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In VivoDental is a software application used for the display and 3D visualization of medical image files from scanning devices, such as CT, MRI, or 3D Ultrasound. It is intended for use by radiologists, clinicians, referring physicians and other qualified individuals to retrieve, process, render, review, store, print, assist in diagnosis and distribute images, utilizing standard PC hardware. Additionally, In VivoDental is a preoperative software application used for the simulation of dental implants, orthodontic planning and surgical treatments.

This device is not indicated for mammography use.

InVivoDental is a volumetric imaging software designed specifically for clinicians, doctors, physicians, and other qualified medical professionals. The software runs in Windows operating systems and visualizes medical imaging data on the computer screen. The software is downloaded over the internet and installed on the customer's computer. Users are able to examine anatomy on a computer screen and use software tools to move and manipulate rendered images by turning, zooming, flipping, adjusting contrast and brightness, cutting, and slicing. There are several rendering settings to emphasize bone and/or soft tissue. The software also has the ability to create panoramic images, superimpose images, and create measurements of volume, angle, and length. There are multiple tools to annotate and otherwise mark areas of interest on the images. The software has specialized tools to simulate surgical treatments using models rendered by Antomage, and any view or user modified area can be saved to a gallery. These simulations and galleries can then be reviewed by medical personnel or used for consultation between the doctor and patient.

This section describes the acceptance criteria and the study that proves the device meets the acceptance criteria.

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample Size Used for the Test Set and Data Provenance:

The document does not explicitly state the sample size (number of cases or images) used for the test set during the bench testing. The data provenance (country of origin, retrospective or prospective) is also not specified.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications:

The bench testing "was performed by an expert in the field of radiology." The specific qualifications (e.g., years of experience, subspecialty) of this expert are not detailed beyond "an expert in the field of radiology."

4. Adjudication Method for the Test Set:

The document does not describe an adjudication method (such as 2+1, 3+1, or none) for the test set. The evaluation was performed by a single expert.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

A multi-reader multi-case (MRMC) comparative effectiveness study was not conducted according to the provided information. The testing involved comparing InVivoDental's performance to predicate devices by a single expert.

6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance:

The primary evaluation described is a standalone performance assessment where the software's rendering and measurement capabilities were directly compared against predicate devices. The "human-in-the-loop" aspect was the expert radiologist evaluating the outputs of both the subject and predicate software, rather than evaluating the AI assisting a human. The software itself is designed to be used by clinicians independently.

7. Type of Ground Truth Used:

The ground truth for the bench testing was established by comparing the performance (specifically measurement and rendering accuracy) of InVivoDental against that of predicate software. The "truth" was based on the accepted functionality and output of already-marketed equivalent devices.

8. Sample Size for the Training Set:

The submission does not provide any information regarding a training set size. This suggests that the device's development and validation relied on comparison to predicate devices and established software engineering practices rather than a machine learning model that would require a distinct training set.

9. How the Ground Truth for the Training Set Was Established:

Since no information regarding a training set or machine learning model is provided, the method for establishing ground truth for a training set is not applicable in this context. The validation focuses on verification through quality assurance measures and performance testing against predicate devices.

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