regarding detailed acceptance criteria, study designs, sample sizes, expert involvement, and comparative effectiveness studies is not typically present in this type of submission.

Here's an assessment based on the provided document:

Analysis of Acceptance Criteria and Device Performance Study

The provided document describes a 510(k) submission for an orthodontic bracket. The primary goal of a 510(k) submission is to demonstrate substantial equivalence to a predicate device, not necessarily to prove specific performance metric acceptance criteria through new clinical efficacy studies. Therefore, the submission relies on the established safety and effectiveness of the predicate devices.

1. Table of Acceptance Criteria and Reported Device Performance

No explicit quantitative acceptance criteria or detailed performance metrics are provided in this 510(k) submission. The core "acceptance criteria" here is the FDA's determination of substantial equivalence to legally marketed predicate devices.

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

• Sample Size for Test Set: Not applicable. This submission does not describe a clinical "test set" for performance evaluation in the way you might expect for a diagnostic or AI device. The comparison is primarily based on design specifications, material equivalency, and functional principles relative to the predicate devices.
• Data Provenance: Not applicable. No new clinical performance data from a test set is presented. The "data" provenance would relate to the established safety and effectiveness of the cited predicate devices.

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

• Number of Experts: Not applicable. No ground truth was established by experts for a novel test set performance evaluation. The "ground truth" here is effectively historical: the predicate devices have established their safety and effectiveness through their regulatory history and market use.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. No performance adjudication of a new test set was conducted for this 510(k) submission.

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

• MRMC Study: No, an MRMC comparative effectiveness study was not done. Such studies are typically reserved for devices where the performance difference between a new device (especially AI-assisted) and standard practice is being quantified, which is beyond the scope of a substantial equivalence claim for a mechanical bracket.
• Effect Size of Human Readers Improvement with AI vs. Without AI: Not applicable, as this is not an AI-based device and no such study was conducted.

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

• Standalone Study: Not applicable. This device is a mechanical orthodontic bracket, not an algorithm. Therefore, no standalone algorithmic performance study was conducted or is relevant.

7. The Type of Ground Truth Used

• Type of Ground Truth: The "ground truth" in this context is the established safety and effectiveness profile of the predicate devices currently on the market. This is based on their historical regulatory clearances, clinical use, and performance data accrued over time, rather than a prospectively established gold standard for a new device's performance.

8. The Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. This is not an AI/machine learning device, so there is no concept of a "training set" for an algorithm.

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

• How Ground Truth for Training Set Was Established: Not applicable, as there is no training set for an algorithm.

In summary: This 510(k) submission is for a modified mechanical device. Its acceptance hinges on demonstrating that it is as safe and effective as existing devices (predicates) through a comparison of intended use, technological characteristics, and materials. It does not involve new clinical performance studies with specific acceptance criteria, test sets, or expert ground truthing as would be associated with the evaluation of a new diagnostic or AI-driven medical device.

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The CDB Clip, commonly known as an orthodontic ligating clip, is an orthodontic bracket accessory. It attaches to the orthodontic bracket to hold the archwire firmly in the slot. Together they facilitate the orthodontic movement of teeth.

The CDB Clip is a passive ligation clip which functions to retain the orthodontic archwire in the slot of an orthodontic bracket. It is made of Celcon MT 24U01 Acetal (POM) with no fillers.

The provided text describes a 510(k) summary for the "CDB Clip - Orthodontic Bracket Accessory". This is a medical device submission, and the information presented focuses on demonstrating substantial equivalence to a predicate device rather than presenting a performance study with detailed acceptance criteria and results.

Therefore, many of the requested items (e.g., sample size for test set, data provenance, number of experts, adjudication method, MRMC study, standalone performance, ground truth types and training set details) are not applicable or not provided in this type of regulatory document.

Here's a breakdown based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Not Applicable / Not Provided. This document is a 510(k) summary for a Class II orthodontic device. The demonstration of substantial equivalence for such devices typically relies on comparative analysis of design, materials, and intended use with a legally marketed predicate, rather than a clinical performance study with a "test set" as one might find for diagnostic AI software. The claim is that the device is identical in function and performance to the predicate.

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

• Not Applicable / Not Provided. See point 2. There was no "ground truth" to establish for a test set in the context of this 510(k) submission.

4. Adjudication method for the test set:

• Not Applicable / Not Provided. See point 2.

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 / Not Provided. This device is an orthodontic bracket accessory, not an AI-powered diagnostic tool. Therefore, an MRMC study and AI assistance metrics are irrelevant to this submission.

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

• Not Applicable / Not Provided. This is a physical, passive mechanical device, not an algorithm.

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

• Not Applicable / Not Provided. The "ground truth" for this submission is essentially the established safety and effectiveness of the legally marketed predicate device (NEO-CLIP ORTHODONTIC BRACKET ACCESSORY, K042905). The applicant is demonstrating that their device is sufficiently similar to that predicate.

8. The sample size for the training set:

• Not Applicable / Not Provided. This device is not an AI/ML algorithm that requires a "training set."

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

• Not Applicable / Not Provided. See point 8.

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