(170 days)
DTX Studio diagnose is a software program for the transfer and visualization of dental and craniomaxillofacial image information. It displays and enhances digital images from various sources to support the diagnostic process It stores and communicates these images within the system or across computer systems at distributed locations.
DTX Studio diagnose is a software solution used to support the image-based diagnostic process of dental and cranio-maxillofacial cases.
DTX Studio diagnose has specific functionalities to visualize imaging information for facilitating diagnosis, e.g. 2D and 3D X-ray information, and to perform specific measurements on the data to support users with the diagnostic process.
The types of digital image data which are supported by the DTX Studio diagnose include, for example, 3D (CB)CT images, orthopantomograph(OPG)/panorex images, intraoral images, cephalograms and clinical pictures.
DTX Studio diagnose allows the user to view and inspect the patient images, and to add findings and measurements. Indicated findings can be saved within the patient profile in DTX Studio. For the purposes of diagnosis, different workspaces are available within the diagnostic module.
The DTX Studio diagnose software is compatible with both Windows and Mac OS X operating systems. Two different software versions/installers are available based on the user's operating system.
This FDA 510(k) clearance document for the DTX Studio diagnose software does not include detailed acceptance criteria or a study proving that the device meets specific performance criteria in terms of diagnostic accuracy or clinical effectiveness.
The document primarily focuses on demonstrating substantial equivalence to predicate devices (CliniView (K162799) and Sidexis 4 (K132773)) based on technological characteristics and non-clinical performance data (software verification and validation).
Here's a breakdown of the requested information based on the provided text:
1. A table of acceptance criteria and the reported device performance
This information is not provided in the document. The submission focuses on software validation and verification against its own requirements, not against pre-defined clinical performance acceptance criteria.
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
This information is not provided. The document mentions "testing which demonstrates that the requirements for the features have been met" (Page 8, Section VII) but does not detail the nature of this testing, the size of any test sets (e.g., images), or data provenance.
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)
This information is not provided. As no clinical or diagnostic performance study is described with a ground truth, this is not applicable.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not provided. As no clinical or diagnostic performance study is described, adjudication methods are not relevant to this submission.
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 comparative effectiveness study was done or reported. The document explicitly states: "No clinical data was used to support the decision of substantial equivalence." (Page 9, Section VII).
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The device is described as "software solution used to support the image-based diagnostic process" (Page 3, Section IV) and to "support the diagnostic process" (Page 5, Section V). Its functions include "visualize imaging information for facilitating diagnosis" and "perform specific measurements on the data to support users with the diagnostic process" (Page 3, Section IV). This indicates it is intended to be used with human-in-the-loop, not as a standalone diagnostic algorithm. No standalone validation is described.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
This information is not provided. Since no clinical performance study aiming to establish diagnostic accuracy is detailed, no ground truth type is mentioned. The "performance data" referred to is non-clinical software verification and validation against functional requirements.
8. The sample size for the training set
This information is not provided. As "No clinical data was used to support the decision of substantial equivalence" and the software's functionality revolves around image visualization and measurement tools (rather than automated diagnostic algorithms requiring extensive training), no training set is mentioned.
9. How the ground truth for the training set was established
This information is not provided. As no training set is discussed, the method for establishing its ground truth is not applicable.
In summary:
This 510(k) clearance is based on substantial equivalence to legally marketed predicate devices, primarily focusing on the software's functional verification and validation against its own requirements as per FDA guidance for software, rather than a clinical performance study with specific diagnostic accuracy acceptance criteria. The document explicitly states that "No clinical data was used to support the decision of substantial equivalence."
§ 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).