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VELMENI for DENTISTS (V4D) is a concurrent-read, computer-assisted detection software intended to assist dentists in the clinical detection of dental caries, fillings/restorations, fixed prostheses, and implants in digital bitewing, periapical, and panoramic radiographs of permanent teeth in patients 15 years of age or older. This device provides additional information for dentists in examining radiographs of patients' teeth. This device is not intended as a replacement for a complete examination by the dentist or their clinical judgment that considers other relevant information from the image, patient history, or actual in vivo clinical assessment. Final diagnoses and patient treatment plans are the responsibility of the dentist.

This device includes a Predetermined Change Control Plan (PCCP).

V4D software medical device comprises of the following key components:

• Web Application Interface delivers front-end capabilities and is the point of interaction between the device and the user.
• Machine Learning (ML) Engine delivers V4D's core ML capabilities through the radiograph type classifier, condition detection module, tooth numbering module, and merging module.
• Backend API allows interaction between all the components, as defined in this section, in order to fulfill the user's requests on the web application interface.
• Queue receives and stores messages from Backend API to send to AI-Worker.
• AI-Worker accepts radiograph analysis requests from Backend API via the Queue, passes gray scale radiographs to the ML Engine in the supported extensions (jpeg and png), and returns the ML analysis results to the Backend API.
• Database and File Storage store critical information related to the application, including user data, patient profiles, analysis results, radiographs, and associated data.

The following non-medical interfaces are also available with VELMENI for DENTISTS (V4D):

• VELMENI BRIDGE (VB) acts as a conduit enabling data and information exchange between Backend API and third-party software like Patient Management or Imaging Software
• Rejection Review (RR) module captures the ML-detected conditions rejected by dental professionals to aid in future product development and to be evaluated in accordance with VELMENIs post-market surveillance procedure.

This device includes a Predetermined Change Control Plan (PCCP).

This 510(k) clearance letter for VELMENI for DENTISTS (V4D) states that the proposed device is unchanged from its predicate (VELMENI for Dentists cleared under K240003), except for the inclusion of a Predetermined Change Control Plan (PCCP). Therefore, all performance data refers back to the original K240003 clearance. The provided document does not contain the specific performance study details directly, but it references their applicability from the predicate device.

Based on the provided text, the response will extract what details are available and note where specific information is not included in this document, but referred to as existing from the predicate device's clearance.

1. Table of Acceptance Criteria and Reported Device Performance

The provided document refers to the acceptance criteria and performance data existing from the predicate device (K240003). It also mentions that the PCCP updates the acceptance criteria for Sensitivity, Specificity, and Average False Positives to match the lower bounds of the confidence interval demonstrated by the originally cleared models' standalone results. However, the specific values for these criteria and the reported performance are not explicitly stated in this document.

Note: The document only states that MRMC results concluded the effectiveness of the V4D software in assisting readers to identify more caries and identify more fixed prostheses, implants, and restorations correctly. Specific quantitative performance metrics (e.g., Sensitivity, Specificity, AUC, FROC, etc.) are not provided in this document.

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

The document states:

• "The new models will be evaluated on a combined test dataset with balanced ratio of historical and new data for validation to avoid overfitting historical data from repeated use."
• "The new test data is fully independent on a site-level from training/tuning data, and the test dataset remains at least 50% US data."

Specific sample size for the test set is not provided in this document.
Data Provenance: At least 50% US data, including both historical and new data. It is a retrospective dataset for testing as it uses both historical and new data collected implicitly beforehand.

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

The document does not specify the number of experts used and their qualifications for establishing ground truth for the test set.

4. Adjudication Method for the Test Set

The document does not specify the adjudication method used for the test set (e.g., 2+1, 3+1, none).

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

Yes, an MRMC comparative effectiveness study was done.

The document states: "MRMC results concluded the effectiveness of the V4D software in assisting readers to identify more caries and identify more fixed prostheses, implants, and restorations correctly."

Effect Size: The document does not provide a specific quantitative effect size of how much human readers improve with AI vs. without AI assistance. It only makes a qualitative statement about improved identification of conditions.

6. Standalone Performance Study

Yes, a standalone (algorithm only without human-in-the-loop performance) study was done.

The document states: "The acceptance criteria for Sensitivity, Specificity and Average False Positives have been updated to match the lower bounds of confidence interval demonstrated by the originally cleared models' standalone results." This implies that standalone performance metrics were evaluated for the original clearance.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used (e.g., expert consensus, pathology, outcomes data). However, for a dental imaging device assisting dentists, it is highly likely that expert consensus from dental professionals (dentists or dental radiologists) would have been used for establishing ground truth. The mention of "dental professionals" rejecting ML-detected conditions in the "Rejection Review (RR)" module also hints at expert review for ground truth establishment.

8. Sample Size for the Training Set

The document does not specify the sample size for the training set. It mentions "new and existing training and tuning data" for re-training.

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

The document does not explicitly state how the ground truth for the training set was established. However, given the context of a medical device aiding dentists in clinical detection, it is highly probable that ground truth would have been established through expert annotations or consensus from qualified dental professionals.

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