(141 days)
Implant Studio™ is indicated for use as a medical front-end software that can be used by medically trained professionals for the purpose of visualizing gray value images. It is intended for use as a pre-operative planning software for the placement of dental implant(s) based on imported CT image data, optionally aligned to an optical 3D surface scan. Virtual Crowns can be used for optimized implant positioning under the prosthetic aspect. The digital three dimensional model of a surgical guide for a guided surgery can be designed based on the approved implant position. This 3D data can be exported to manufacture a separate physical product.
Indications of the dental implants do not change with guided surgery compared to conventional surgery.
Use of the software requires that the user has the necessary medical training in implantology and surgical dentistry.
Implant Studio™ is a software only device used to pre-operatively plan the placement of a dental implant and to visualize a patient's CT image, optionally aligned to an optical 3D surface data. Virtual crown(s) can be used to quide the planning under the final prosthetic aspect. The surgical guide data can be designed then exported to an external system for manufacturing.
The device has no patient contact.
The provided text describes the 3Shape Implant Studio, a medical planning software. However, it does not contain a detailed study with acceptance criteria and reported device performance in a table format.
Here's an analysis of the available information:
1. Table of Acceptance Criteria and Reported Device Performance:
The document states: "Prior to release, verification and validation testing of the Implant Studio has been completed using the approved acceptance criteria: Each user need has its own validation acceptance criteria: each specification has its own verification acceptance criteria; bug verification consists in ensuring issue is not reproducible; issues reported by beta partners must be reviewed and handled appropriately."
However, no specific quantitative acceptance criteria or corresponding reported device performance metrics are provided in a table or any other format within the given text. It mentions that "All test results have been reviewed and approved, showing the Implant Studio to be substantially equivalent in safety and effectiveness to the predicate," but lacks concrete data.
2. Sample size used for the test set and the data provenance:
- Sample size: Not specified.
- Data provenance: Not specified. The document only mentions "bug verification consists in ensuring issue is not reproducible; issues reported by beta partners must be reviewed and handled appropriately," which suggests some testing with external users, but the origin or nature of the data involved is not detailed.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
This information is not provided. The text does not detail how ground truth was established for any internal testing or beta testing.
4. Adjudication method for the test set:
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:
No MRMC study was mentioned. The document focuses on the software as a planning tool, not as an AI-assissted diagnostic or interpretive device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
The device is described as "software only device used to pre-operatively plan the placement of a dental implant and to visualize a patient's CT image... The guide can be used for aiding the placement of the implant(s) to the intended position(s)." It appears to be an algorithm-only device for planning, but its performance is not explicitly presented as a standalone study result. The "use of the software requires that the user has the necessary medical training in implantology and surgical dentistry," indicating a human-in-the-loop for the overall process, even if the planning itself is algorithmically driven. However, there's no standalone performance study reported.
7. The type of ground truth used:
Not specified in the provided text.
8. The sample size for the training set:
The document does not mention a "training set" in the context of machine learning or AI. It refers to a "planning phase" and "library files," but no training data for an algorithm is described.
9. How the ground truth for the training set was established:
This information is not applicable as no training set is discussed. The "implant and sleeve library files are provided via encrypted library files which are generated by 3Shape and approved by the corresponding original manufactures," suggesting pre-defined data rather than a learned model.
In summary, the provided FDA 510(k) summary focuses on establishing substantial equivalence to predicate devices and describes the software's intended use, technological characteristics, and verification/validation processes. However, it does not include the specific quantitative acceptance criteria, detailed study designs, sample sizes, or ground truth establishment methods that would fully answer the questions posed. The nonclinical testing section broadly states that "verification and validation testing... has been completed using the approved acceptance criteria," but these criteria and their results are not explicitly detailed. The document also explicitly states: "Clinical testing is not a requirement and has not been performed."
§ 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).