Search Results
Found 1 results
510(k) Data Aggregation
(90 days)
ULab Systems uDesign Software
The uLab Systems uDesign 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 dental casts, which may be used for sequential aligner trays or retainers, and of Indirect Bonding Transfer Media, based on 3D models of the patient's dentition before the start of an orthodontie treatment. It can also be applied during the treatment to inspect and analyze the progress of the treatment. It can be used at the treatment to evaluate if the outcome is consistent with the planned desired treatment objectives. The use of ul ab Systems uDesign requires the user to have 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 ULab Systems UDesign is orthodontic diagnosis and treatment simulation software for use by dental professionals. UDesign imports patient 3-D digital scans and allows the user to diagnose the orthodontic treatment needs of the patient and rapidly develop a treatment plan. The treatment plan may be downloaded as files in standard stereolithographic (STL) format for fabrication of dental casts, which may be used to fabricate sequential aligner trays or retainers, and of indirect bonding transfer trays.
The provided document is a 510(k) Premarket Notification from the U.S. Food & Drug Administration (FDA) for the ULab Systems uDesign Software. This document focuses on establishing substantial equivalence to a predicate device rather than detailing specific performance studies with rigorous acceptance criteria typical for AI/ML devices that make new diagnostic claims or require clinical performance validation.
Based on the provided text, the device is a "medical front-end device providing tools for management of orthodontic models, systematic inspection, detailed analysis, treatment simulation and virtual design of dental casts." It is not an AI/ML device making diagnostic or prognostic claims requiring clinical performance studies with acceptance criteria based on sensitivity, specificity, or AUC. Instead, it is a software device intended for use by dental professionals for orthodontic treatment planning and design, and its performance validation revolves around software verification and validation.
Therefore, the information regarding acceptance criteria and performance study details as requested (e.g., sample size for test set, number of experts for ground truth, MRMC study, standalone performance) is not explicitly present in the provided document because the nature of the device and its regulatory pathway (510(k) for substantial equivalence of software tools) do not typically require such detailed clinical performance studies.
However, I will extract what information is available regarding performance and acceptance criteria and explicitly state where the requested information is not provided.
Device: ULab Systems uDesign Software
Regulatory Pathway: 510(k) Premarket Notification
1. Table of Acceptance Criteria and Reported Device Performance
As this is a 510(k) for software tools rather than a diagnostic AI/ML algorithm, the "acceptance criteria" discussed are related to software verification and validation, not clinical performance metrics like sensitivity or specificity.
| Acceptance Criterion (Implicit from V&V) | Reported Device Performance |
|---|---|
| Software Functionality & Reliability | The document states: "All test results met acceptance criteria, demonstrating the uLab Systems uDesign performs as intended." This indicates successful functional testing, integration testing, and reliability testing. |
| Hazard Mitigation (Risk Management) | "The testing includes validation of implemented mitigations related to device hazards identified in the risk management procedures." This implies that the software adequately addresses identified risks, meeting the acceptance criteria for safety and risk controls. |
| Substantial Equivalence | "demonstrates that the device should perform as intended in the specified use conditions. Therefore, the ULab Systems UDesign is substantially equivalent to the cleared predicate devices." This is the ultimate regulatory acceptance criterion for a 510(k), confirming the device's technical characteristics and intended use are similar to a legally marketed predicate and do not raise new questions of safety or effectiveness. |
2. Sample Size Used for the Test Set and Data Provenance
The document does not specify the sample size for a test set in the context of clinical or performance data (e.g., number of patient cases or 3D models used for a performance evaluation). The testing mentioned is "Software and integration verification and validation testing." This typically involves testing scenarios, functions, and workflows, rather than a fixed "test set" of patient data in the same way an AI model would be evaluated.
Data provenance (e.g., country of origin, retrospective/prospective) is also not applicable or specified, as the evaluation is not a clinical study involving patient data collection for performance assessment.
3. Number of Experts Used to Establish Ground Truth and Qualifications
This information is not provided. The "ground truth" for this type of software (an orthodontic design tool) is its ability to accurately execute its functions (e.g., allowing precise measurements, correct treatment simulation, proper STL file generation). This is validated through internal software testing, not typically through expert consensus on a dataset.
4. Adjudication Method for the Test Set
This information is not provided, as it's not relevant for software verification and validation testing of a design tool. Adjudication methods are typically used in clinical studies where expert readers resolve discrepancies in interpretations of medical images or data.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
An MRMC study was not done. This type of study is typically conducted for diagnostic AI/ML devices to assess how the AI assists human readers in tasks like disease detection or diagnosis. The uDesign software is a tool for orthodontic planning and design, not an AI for assisting with diagnostic accuracy. Therefore, there is no effect size reported for how human readers improve with AI vs. without AI assistance.
6. If a Standalone Performance Study Was Done
A standalone performance study in the sense of an algorithm-only (without human-in-the-loop) diagnostic accuracy study was not performed. The "performance data" refers to the successful completion of software verification and validation tests. The device itself is described as "providing tools for management," indicating it is an assistive software tool for a user, not a standalone diagnostic AI.
7. The Type of Ground Truth Used
The "ground truth" for this device, in the context of its software V&V, would be the correct functional behavior of the software components. For example:
- Correct Measurement: If the software calculates a distance, the ground truth is the mathematically correct distance for the virtual model.
- Accurate Simulation: If it simulates tooth movement, the ground truth is that the simulation adheres to the programmed algorithms and orthodontic principles.
- Correct File Generation: The ground truth for STL file output is that the generated file accurately represents the virtual design and is in the correct format.
This "ground truth" is established through software engineering best practices, including requirements traceability, design specifications, and automated/manual testing against these specifications. It is not based on clinical outcomes, pathology, or expert consensus on clinical cases.
8. The Sample Size for the Training Set
This information is not applicable and therefore not provided. The uLab Systems uDesign software is described as a "medical front-end device providing tools" and as "orthodontic diagnosis and treatment simulation software." The document does not indicate that it is an AI/ML device trained on a dataset. It appears to be a rule-based or algorithmic software tool rather than one developed through machine learning. Therefore, there is no "training set" in the context of AI/ML.
9. How the Ground Truth for the Training Set Was Established
This information is not applicable, as there is no indication of a "training set" for an AI/ML algorithm.
Ask a specific question about this device
Page 1 of 1