(308 days)
ImplaStation is stand-alone software designed for trained qualified dental practitioners, including dental technicians.
The software can be used to visualize a patient's medical image dataset output in DICOM format from third-party CT/ CBCT scanners.
ImplaStation is intended for use as a pre-operative tool for the dental implant(s) positioning based on the CT/CBCT image dataset aligned to optical 3D surface scan(s) and for the surgical guide planning result file creation. The surgical guide can be manufactured using a planning result file when used as input to 3D manufacturing systems.
3D manufacturing is out of ImplaStation software control, depends on many external factors and lie within the sole responsibility of the user.
ImplaStation is stand-alone software designed for trained qualified dental practitioners.
The key scientific concept (807.92(a)(4)) of the ImplaStation software is the visualization of a patient's medical image data (DICOM file from third-party CT/CBCT scanners) to pre-operative digital implant planning, surgical guide (drill guide) file (output of the pre-operative implant planning) creation.
The data acquired by the optical scanner (scanned surface of the maxilla or mandible) can be aligned to the CT/CBCT reconstruction through a point-based registration technique.
Virtual crown(s) design and nerve tracing can be used as additional tools to assist the specialist during an implant planning process.
The ImplaStation library contains implant, abutment, drill, and sleeve files, which are encrypted files and approved by the corresponding implant manufactures. The software allows designing the surgical guide (drill guide) file and exporting the generated file to a 3th party external system for manufacturing. The device has no patient contact, nor does it control any life-sustaining devices.
Here's a summary of the acceptance criteria and study information for the ImplaStation device, based on the provided document:
1. Table of Acceptance Criteria and Reported Device Performance:
The provided document does not explicitly state specific numerical acceptance criteria for the ImplaStation device's performance. Instead, it relies on a comparison to a predicate device and general software validation principles. The performance data is primarily demonstrated through non-clinical testing to ensure the software functions as intended and is substantially equivalent to the predicate.
| Feature/Metric | Acceptance Criteria (Implied/General) | Reported Device Performance (ImplaStation) |
|---|---|---|
| Overall Functionality | Functions as intended, similar to the predicate device, for pre-operative dental implant planning and surgical guide creation. | Nonclinical verification and validation testing performed. ImplaStation functions as intended, providing visualization of medical image datasets, pre-operative digital implant planning, and surgical guide file creation. Key functionalities (image registration, project management, case visualization, DICOM/STL processing, measurement tools, nerve tracing, virtual wax-up, implant planning tools, surgical guide design, surgical protocol design) are present. |
| Safety and Effectiveness Profile | Similar to the predicate device (CoDiagnostiX Implant Planning Software K130724). No additional concerns regarding safety and effectiveness compared to the predicate, despite minor differences. | Found to have a safety and effectiveness profile similar to the predicate device. The difference in bone density measurement (ImplaStation does not offer it) is stated not to raise additional concerns as CBCT bone density measurements are not reliable. |
| Compliance with Standards | Adherence to relevant medical device software and quality management standards. | Complies with IEC 62304, ISO 13485, ISO 14971, IEC 80001-2-2, NEMA PS 3.1-3.20. |
2. Sample Size Used for the Test Set and Data Provenance:
- Sample Size for Test Set: The document does not specify a distinct "test set" size in terms of patient cases or images for the nonclinical testing. The validation processes mentioned are general software engineering practices rather than a clinical performance study with a specific dataset.
- Data Provenance: Not explicitly stated for specific test data. The input sources for the software are stated as medical image datasets in DICOM format from third-party CT/CBCT scanners and optical 3D surface scans (.stl files). There is no mention of country of origin or whether data was retrospective or prospective.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:
- This information is not provided in the document. The nonclinical testing focuses on software functionality validation against predetermined requirements rather than a clinical "ground truth" derived from expert consensus on patient cases.
4. Adjudication Method for the Test Set:
- This information is not applicable/not provided as the document describes nonclinical software validation, not a study involving human readers and adjudicated outcomes.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
- No, an MRMC comparative effectiveness study was not done. The document explicitly states: "Clinical testing was not needed to support a claim of Substantial Equivalence to the predicate device." Therefore, there is no discussion of human reader improvement with or without AI assistance.
6. Standalone Performance Study:
- Yes, a standalone (algorithm only) performance assessment was done in the form of "Nonclinical verification and validation testing." This testing was performed to "ensure that the ImplaStation subject to this 510(k) Premarket Notification functions as intended." This refers to evaluating the software's functionality, adherence to requirements, and internal consistency. However, it's not a standalone clinical performance study in the sense of evaluating diagnostic accuracy against clinical ground truth.
7. Type of Ground Truth Used:
- For the nonclinical testing, the "ground truth" was essentially the pre-defined software requirements, specifications, and expected functionality. The validation aimed to confirm that the software behaved as designed and met these internal criteria, rather than comparing its output to external clinical ground truth (e.g., pathology, outcomes data, or expert consensus on patient cases).
8. Sample Size for the Training Set:
- This information is not provided. The document describes a medical image management and processing system and planning software, not a machine learning or AI algorithm that typically requires a distinct training set. While the software utilizes medical image data, it's presented as a tool for visualization and planning, implying rule-based or deterministic algorithms rather than trainable models with specific training data.
9. How the Ground Truth for the Training Set Was Established:
- This information is not applicable/not provided for the reasons stated above. As there is no mention of a traditional "training set" for a machine learning model, the concept of establishing ground truth for it doesn't apply within this document's scope.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).