(252 days)
SIMPLANT 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. It is also intended as pre-planning software for dental implant placement and surgical treatment.
The proposed device SIMPLANT 18 is stand-alone software intended for pre-operative planning of dental implant placement and surgical treatment options, without patient contact. A SIMPLANT project file is created from patient image data, patient information, and implants data which are aggregated together. The SIMPLANT project file is the basis for continued implant surgical planning by dental professionals. The dental plan, which is the result of the dental implant planning process, can be used for manufacturing of a surgical guide or for evaluation of treatment options during the implant surgery procedure.
Here's an analysis of the provided text regarding the acceptance criteria and study for SIMPLANT 18:
Note: The provided document is a 510(k) summary for a medical device (SIMPLANT 18). While it outlines the software's functionality and a general approach to testing for substantial equivalence, it does not contain detailed acceptance criteria, specific performance metrics, or a formal clinical study report with detailed results, sample sizes, and expert qualifications as would be found in a comprehensive clinical trial write-up. The information presented is focused on demonstrating that the new version is substantially equivalent to a previously cleared device, rather than proving performance against specific acceptance criteria with quantifiable metrics.
Acceptance Criteria and Study for SIMPLANT 18
Based on the provided 510(k) summary, formal, quantitative acceptance criteria and a detailed clinical study demonstrating device performance against those criteria are not explicitly provided in the typical format of a clinical trial report. The document focuses on demonstrating substantial equivalence to a predicate device (SimPlant 2011) through software verification and validation activities.
1. A table of acceptance criteria and the reported device performance:
As mentioned, explicit, quantifiable acceptance criteria with corresponding performance metrics are not detailed in this document. The "performance" is generally described as the software fulfilling "all user needs and performance requirements according to the design inputs" and that functionality is confirmed through various software tests.
Acceptance Criteria (Implied) | Reported Device Performance |
---|---|
Software functionality for dental implant pre-operative planning. | Confirmed through Peer Code Review, Integration test, Internal release test, Smoke test, Formal system test, Acceptance test, Beta test. |
Accurate image segmentation and transfer from medical scanners. | Confirmed through software verification and validation activities. |
Correct functioning of new features (e.g., improved virtual teeth, immediate smile). | Confirmed through software verification and validation activities. |
Safe and effective operation as intended. | Confirmed through software verification and validation activities, and deemed substantially equivalent to predicate. |
2. Sample sized used for the test set and the data provenance:
- Sample Size for Test Set: Not specified. The document mentions "software testing" and "verification and validation testing" without detailing the number of test cases, patient datasets, or specific scenarios used in these tests.
- Data Provenance: Not specified. The origin of any data used for testing (e.g., country of origin, retrospective or prospective collection) is not mentioned. It is implied that typical medical image data (CT, CBCT) would be used for testing the functionalities.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Number of Experts: Not specified.
- Qualifications of Experts: Not specified.
The document states the "User" for both proposed and predicate devices is "Medically trained people," implying testing would likely involve such individuals, but explicit details about experts for ground truth are absent.
4. Adjudication method for the test set:
- Adjudication Method: Not specified.
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:
- MRMC Study: No. The document does not describe an MRMC comparative effectiveness study, nor does it refer to AI assistance for human readers in the context of effectiveness improvement. This device is described as "standalone software" for pre-planning, which itself is a tool for medical professionals, not explicitly an "AI assistance" system in the sense of improving human reader diagnostic accuracy in a comparative study.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Standalone Performance: Yes, implicitly. The device SIMPLANT 18 is described as "stand-alone software" whose functionalities (like image segmentation, measurement tools, planning tools) are tested through various software verification and validation activities. These tests verify the algorithm's performance and the software's functionality without necessarily a human "in-the-loop" for performance measurement in the context of a clinical study, but rather to ensure the software performs as designed for human practitioners to use.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Type of Ground Truth: Not specified. Given the nature of dental implant planning software, ground truth for functional verification would likely involve:
- Design Specifications/Requirements: The software is tested against its own design inputs.
- Reference Data/Models: For features like measurements, segmentation, and implant placement visualization, ground truth might involve known anatomical measurements, accurately segmented 3D models, or expert-defined optimal implant positions.
- Comparison to Predicate: Implicitly, the performance is benchmarked against the predicate device's known performance.
8. The sample size for the training set:
- Sample Size for Training Set: Not applicable/Not specified.
The document describes SIMPLANT 18 as software with various functionalities (segmentation, measurement, visualization, planning tools). It does not indicate that the software uses machine learning or AI models that would require a "training set" in the conventional sense of supervised learning. The changes are described as functional improvements, clarifications, and iterative updates to existing software.
9. How the ground truth for the training set was established:
- Ground Truth for Training Set: Not applicable/Not specified, as no training set for an AI model is mentioned.
§ 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).