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IPS is prescription use software used by dentists and dental lab technicians for the visualization and image segmentation of DICOM data from medical scanners such as CT. The software aids the user in the creation of 3D models of oral maxillofacial region and in planning dental surgical treatments and placement of dental implants.

IPS is a Dental 3D Planning Software that aids dentists and dental lab technicians in the creation of 3D models of oral maxillofacial region and in planning dental surgical treatments and placement of dental implants. The data used for planning the treatment course is obtained from radiological devices (e.g., CT) complying with DICOM format. IPS provides dentists with various visualization and measurement tools which facilitate in generating dental panoramic view, segmenting of the dental anatomical structures and measuring bone density and distance between the dental implants.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Dental 3D Planning Software IPS:

Summary of Acceptance Criteria and Reported Device Performance

• Importing DICOM Data.
• Sorting DICOM data.
• Grouping multiple DICOM cases. | Results of all testing demonstrate that IPS does not raise any new issues of safety, effectiveness or performance when compared to the existing predicate devices. The software conforms to DICOM standard and it has been verified and validated for it. | Validation and verification study (DICOM Conformance Testing) |
  | Safety, Effectiveness, and Performance (implicitly, based on substantial equivalence to predicates) | No new issues of safety, effectiveness, or performance compared to predicate devices. | Comparison to predicate devices, supported by DICOM conformance testing. |

Detailed Study Information:

1. Sample size used for the test set and the data provenance:

   • Sample Size: The document does not explicitly state the number of DICOM cases or patient data used for the "Validation and verification was performed to demonstrate DICOM conformance." It mentions "These test cases are performed on the patient's DICOM images," but the quantity is not specified.
   • Data Provenance: Not specified. It's unclear if the data was retrospective, prospective, or its country of origin.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not applicable. The study described is a technical validation of DICOM conformance, not a clinical study involving expert assessment of diagnostic accuracy or treatment planning outcomes. Ground truth in this context would likely be the expectation of proper DICOM data handling, rather than clinical interpretation.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

   • Not applicable. As this was a technical validation of DICOM conformance, an adjudication method for clinical agreement is not relevant.

4. 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 performed or described. This submission focuses on establishing substantial equivalence based on intended use, technology, and DICOM conformance, not on the comparative clinical performance of human readers with or without AI assistance.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

   • The "Validation and verification was performed to demonstrate DICOM conformance" is implicitly a standalone performance test of the software's ability to handle DICOM data. The software is described as a "pure software device" for "visualization and planning simulation," indicating its primary function is independent image processing and display. However, it's not a standalone diagnostic algorithm in the sense of making a diagnosis without human input; it's a tool for human planning.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • The ground truth for the DICOM conformance testing would be the DICOM standard itself. The test cases were designed to verify that the software correctly imports, sorts, and groups DICOM data according to the established rules and specifications of the DICOM standard.

7. The sample size for the training set:

   • Not applicable. This device is a dental 3D planning software and the provided text does not describe an AI/machine learning model that would require a "training set." The validation described is for DICOM conformance, which is a functional test, not a learning algorithm.

8. How the ground truth for the training set was established:

   • Not applicable, as there is no training set mentioned or implied for this device.

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