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DBSWIN and VistaEasy imaging software are intended for use by qualified dental professionals for windows based diagnostics. The software is a diagnostic aide for licensed radiologists, dentists and clinicians, who perform the actual diagnosis based on their training, qualification, and clinical experience. DBSWIN and VistaEasy are clinical software applications that receive images and data from various imaging sources (i.e., radiography devices and digital video capture devices) that are manufactured and distributed by DÜRR Dental and Air Techniques. It is intended to acquire, display, edit (i.e., resize, adjust contrast, etc.) and distribute images using standard PC hardware. In addition, DBSWIN enables the acquisition of still images from 3rd party TWAIN compliant imaging devices (e.g., generic image devices such as scanners) and the storage and printing of clinical exam data, while VistaEasy distributes the acquired images to 3rd party TWAIN compliant PACS systems for storage and printing.

DBSWIN and VistaEasy software are not intended for mammography use.

DBSWIN and VistaEasy imaging software is an image management system that allows dentists to acquire, display, edit, view, store, print, and distribute medical images. DBSWIN and VistaEasy software runs on user provided PC-compatible computers and utilize previously cleared digital image capture devices for image acquisition. VistaEasy is included as part of DBSWIN. It provides additional interfaces for Third Party Software. VistaEasy can also be used by itself, as a reduced feature version of DBSWIN.

The provided document is a 510(k) summary for DÜRR DENTAL SE's DBSWIN and VISTAEASY Imaging Software (K190629). This submission focuses on establishing substantial equivalence to a predicate device (K161444) rather than presenting a study to prove performance against specific acceptance criteria for a novel device. Therefore, much of the requested information about device performance and study details is not explicitly available in this document.

However, based on the information provided, here's an attempt to answer the questions:

1. A table of acceptance criteria and the reported device performance

The document does not specify quantitative acceptance criteria for device performance. Instead, it asserts substantial equivalence to a predicate device by comparing technological characteristics and functionalities. The performance is implied to be equivalent to the predicate.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document does not specify a separate "test set" or its sample size. The performance claims are based on "Bench testing, effectiveness, and functionality" and "Full functional software cross check testing." No information on data provenance (country of origin, retrospective/prospective) is provided, as no clinical study with patient data is mentioned.

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)

Not applicable. The document does not describe a study involving expert-established ground truth for a test set, as it emphasizes technological equivalence and software verification/validation rather than clinical performance evaluation against a gold standard.

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

Not applicable. No expert-adjudicated test set is described.

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

Not applicable. This document is for imaging software that aids in acquiring, displaying, editing, and distributing images, rather than an AI diagnostic tool. No MRMC study is mentioned.

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

Not applicable. The device is referred to as "clinical software applications" and a "diagnostic aide for licensed radiologists, dentists and clinicians," indicating a human-in-the-loop context. It processes and presents images, it does not perform standalone diagnosis.

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

Not applicable. As no clinical study validating diagnostic accuracy is presented, there is no mention of a ground truth type. The focus is on software functionality and technical specifications.

8. The sample size for the training set

Not applicable. This device is not described as an AI/machine learning model where a training set size would be relevant. It's a general imaging software.

9. How the ground truth for the training set was established

Not applicable, for the same reason as above.

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