(122 days)
Implant 3D 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 CAT scanner. It is also intended as pre-planning software for dental implant placement.
Implant 3D is a software that allows to perform three-dimensional implant simulation directly on the PC. It enables to simulate the position of the implants in bi-dimensional and three-dimensional models. It also consent to identify the mandibular canal and to draw overviews and sections of the bone mode. Implant 3D enables to view the three dimensional bone model with the possibility to provide a qualitative indication of bone density. Implant 3D generates the overview, the sections and the three-dimensional bone model by reading the axial images.
This 510(k) submission for IMPLANT 3D does not contain the detailed information necessary to fully answer your request regarding acceptance criteria and a study proving the device meets those criteria. The provided document is a summary of the 510(k) submission, focusing on establishing substantial equivalence to a predicate device rather than presenting a standalone performance study with specific acceptance criteria.
Here's an breakdown of what can and cannot be answered based on the provided text:
1. A table of acceptance criteria and the reported device performance
- Cannot be fully provided. The document states that "MEDIA LAB S.R.L. has conducted laboratory testing and determined device functionality and conformance to design input requirements" and "The device has been designed and validated in such a way that, when used under the conditions and for the purposes intended, it will not compromise the clinical condition or the safety of patients, or the safety and health of users or other people...". However, it does not specify any quantitative acceptance criteria or corresponding reported performance metrics for the device's clinical functionality (e.g., accuracy of implant simulation, segmentation accuracy of the mandibular canal, or reliability of bone density indication). The "Software testing" section lists general testing types (Unit, Integration, IR, Smoke, Formal, Acceptance, Alpha, Beta testing) but does not provide specific performance outcomes against measurable criteria.
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Cannot be provided. The document does not mention any sample sizes for a test set, nor does it describe the provenance of any data used for testing.
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)
- Cannot be provided. The document does not describe any specific "test set" in the context of clinical performance evaluation that would require expert-established ground truth.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Cannot be provided. No adjudication method is mentioned, as there is no described test set requiring this.
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
- Cannot be provided. The document does not describe an MRMC study or any study comparing human reader performance with and without AI assistance. The device is a "software interface and image segmentation system" and "pre-planning software," implying a tool for clinicians, but no a comparative effectiveness study is presented to measure its impact on human reader performance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Partially addressed, but no specific performance data is given. The document implies standalone software testing ("Unit testing", "Integration testing", etc.) was conducted to confirm functionality and conformance to design input requirements. However, it does not provide any quantitative results from such standalone performance tests relevant to clinical metrics. The device itself is software (algorithm only) that assists a human user.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- Cannot be provided. Since no specific clinical performance study with annotated data is described, the type of ground truth used is not mentioned.
8. The sample size for the training set
- Cannot be provided. No information about a training set for an algorithm is provided. The document focuses on the functionality of the software for simulation and segmentation, rather than a machine learning model that would require a distinct training set.
9. How the ground truth for the training set was established
- Cannot be provided. As no training set is mentioned, the method for establishing its ground truth is also not provided.
Summary of available information:
The provided document describes IMPLANT 3D as a software for 3D implant simulation and pre-planning for dental implant placement, functioning as an image segmentation system. It establishes substantial equivalence to the SIMPLANT 2011 predicate device based on intended use, fundamental technology, and operation characteristics. The document states that "laboratory testing and determined device functionality and conformance to design input requirements" were conducted and that the "device has been designed and validated... it will not compromise the clinical condition or the safety of patients." General software testing types (Unit, Integration, IR, Smoke, Formal, Acceptance, Alpha, Beta testing) are listed.
In essence, this 510(k) summary focuses on regulatory substantial equivalence and general safety/functionality assertions, rather than presenting detailed clinical performance studies with quantitative acceptance criteria and supporting data. This is common for certain Class II devices seeking 510(k) clearance, where substantial equivalence to a legally marketed predicate can be demonstrated without comprehensive clinical trials, especially if the new device shares the same fundamental technology and intended use.
§ 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).