(161 days)
SimPlant Navigator Personalized Dental Care System is intended for use as a software interface and image segmentation system for the transfer of imaging information from a medical scanner such as a CT scanner or a Magnetic Resonance scanner. It is also intended as pre-planning software for dental implant placement and surgical treatment. SurgiGuide® guides and the BIOMET 3i Navigator Surgical Kit, which are used intra-operatively to prepare the osteotomy for placement of BIOMET 3i implants pre-operatively determined in the software.
SimPlant Navigator Personalized Dental Care System provides a method of importing medical imaging information from radiological imaging systems such as Computer Tomography (CT) or Magnetic Resonance Imaging (MRI) to a computer file that is usable in conjunction with other diagnostic tools and expert clinical judgment. Visual representations of the imaged anatomical structures (e.g. the jaw) are derived allowing for a three-dimensional assessment of the patient without patient contact. Dental implant positions including orientations are planned pre-operatively. Computer visualization of the 3D anatomical jaw models, planned implants, planned tooth setup, and numerical measurements assist the surgeon in the creation and approval of a pre-surgical plan. SurgiGuide® guides and the BIOMET 3i Navigator Surgical Kit are used intra-operatively to prepare the osteotomy for placement of BIOMET 3i implants pre-operatively determined in the software.
Here's a breakdown of the acceptance criteria and study information based on the provided 510(k) summary:
Note: The provided document is a 510(k) summary, which often focuses on establishing substantial equivalence to a predicate device rather than presenting detailed clinical study results with specific acceptance criteria and performance against those criteria in a tabular format. The document highlights software and bench testing for compatibility, but not a standalone clinical performance study with defined metrics. Therefore, some sections below will indicate "Not explicitly stated" or will infer information based on the presented context.
1. Table of Acceptance Criteria and Reported Device Performance
The provided document does not include a table of explicit acceptance criteria with specific performance metrics (e.g., accuracy, sensitivity, specificity, or quantitative error bounds) and corresponding reported device performance values. The performance data section broadly mentions "Software Validation in addition to bench top performance testing was conducted to ensure the compatibility of all system components."
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: Not explicitly stated. The document refers to "Software Validation" and "bench top performance testing" without detailing the specific datasets or number of cases used for these tests.
- Data Provenance (e.g., country of origin, retrospective/prospective): Not explicitly stated.
3. Number of Experts Used to Establish Ground Truth and Qualifications
- Number of Experts: Not explicitly stated.
- Qualifications of Experts: Not explicitly stated.
4. Adjudication Method for the Test Set
- Adjudication Method: Not explicitly stated.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- MRMC Study Conducted?: No. The document does not mention any MRMC study comparing human reader performance with or without AI assistance.
- Effect Size of Human Reader Improvement: Not applicable, as no MRMC study was reported.
6. Standalone (Algorithm Only) Performance Study
- Standalone Study Conducted?: Yes, to a degree. The "Software Validation" and "bench top performance testing" could be interpreted as standalone performance evaluations of the software's functionality and compatibility. However, specific metrics of clinical performance (e.g., accuracy of implant placement prediction) were not provided from such a study. The focus is on ensuring the software correctly performs its intended functions (image segmentation, pre-operative planning, transfer of information).
7. Type of Ground Truth Used
- Type of Ground Truth: Not explicitly stated for specific metrics. The software's function involves processing medical images (CT/MRI) for 3D reconstruction and pre-operative planning. The "acceptance testing" and "formal testing" likely validated the software's output against expected computational results or pre-defined clinical scenarios, but the source of the "ground truth" for these comparisons is not detailed. Given the nature of the device (planning software), ground truth would typically relate to the accuracy of anatomical visualization and the precise positioning of planned implants relative to a known anatomical model or expert-defined optimal plan.
8. Sample Size for the Training Set
- Sample Size for Training Set: Not applicable. This device is explicitly described as an "Image processing system" leveraging "Visual representations" and "numerical measurements" for pre-operative planning based on CT/MRI data. It's a software tool for clinicians to use, not a machine learning or AI-driven diagnostic algorithm that would typically require a training set in the conventional sense for learning patterns from data. The software's design is based on established imaging and anatomical principles, rather than being "trained" on a large dataset of patient cases to learn to identify patterns or make predictions.
9. How Ground Truth for the Training Set Was Established
- Ground Truth for Training Set: Not applicable, as this device does not appear to utilize a training set in the context of machine learning or AI. Its functionality is based on deterministic algorithms for image processing and 3D visualization.
§ 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).