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Dolphin Blue Imaging 2.0 software is designed for use by specialized dental practices for capturing, storing and presenting patient images and assisting in treatment planning and case diagnosis. Results produced by the software's diagnostic and treatment planning tools are dependent on the interpretation of trained and licensed practitioners.

Dolphin Blue Imaging 2.0 software is designed for use by specialized dental practices for capturing, storing and presenting patient images and assisting in treatment planning and case diagnosis. Results produced by the software's diagnostic and treatment planning tools are dependent on the interpretation of trained and licensed practitioners.

Dolphin Blue Imaging 2.0 is a software that provides imaging, diagnostics, and case presentation capabilities for dental specialty professionals. The Dolphin Blue Imaging 2.0 suite of software products is a collection of modules that together provide a comprehensive toolset for the dental specialty practitioner. Users can easily manage 2D images and x-rays; accurately diagnose and treatment plan, quickly communicate and present cases to patients and can work efficiently with colleagues on multidisciplinary cases. The below functionalities make up the medical device modules:

Tracing Module
Measurements Module
Superimpositions Module

The provided text does not contain specific acceptance criteria or a detailed study proving the device meets particular performance metrics. It focuses on regulatory aspects of the Dolphin Blue Imaging 2.0 software, asserting its substantial equivalence to predicate devices and outlining the general verification and validation processes followed.

However, based on the general information provided regarding verification and validation activities, we can infer some general acceptance an "Implied Acceptance Criteria" and what the document generally states regarding testing.

Implied Acceptance Criteria and Reported Device Performance

Since no specific quantitative performance metrics are provided as acceptance criteria, the table below reflects what can be inferred from the document's statements about successful verification and validation.

Study Information

1. Sample Size used for the test set and the data provenance: Not explicitly stated within the provided text. The document refers to "Data sets are utilized while testing several categories" in the performance testing section but does not specify the size or provenance of these datasets.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not explicitly stated within the provided text. The document mentions that "Results produced by the software's diagnostic and treatment planning tools are dependent on the interpretation of trained and licensed practitioners," implying human interpretation is critical, but does not detail how ground truth was established for testing.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not explicitly stated within the provided text.

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: Not explicitly stated or indicated. The document focuses on the software's substantial equivalence and internal testing, not on human reader performance with or without AI assistance. The device is described as "assisting in treatment planning and case diagnosis," but no human-in-the-loop performance study is mentioned.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The document describes various software tests (Unit, Performance, Manual, Integration, System, Regression testing) that would evaluate the algorithm's standalone performance in terms of functionality and adherence to specifications. However, it does not present a formal "standalone performance study" with specific metrics like sensitivity, specificity, or AUC against a ground truth. The focus is on verifying that the software "works as designed" and meets "performance requirements."

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not explicitly stated. The document mentions that cephalometric measurements are "calculated in real-time by the Dolphin Blue Ceph server" and that "Calculations take into consideration the race, gender, age, and customizable normal measurement values for a patient to indicate deviations from the accepted normal measurements." This suggests that the "ground truth" for calculations is based on established anatomical landmark locations and predefined normal measurement values, but the source or method of establishing these for the test set is not detailed.

7. The sample size for the training set: Not applicable and not stated. The document describes the software functionalities for image management, diagnostic tools (cephalometric tracing, measurements, superimpositions), and case presentation. It does not describe a machine learning or AI model that would require a distinct training set for itself, separate from the software's development and verification processes.

8. How the ground truth for the training set was established: Not applicable and not stated, as there is no mention of a distinct training set for an AI/ML model.

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