(241 days)
Yes
The device description explicitly mentions a "Machine Learning (ML) Engine" and "Al-Worker" which are core components delivering the device's capabilities. The "Mentions AI, DNN, or ML" section also confirms the use of Machine Learning and neural network-based computer algorithms.
No
The device is intended to assist dentists in detecting certain dental conditions and structures from radiographs. It does not directly treat or prevent a disease or condition, which is characteristic of a therapeutic device. It provides diagnostic information to aid clinical judgment.
Yes
Explanation: The "Intended Use / Indications for Use" section explicitly states that the software is "intended to assist dentists in the clinical detection of dental caries, fillings/restorations, fixed prostheses, and implants." This function directly aligns with the definition of a diagnostic device as it aims to identify the presence of diseases or conditions.
Yes
The device description explicitly states "V4D software medical device comprises of the following key components" and lists only software-based components (Web Application Interface, ML Engine, Backend API, Queue, AI-Worker, Database and File Storage). It also lists "non-medical interfaces" which are also software-based. There is no mention of any hardware component being part of the device itself.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections.
- Device Function: The VELMENI for DENTISTS (V4D) software analyzes digital radiographs (images) of teeth. It does not analyze biological samples taken from the patient.
- Intended Use: The intended use is to assist dentists in the clinical detection of dental conditions by analyzing images, not by performing tests on biological specimens.
Therefore, VELMENI for DENTISTS (V4D) falls under the category of a medical device that processes medical images, not an In Vitro Diagnostic device.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
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.
Product codes
MYN
Device Description
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 Al-Worker.
- Al-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.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Radiograph / Digital intraoral files of bitewing and periapical radiographs and digital extraoral files of panoramic radiograph
Anatomical Site
Dental/teeth
Indicated Patient Age Range
15 years of age or older
Intended User / Care Setting
Dentists / Licensed Dental Professional
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
Standalone performance of the Velmeni for Dentists (V4D) was evaluated on a total of 600 Bitewing images, 597 Periapical images and 600 Panoramic images. Standalone performance was compared to ground truth established by consensus labels of three US licensed dentists, and nonconsensus labels were adjudicated by an oral radiologist.
Velmeni evaluated the performance of Velmeni for Dentists (V4D) in a multi-reader fully crossed reader improvement study. 12 US licensed dentists were asked to evaluate 600 bitewing images (total caries 315), 597 periapical images (total caries 271) and 600 panoramic images (total caries 853). Ground truth was established by the consensus labels of three US licensed dentists, and nonconsensus labels were adjudicated by an oral radiologist. Half of the data set contained unannotated images, and the second half contained radiographs that had been processed through the V4D model. Radiographs were presented to readers in alternating groups. In Session 1, readers were asked to outline suspected caries, fixed prosthesis, implant, and restorations and to review predictions from the V4D model. Each reader was asked to provide a rating of 25 - 100 for their confidence in the annotation (25 for lowest confidence, up to 100 for highest confidence). A 4-week washout period was utilized to limit recollection bias. Following the washout, the readers were presented with the same data set but with alternate grouping. i.e., if a reader saw a radiograph in the unpredicted state in session 1, they were presented with the same radiograph with V4D predictions in session 2, and vice versa.
Summary of Performance Studies
Standalone Performance:
- Study Type: Standalone performance evaluation
- Sample Size: 600 Bitewing images, 597 Periapical images, 600 Panoramic images
- AUC: Not explicitly reported for standalone performance, but sensitivity and specificity are provided.
- MRMC: Not applicable for standalone performance.
- Standalone performance: Assessed sensitivity, specificity and Dice coefficient.
- Key results:
- Lesion-Level Sensitivity for Caries: Bitewing Views 72.8%, Periapical Views 70.6%, Panoramic Views 68.3%.
- Lesion-level Sensitivity for Fixed Prosthesis: Bitewing Views 92.1%, Periapical Views 81.0%, Panoramic Views 74.5%.
- Lesion-level Sensitivity for Implant: Bitewing Views 81.1%, Periapical Views 94.5%, Panoramic Views 79.6%.
- Lesion-level Sensitivity for Restoration: Bitewing Views 88.1%, Periapical Views 76.8%, Panoramic Views 72.6%.
- Case-level Sensitivity for Caries: Bitewing Views 59.5%, Periapical Views 57.5%, Panoramic Views 45.0%.
- Case-level Sensitivity for Fixed Prosthesis: Bitewing Views 81.1%, Periapical Views 71.7%, Panoramic Views 44.3%.
- Case-level Sensitivity for Implant: Bitewing Views 75.0%, Periapical Views 91.7%, Panoramic Views 60.0%.
- Case-level Sensitivity for Restoration: Bitewing Views 71.6%, Periapical Views 68.1%, Panoramic Views 27.2%.
- Case-level Specificity for Caries: Bitewing Views 88.0%, Periapical Views 84.7%, Panoramic Views 96.8%.
- Case-level Specificity for Fixed Prosthesis: Bitewing Views 98.2%, Periapical Views 99.7%, Panoramic Views 97.7%.
- Case-level Specificity for Implant: Bitewing Views 100.0% (NA, NA), Periapical Views 100.0% (NA, NA), Panoramic Views 100.0% (NA, NA).
- Case-level Specificity for Restoration: Bitewing Views 93.3%, Periapical Views 95.1%, Panoramic Views 83.3%.
- DICE Score Mean for Caries: Bitewing 81.96%, Periapical 82.77%, Panoramic 77.07%.
- DICE Score Mean for Fixed Prosthesis: Bitewing 97.09%, Periapical 96.23%, Panoramic 91.47%.
- DICE Score Mean for Implant: Bitewing 94.20%, Periapical 95.47%, Panoramic 88.67%.
- DICE Score Mean for Restoration: Bitewing 90.45%, Periapical 81%, Panoramic 81.49%.
Clinical Performance:
- Study Type: Multi-reader fully crossed reader improvement study (MRMC)
- Sample Size: 12 US licensed dentists evaluating 600 bitewing images, 597 periapical images, and 600 panoramic images.
- AUC (wAFROC AUC):
- Caries Detection by Reader Mode: Bitewing Aided 0.848, Unaided 0.794. Periapical Aided 0.814, Unaided 0.721. Panoramic Aided 0.615, Unaided 0.579.
- Standalone performance: Not applicable for clinical performance.
- Key results:
- The V4D software demonstrated clear benefit for bitewing and periapical views in all features.
- The panoramic view demonstrated benefit though the absolute benefit for caries sensitivity was smaller due to lower overall reader performance.
- For the panoramic view, there was a benefit in restoration sensitivity that was somewhat offset by a drop in image-level specificity.
- For all lesion types and views, lesion-level sensitivity and case-level sensitivity improved with aid.
- For most lesion types and views, mean FPs per image decreased with aid.
- Case-level specificity varied, with some decreases when aided, particularly for periapical caries and panoramic restorations.
Key Metrics
Standalone Performance:
- Lesion-Level Sensitivity for Caries: Bitewing Views 72.8% (68.0%, 77.4%), Periapical Views 70.6% (64.1%, 76.6%), Panoramic Views 68.3% (64.9%, 71.8%).
- Lesion-level Sensitivity for Fixed Prosthesis: Bitewing Views 92.1% [90.0%, 94.1%], Periapical Views 81.0% [76.6%, 85.4%], Panoramic Views 74.5% [71.5%, 77.3%].
- Lesion-level Sensitivity for Implant: Bitewing Views 81.1% [67.6%, 92.1%], Periapical Views 94.5% [90.0%, 98.2%], Panoramic Views 79.6% [69.7%, 87.4%].
- Lesion-level Sensitivity for Restoration: Bitewing Views 88.1% [86.1%, 90.1%], Periapical Views 76.8% [72.0%, 81.4%], Panoramic Views 72.6% [70.6%, 74.6%].
- Case-level Sensitivity for Caries: Bitewing Views 59.5% [51.7%, 66.9%], Periapical Views 57.5% [50.0%, 65.1%], Panoramic Views 45.0% [39.7%, 50.5%].
- Case-level Sensitivity for Fixed Prosthesis: Bitewing Views 81.1% [75.7%, 86.3%], Periapical Views 71.7% [62.5%, 79.9%], Panoramic Views 44.3% [38.0%, 50.6%].
- Case-level Sensitivity for Implant: Bitewing Views 75.0% [59.1%, 88.9%], Periapical Views 91.7% [84.8%, 97.2%], Panoramic Views 60.0% [45.0%, 74.4%].
- Case-level Sensitivity for Restoration: Bitewing Views 71.6% [67.0%, 76.2%], Periapical Views 68.1% [61.2%, 74.5%], Panoramic Views 27.2% [23.3%, 31.2%].
- False Positives Per Image Mean for Caries: Bitewing 0.24 [0.19, 0.29], Periapical 0.27 [0.22, 0.32], Panoramic 0.33 [0.28, 0.38].
- False Positives Per Image Mean for Fixed Prosthesis: Bitewing 0.03 [0.01, 0.05], Periapical 0.01 [0.00, 0.02], Panoramic 0.06 [0.04, 0.08].
- False Positives Per Image Mean for Implant: Bitewing 0.00 [0.00, 0.01], Periapical 0.00 [NA, NA], Panoramic 0.01 [0.00, 0.02].
- False Positives Per Image Mean for Restoration: Bitewing 0.15 [0.11, 0.19], Periapical 0.10 [0.07, 0.13], Panoramic 0.62 [0.54, 0.70].
- Case-level Specificity for Caries: Bitewing 88.0% [84.4%, 91.2%], Periapical 84.7% [81.4%, 87.9%], Panoramic 96.8% [94.6%, 98.6%].
- Case-level Specificity for Fixed Prosthesis: Bitewing 98.2% [96.4%, 99.7%], Periapical 99.7% [99.2%, 100.0%], Panoramic 97.7%[96.0%, 99.2%].
- Case-level Specificity for Implant: Bitewing 100.0%[NA, NA], Periapical 100.0% [NA, NA], Panoramic 100.0% [NA, NA].
- Case-level Specificity for Restoration: Bitewing 93.3% [89.1%, 96.9%], Periapical 95.1%[92.8%, 97.2%], Panoramic 83.3% [75.0%, 90.6%].
- DICE Score Mean for Caries: Bitewing 81.96% (80.81%, 83.10%), Periapical 82.77% (81.41%, 84.13%), Panoramic 77.07% (76.25%, 77.89%).
- DICE Score Mean for Fixed Prosthesis: Bitewing 97.09% (96.84%, 97.33%), Periapical 96.23% (95.78%, 96.69%), Panoramic 91.47% (91.24%, 91.71%).
- DICE Score Mean for Implant: Bitewing 94.20% (92.44%, 95.97%), Periapical 95.47% (94.60%, 96.34%), Panoramic 88.67% (87.22%, 90.11%).
- DICE Score Mean for Restoration: Bitewing 90.45% (90.06%, 90.84%), Periapical 81% (88.97%, 90.64%), Panoramic 81.49% (81.19%, 81.78%).
Clinical Performance:
- Lesion-Level Sensitivity (Aided): Caries Bitewing 80.3%, Periapical 73.4%, Panoramic 27.2%; Fixed Prosthesis Bitewing 95.7%, Periapical 91.1%, Panoramic 88.8%; Implant Bitewing 93.2%, Periapical 95.9%, Panoramic 88.3%; Restoration Bitewing 90.8%, Periapical 90.6%, Panoramic 73.0%.
- Lesion-Level Sensitivity (Unaided): Caries Bitewing 67.5%, Periapical 48.7%, Panoramic 15.1%; Fixed Prosthesis Bitewing 90.2%, Periapical 80.0%, Panoramic 80.5%; Implant Bitewing 61.3%, Periapical 79.5%, Panoramic 79.6%; Restoration Bitewing 74.1%, Periapical 80.3%, Panoramic 57.4%.
- Mean FPs per Image (Aided): Caries Bitewing 0.18, Periapical 0.19, Panoramic 0.21; Fixed Prosthesis Bitewing 0.04, Periapical 0.01, Panoramic 0.07; Implant Bitewing 0.00, Periapical 0.00, Panoramic 0.01; Restoration Bitewing 0.15, Periapical 0.07, Panoramic 0.73.
- Mean FPs per Image (Unaided): Caries Bitewing 0.18, Periapical 0.08, Panoramic 0.30; Fixed Prosthesis Bitewing 0.13, Periapical 0.04, Panoramic 0.18; Implant Bitewing 0.00, Periapical 0.01, Panoramic 0.01; Restoration Bitewing 0.29, Periapical 0.05, Panoramic 1.02.
- Case-level Sensitivity (Aided): Caries Bitewing 69.5%, Periapical 59.0%, Panoramic 11.5%; Fixed Prosthesis Bitewing 89.6%, Periapical 82.7%, Panoramic 70.5%; Implant Bitewing 91.1%, Periapical 93.8%, Panoramic 77.1%; Restoration Bitewing 77.8%, Periapical 83.9%, Panoramic 26.7%.
- Case-level Sensitivity (Unaided): Caries Bitewing 51.5%, Periapical 33.6%, Panoramic 8.5%; Fixed Prosthesis Bitewing 82.7%, Periapical 67.1%, Panoramic 67.0%; Implant Bitewing 59.2%, Periapical 77.5%, Panoramic 77.1%; Restoration Bitewing 52.7%, Periapical 69.4%, Panoramic 19.6%.
- Case-level Specificity (Aided): Caries Bitewing 85.7%, Periapical 84.2%, Panoramic 95.1%; Fixed Prosthesis Bitewing 97.9%, Periapical 99.7%, Panoramic 97.6%; Implant Bitewing 100.0%, Periapical 99.9%, Panoramic 100.0%; Restoration Bitewing 92.5%, Periapical 94.9%, Panoramic 85.8%.
- Case-level Specificity (Unaided): Caries Bitewing 88.5%, Periapical 94.5%, Panoramic 94.6%; Fixed Prosthesis Bitewing 98.7%, Periapical 99.5%, Panoramic 99.0%; Implant Bitewing 99.9%, Periapical 100.0%, Panoramic 100.0%; Restoration Bitewing 94.0%, Periapical 97.6%, Panoramic 96.2%.
Predicate Device(s)
Overjet Caries Assist (K222746)
Reference Device(s)
Second Opinion (K210365), Denti.Al Detect (K230144)
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.2070 Medical image analyzer.
(a)
Identification. Medical image analyzers, including computer-assisted/aided detection (CADe) devices for mammography breast cancer, ultrasound breast lesions, radiograph lung nodules, and radiograph dental caries detection, is a prescription device that is intended to identify, mark, highlight, or in any other manner direct the clinicians' attention to portions of a radiology image that may reveal abnormalities during interpretation of patient radiology images by the clinicians. This device incorporates pattern recognition and data analysis capabilities and operates on previously acquired medical images. This device is not intended to replace the review by a qualified radiologist, and is not intended to be used for triage, or to recommend diagnosis.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the image analysis algorithms including a description of the algorithm inputs and outputs, each major component or block, and algorithm limitations.
(ii) A detailed description of pre-specified performance testing methods and dataset(s) used to assess whether the device will improve reader performance as intended and to characterize the standalone device performance. Performance testing includes one or more standalone tests, side-by-side comparisons, or a reader study, as applicable.
(iii) Results from performance testing that demonstrate that the device improves reader performance in the intended use population when used in accordance with the instructions for use. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, predictive value, and diagnostic likelihood ratio). The test dataset must contain a sufficient number of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results; and cybersecurity).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use.
(ii) A detailed description of the intended reading protocol.
(iii) A detailed description of the intended user and user training that addresses appropriate reading protocols for the device.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality or for certain subpopulations), as applicable.(vii) Device operating instructions.
(viii) A detailed summary of the performance testing, including: test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.
0
August 30, 2024
Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). The FDA logo is composed of two parts: the Department of Health & Human Services logo on the left and the FDA acronym and full name on the right. The Department of Health & Human Services logo is a stylized depiction of an eagle. The FDA acronym is in a blue square, and the full name "U.S. Food & Drug Administration" is in blue text.
Velmeni Inc. °/o Mini Suri CEO 333 West Maude Avenue Suite 207 SUNNYVALE, CA 94085
Re: K240003
Trade/Device Name: Velmeni for Dentists (V4D) Regulation Number: 21 CFR 892.2070 Regulation Name: Medical Image Analyzer Regulatory Class: Class II Product Code: MYN Dated: August 1, 2024 Received: August 1, 2024
Dear Mini Suri:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
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Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerelv.
Lu Jiang
Lu Jiang, Ph.D. Assistant Director Diagnostic X-ray Systems Team DHT8B: Division of Radiologic Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
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Indications for Use
Submission Number (if known)
Device Name
VELMENI for DENTISTS (V4D)
Indications for Use (Describe)
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.
Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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VELMENI for DENTISTS (V4D)
510(k) Summary
In accordance with 21 CFR 807.87(h) and 21 CFR 807.92, the following 510(k) Summary for VELMENI for DENTISTS (V4D) is provided:
Submitter Information
| Submitter: | Velmeni Inc.
333 West Maude Avenue, STE 207
Sunnyvale, CA 94085
Phone: 201-289-3500 |
| ------------ | ---------------------------------------------------------------------------------------------- |
|---|
June 29, 2023 Date Prepared:
Contact Person:
Mini Suri, CEO Velmeni Inc. Phone: 201-289-3500 Email: mini@velmeni.com
Identification of the Device
| Trade Name: | VELMENI for DENTISTS (V4D) |
|---|---|
| Common Name: | Medical image analyzer |
| Classification Name | Medical image analyzer |
| 21CFR892.2070 | |
| Product Code: | MYN |
| Device Class: | Class II |
| Predicate Device(s) | |
| Predicate Device(s): | Overjet Caries Assist (K222746) |
| Reference Device: | Second Opinion (K210365) |
| Reference Device: | Denti.Al Detect (K230144) |
Intended Use/ Indications for Use
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.
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Device Description
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 Al-Worker. ●
- Al-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.
Substantial Equivalence
The proposed VELMENI for DENTISTS (V4D) has similar indications for use to, and uses the same fundamental technology as, the legally marketed predicate device to which substantial equivalence is claimed, the Overiet Caries Assist (K2222746) device. Reference devices are included for comparison for detection features and image types.
| Specification | Proposed
Device:
Velmeni For
Dentists
(V4D) | Predicate Device:
Overjet Caries
Assist
K222746 | Reference
Device:
Denti.Al
Detect
K230144 | Reference
Device:
Second
Opinion
K210365 |
|-----------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Manufacturer | Velmeni Inc. | Overjet Inc. | Denti.Al
Technology Inc. | Pearl Inc. |
| Classification | 892.2070 | 892.2070 | 892.2070 | 892.2070 |
| Product
Code | MYN | MYN | MYN | MYN |
| Specification | Proposed
Device:
Velmeni For
Dentists
(V4D) | Predicate Device:
Overjet Caries
Assist
K222746 | Reference
Device:
Denti.Al
Detect
K230144 | Reference
Device:
Second
Opinion
K210365 |
| Image
Modality | Radiograph | Radiograph | Radiograph | Radiograph |
| Intended
Use | To aid in clinical
detection of
pathologic and/or
non-pathological
dental features in
radiographs of
permanent teeth | To aid in clinical
detection of
pathologic
and/or non-
pathological
dental features
in radiographs of
permanent teeth | To aid in clinical
detection of
pathologic and/or
non-pathological
dental features in
radiographs of
permanent teeth | To aid in clinical
detection of
pathologic and/or
non-pathological
dental features in
radiographs of
permanent teeth |
| Indications | Velmeni for
Dentists (V4D) is
a concurrent-read,
computer-assisted
detection software
intended to assist
dentist 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 | The Overjet
Caries Assist
(OCA) is a
radiological,
automated,
concurrent read,
computer-
assisted
detection
software intended
to aid in the
detection and
segmentation of
caries on
bitewing and
periapical
radiographs. The
device provides
additional
information for
the dentist to use
in their diagnosis
of tooth surface
suspected of
being carious.
The device is not
intended as a
replacement for
complete
dentist's review
or their clinical
judgment that
takes into
account other
relevant
information from
the image, | Denti.Al Detect is
a Computer-
Assisted
Detection (CADe)
software device
intended to be
used by dental
professionals,
comprising
dentists and
dental specialists,
while reading
extraoral and
intraoral 2D
dental
radiographs. The
device aims to
assist in
detecting and
highlighting
uncategorized
regions of
interest (ROIs)
within the teeth
area, which
include caries
and periapical
radiolucency, as
a second reader.
The device is
also intended to
aid in the
measurements of
mesial and distal
bone levels
associated with
each tooth. The
device is aimed | Second
Opinion® is a
computer aided
detection
("CADe")
software to
identify and mark
regions in
relation to
suspected dental
findings which
include Caries,
Discrepancy at
the margin of an
existing
restoration,
Calculus,
Periapical
radiolucency,
Crown (metal,
including zirconia
& non-metal),
Filling (metal &
non- metal), Root
canal, Bridge,
and Implants. It
is designed to aid
dental health
professionals to
review bitewing
and periapical
radiographs of
permanent teeth
in patients 12
years of age or
older as a |
| Specification | Propos
ed
Device:
Velmeni
For
Dentists
(V4D) | Predicate Device:
Overjet Caries
Assist
K222746 | Reference
Device:
Denti.Al
Detect
K230144 | Reference
Device:
Second
Opinion
K210365 |
| | information from
the image,
patient history,
or actual in vivo
clinical
assessment.
Final diagnosis
and patient
treatment plans
are the
responsibility of
the dentist. | patient history,
and actual in vivo
clinical
assessment. | to be used with
images from the
patients of 22
years age and
older without
remaining
primary
dentition. The
device is not
intended to
replace a
complete
clinician's review
or clinical
judgment that
considers other
relevant
information from
the image or
patient history. | second reader. |
| Intended
Body Part | Dental/teeth | Dental/teeth | Dental/teeth | Dental/teeth |
| End User | Licensed
Dental
Professional | Dentist | Dental
Professional | Dental Clinicians |
| Patient Population | Patients requiring
dental services,
all sexes, at least
15 years of age,
and with
permanent
dentition | Patients requiring
dental services,
all sexes, at least
12 years of age,
and with
permanent
dentition | Patients
requiring dental
services, all
sexes, at least
22 years of age
or older | Patients
requiring dental
services, all
sexes, at least
12 years of age
or older |
| Prescriptio n
or OTC | Prescription Use | Prescription Use | Prescription
Use | Prescription Use |
| Reader
Workflow | Concurrent Read | Concurrent Read | Second Read | Second Read |
| Image
Source | JPG, JPEG, PNG
or DCM, DEX,
and RVG | JPG, PNG, EOP,
JIF, DICOM | JPEG, JPG,
TIFF, TIF,
PNG, BMP,
DICOM | RVG, DICOM,
JPEG, TIFF,
PNG |
| Cloud
Hosted
Software | Yes | Yes | Yes | Yes |
| Data Input | Digital
intraoral files
of bitewing
and periapical
radiographs
and digital
extraoral files
of panoramic
radiograph | Digital files of
bitewing and
periapical
radiographs | Intraoral (bitewing
and periapical)
Extraoral
(panoramic) | Digital intraoral
files of bitewing
and periapical
radiographs |
| Model | Machine
Learning | Machine
Learning | Machine Learning | Machine
Learning |
| Tooth Numbering | Yes | Yes | Unknown | Unknown |
| Detection | Caries,
restorations,
fixed
prostheses,
and implants | Caries | Caries and
periapical
radiolucency | Caries, margin
discrepancy-MD,
calculus.
periapical
radiolucency-PR,
crown, bridges,
implants, root
canals, and
fillings |
| Segmentation | Yes | Yes | Regions of Interest
(ROIs) | No |
| Bounding Boxes | Yes | Unknown | Unknown | Yes |
Table 1. Comparison of the Proposed Device. Predicate Device and Reference Devices
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Technological Characteristics
As shown in Table 1 above, the proposed VELMENI for DENTISTS (V4D) device and the predicate device have similar indications for use and uses the same fundamental technology (i.e., machine learning technology). Differences in technological characteristics include that each device uses its own proprietary algorithm to analyze digital radiographs. The proposed device's output is not limited to caries but includes restorations, fixed prostheses, and implants. Reference devices were identified because they demonstrate the Agency's familiarity with computer-assisted technology and the ability to identify and detect conditions beyond caries and in radiographs other than bitewing and periapical. The differences in technological characteristics do not raise new or different questions of safety or effectiveness.
The proposed device and the predicate device are technologically equivalent as follows:
- Both devices are designed to process digital bitewing and periapical radiographs.
- Both devices use neural network-based computer algorithms. ●
- Both devices mark detections within the user interface with a graphical overlay on the radiograph.
- . Both devices produce near-instantaneous detection results.
- Both devices are for prescription use. ●
- Both devices utilize cloud-hosted solutions. ●
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The proposed device and the predicate device are technologically different as follows:
- The proposed device can detect other dental features (i.e., restorations, fixed prostheses, . and implants) in addition to dental caries. The ability to detect other dental features has been cleared in other devices within this same requlation and product code.
- The proposed device is capable of making detections in panoramic radiographs in addition to the bitewing and periapical radiographs. The ability to detect dental features in panoramic radiographs has been cleared in other devices within the same requlation and product code.
- The proposed device will be used on patient images from 15 years old or older with permanent teeth. The use of the device in patients as young as 12 years of age with permanent teeth has already been cleared within the predicate device.
- . Once suspected findings are detected in a radiograph, the proposed device overlays the radiograph with segmentation on the radiograph or alternatively a bounding box outlining the detected regions of suspected caries, restorations, fixed prosthesis, and implants, while predicate device does so with segmented polygons outlining the detected regions of suspected caries or fills suspected caries.
- . The proposed device utilizes a different list of compatibility of image types as compared to the predicate device.
Performance Data
Biocompatibilitv Testing:
There are no direct or indirect patient-contacting components of the proposed device. Therefore, patient contact information and biocompatibility testing are not applicable for this device.
Electrical Safety and Electromagnetic Compatibility (EMC):
The proposed device is a software-only device. It contains no electric components, generates no electrical emissions, and uses no electrical energy of any type. Therefore, electrical safety and EMC testing is not applicable for this device.
Software Verification and Validation Tests:
Software verification and validation testing were conducted, and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Content of Premarket Submission for Device Software Functions." Verification of the software was conducted to ensure that the product works as designed. Validation was conducted to validate the design and the performance of the device to meet user needs and intended uses. VELMENI FOR DENTISTS (V4D) passed all verification and validation software tests. Overall, the VELMENI for DENTISTS (V4D) was found to be safe and effective for all intended users, uses, and use environments.
Animal Testing:
Animal studies were not necessary to establish the substantial equivalence of this device.
Bench Testing and Clinical Testing:
Velmeni Inc. conducted performance testing according to FDA's Guidance for Industry and Food and Drug Administration Staff, "Computer-Assisted Detection Devices Applied to Radiology Images and Radiological Device Data- Premarket Notification (510(k)) Submissions," as part of the development of VELMENI for DENTISTS (V4D). Performance testing included standalone testing and clinical reader evaluation.
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Standalone Performance .
Standalone performance of the Velmeni for Dentists (V4D) was evaluated on a total of 600 Bitewing images, 597 Periapical images and 600 Panoramic images. Standalone performance was compared to ground truth established by consensus labels of three US licensed dentists, and nonconsensus labels were adjudicated by an oral radiologist.
Standalone testing included images form the following sensor manufacturers: Dexis, Dexis platinum, and Kavo used for bitewing and periapical image type and Kavo, Carestream, Planmeca sensor used for panoramic image type.
Standalone study assessed sensitivity, specificity and dice coefficient.
Table 2, Lesion-Level Sensitivity for Caries
| Caries | |||
|---|---|---|---|
| View | Lesion-Level Sensitivity( 95% CI1 ) | ||
| Bitewing Views | 72.8% (68.0%, 77.4%) | ||
| Periapical Views | 70.6% (64.1%, 76.6%) | ||
| Panoramic Views | 68.3% (64.9%, 71.8%) |
1 Two-sided bootstrap 95% Cl based on images with one or more caries and resampling of subjects.
2 Image classification for Sensitivity: TP = image with all lesions correctly identified. FN = image with at least one
lesion not identified.
Table 3, Lesion-level and case-level sensitivity for caries, fixed prothesis, implants and restorations and dice coefficient for lesion segmentation.
| Assessment | Results | ||
|---|---|---|---|
| Bitewing Views | Periapical Views | Panoramic Views | |
| Lesion-level Sensitivity ( 95% CI¹ ) | |||
| Fixed Prosthesis | 92.1% [90.0%, 94.1%] | 81.0% [76.6%, 85.4%] | 74.5% [71.5%, 77.3%] |
| Implant | 81.1% [67.6%, 92.1%] | 94.5% [90.0%, 98.2%] | 79.6% [69.7%, 87.4%] |
| Restoration | 88.1% [86.1%, 90.1%] | 76.8% [72.0%, 81.4%] | 72.6% [70.6%, 74.6%] |
| Case-level Sensitivity² ( 95% CI¹ ) | |||
| Caries | 59.5% [51.7%, 66.9%] | 57.5% [50.0%, 65.1%] | 45.0% [39.7%, 50.5%] |
| Fixed Prosthesis | 81.1% [75.7%, 86.3%] | 71.7% [62.5%, 79.9%] | 44.3% [38.0%, 50.6%] |
| Implant | 75.0% [59.1%, 88.9%] | 91.7% [84.8%, 97.2%] | 60.0% [45.0%, 74.4%] |
| Restoration | 71.6% [67.0%, 76.2%] | 68.1% [61.2%, 74.5%] | 27.2% [23.3%, 31.2%] |
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| False Positives Per Image Mean (95% CI¹) | |||
|---|---|---|---|
| Caries | 0.24 [0.19, 0.29] | 0.27 [0.22, 0.32] | 0.33 [0.28, 0.38] |
| Fixed Prosthesis | 0.03 [0.01, 0.05] | 0.01 [0.00, 0.02] | 0.06 [0.04, 0.08] |
| Implant | 0.00 [0.00, 0.01] | 0.00 [NA, NA] | 0.01 [0.00, 0.02] |
| Restoration | 0.15 [0.11, 0.19] | 0.10 [0.07, 0.13] | 0.62 [0.54, 0.70] |
| Case-level Specificity³ ( 95% CI¹) | |||
| Caries | 88.0% [84.4%, 91.2%] | 84.7% [81.4%, 87.9%] | 96.8% [94.6%, 98.6%] |
| Fixed Prosthesis | 98.2% [96.4%, 99.7%] | 99.7% [99.2%, 100.0%] | 97.7%[96.0%, 99.2%] |
| Implant | 100.0%[NA, NA] | 100.0% [NA, NA] | 100.0% [NA, NA] |
| Restoration | 93.3% [89.1%, 96.9%] | 95.1%[92.8%, 97.2%] | 83.3% [75.0%, 90.6%] |
| DICE Score Mean (95% CI¹) | |||
| Caries | 81.96% (80.81%, 83.10%) | 82.77% (81.41%, 84.13%) | 77.07% (76.25%, 77.89%) |
| Fixed Prosthesis | 97.09% (96.84%, 97.33%) | 96.23% (95.78%, 96.69%) | 91.47% (91.24%, 91.71%) |
| Implant | 94.20% (92.44%, 95.97%) | 95.47% (94.60%, 96.34%) | 88.67% (87.22%, 90.11%) |
| Restoration | 90.45% (90.06%, 90.84%) | 81% (88.97%, 90.64%) | 81.49% (81.19%, 81.78%) |
1 Two-sided 95% Cl obtained by bootstrapping analysis of subjects.
2 Image classification for Sensitivity: TP = image with all lesions correctly identified. FN = image with at least one lesion not identified.
3 Image classification for Specificity: FP = image without a positive lesion that has at least one false positive. TN = image without a positive lesion that has no false positives.
Subgroup analyses were performed among types of caries (primary and secondary caries; for caries-level sensitivity only), sex, age category, sensor, and study site.
Clinical Performance ●
Velmeni evaluated the performance of Velmeni for Dentists (V4D) in a multi-reader fully crossed reader improvement study. 12 US licensed dentists were asked to evaluate 600 bitewing images (total caries 315), 597 periapical images (total caries 271) and 600 panoramic images (total caries 853). Ground truth was established by the consensus labels of three US licensed dentists, and nonconsensus labels were adjudicated by an oral radiologist. Half of the data set contained unannotated images, and the second half contained radiographs that had been processed through the V4D model. Radiographs were presented to readers in alternating groups. In Session 1, readers were asked to outline suspected caries, fixed prosthesis, implant, and restorations and to review predictions from the V4D model. Each reader was asked to provide a rating of 25 - 100 for their confidence in the annotation (25 for lowest confidence, up to 100 for highest confidence). A 4-week washout period was utilized to limit recollection bias. Following the washout, the readers were presented with the same data set but with alternate grouping. i.e., if a reader saw a radiograph in the
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unpredicted state in session 1, they were presented with the same radiograph with V4D predictions in session 2, and vice versa. Results were compared against a consensus ground truth, and the sensitivity, specificity, and weighted alternative free response receiver operating characteristic (wAFROC) were evaluated to characterize the performance of the readers with (assisted) and without (unassisted) viewing the model annotations.
Findings were first summarized at the lesion level and then at the case (view) level. The V4D software demonstrated clear benefit for bitewing and periapical views in all features. The panoramic view demonstrated benefit though the absolute benefit for caries sensitivity was smaller due to lower overall reader performance. In addition, for the panoramic view, there was a benefit in restoration sensitivity that was somewhat offset by a drop in image-level specificity.
Table 4: wAFROC AUC at lesion- level for Caries Detection by Reader Mode
| View | Measure | Results | ||
|---|---|---|---|---|
| Aided | Unaided | Difference (95% CI) | ||
| Bitewing | wAFROC AUC | 0.848 | 0.794 | 0.054 (0.035, 0.073) |
| Periapical1 | wAFROC AUC | 0.814 | 0.721 | 0.093 (0.066, 0.120) |
| Panoramic | wAFROC AUC | 0.615 | 0.579 | 0.036 (0.022, 0.050) |
1 Excludes reader 1784 for Periapical View since the reader did not have all results for all aided assessments.
| Assessment | Measure | Results | |||
|---|---|---|---|---|---|
| Aided (95% CI) | Unaided (95% CI) | Difference (95% CI) | |||
| Bitewing | |||||
| Caries | Lesion-Level Sensitivity | 80.3% | 67.5% | 12.8% (9.9%, 15.9%) | |
| Mean FPs per Image | 0.18 | 0.18 | 0.00 (-0.03, 0.03) | ||
| Case-level Sensitivity | 69.5% | 51.5% | 18.1% (13.7%, 22.5%) | ||
| Case-level Specificity | 85.7% | 88.5% | -2.8% (-6.0%, 0.3%) | ||
| Fixed Prosthesis | Lesion-Level Sensitivity | 95.7% | 90.2% | 5.5% (3.6%, 7.6%) | |
| Mean FPs per Image | 0.04 | 0.13 | -0.08 (-0.11, -0.05) | ||
| Case-level Sensitivity | 89.6% | 82.7% | 6.8% (3.0%, 10.6%) | ||
| Case-level Specificity | 97.9% | 98.7% | -0.8% (-2.7%, 0.8%) | ||
| Implant | Lesion-Level Sensitivity | 93.2% | 61.3% | 32.0% (21.4%, 41.3%) | |
| Mean FPs per Image | 0.00 | 0.00 | 0.00 (-0.01, 0.00) | ||
| Case-level Sensitivity | 91.1% | 59.2% | 31.8% (21.2%, 42.9%) | ||
| Case-level Specificity | 100.0% | 99.9% | 0.1% (0.0%, 0.2%) | ||
| Restoration | Lesion-Level Sensitivity | 90.8% | 74.1% | 16.7% (14.3%, 19.2%) | |
| Mean FPs per Image | 0.15 | 0.29 | -0.14 (-0.18, -0.09) | ||
| Case-level Sensitivity | 77.8% | 52.7% | 25.1% (21.3%, 29.1%) | ||
| Case-level Specificity | 92.5% | 94.0% | -1.4% (-5.6%, 2.5%) | ||
| Assessment | Measure | Results | |||
| Aided (95% CI) | Unaided (95% CI) | Difference (95% CI) | |||
| Periapical1 | |||||
| Caries | Lesion-Level Sensitivity | 73.4% | 48.7% | 24.8% (19.8%, 29.8%) | |
| Mean FPs per Image | 0.19 | 0.08 | 0.11 (0.08, 0.14) | ||
| Case-level Sensitivity | 59.0% | 33.6% | 25.5% (19.6%, 31.3%) | ||
| Case-level Specificity | 84.2% | 94.5% | -10.3% (-13.0%, -7.6%) | ||
| Fixed Prosthesis | Lesion-Level Sensitivity | 91.1% | 80.0% | 11.1% (8.0%, 14.5%) | |
| Mean FPs per Image | 0.01 | 0.04 | -0.02 (-0.03, -0.01) | ||
| Case-level Sensitivity | 82.7% | 67.1% | 15.7% (10.6%, 20.9%) | ||
| Case-level Specificity | 99.7% | 99.5% | 0.2% (-0.2%, 0.6%) | ||
| Implant | Lesion-Level Sensitivity | 95.9% | 79.5% | 16.4% (12.7%, 20.3%) | |
| Mean FPs per Image | 0.00 | 0.01 | -0.01 (-0.01, 0.00) | ||
| Case-level Sensitivity | 93.8% | 77.5% | 16.3% (12.2%,21.0%) | ||
| Case-level Specificity | 99.9% | 100.0% | -0.1% (-0.4%, 0.0%) | ||
| Restoration | Lesion-Level Sensitivity | 90.6% | 80.3% | 10.3% (8.2%, 12.4%) | |
| Mean FPs per Image | 0.07 | 0.05 | -0.02 (0.00, 0.03) | ||
| Case-level Sensitivity | 83.9% | 69.4% | 14.5% (11.2%, 17.7%) | ||
| Case-level Specificity | 94.9% | 97.6% | -2.7% (-4.9%, -0.8%) | ||
| Panoramic | |||||
| Caries | Lesion-Level Sensitivity | 27.2% | 15.1% | 6.5% (4.5%, 8.6%) | |
| Mean FPs per Image | 0.21 | 0.30 | -0.09 (-0.13, -0.06) | ||
| Case-level Sensitivity | 11.5% | 8.5% | 3.0% (0.6%, 5.5%) | ||
| Case-level Specificity | 95.1% | 94.6% | 0.5% (-1.7%, 2.4%) | ||
| Fixed Prosthesis | Lesion-Level Sensitivity | 88.8% | 80.5% | 8.2% (6.1%, 10.3%) | |
| Mean FPs per Image | 0.07 | 0.18 | -0.10 (-0.13, -0.07) | ||
| Case-level Sensitivity | 70.5% | 67.0% | 3.5% (0.5%, 6.3%) | ||
| Case-level Specificity | 97.6% | 99.0% | -1.4% (-2.8%, -0.1%) | ||
| Implant | Lesion-Level Sensitivity | 88.3% | 79.6% | 8.7% (0.2%, 15.9%) | |
| Mean FPs per Image | 0.01 | 0.01 | 0.00 (-0.01, 0.01) | ||
| Case-level Sensitivity | 77.1% | 77.1% | 0.0% (-10.4%, 10.0%) | ||
| Case-level Specificity | 100.0% | 100.0% | 0.0% (NA, NA) | ||
| Restoration | Lesion-Level Sensitivity | 73.0% | 57.4% | 15.6% (14.3%, 16.9%) | |
| Mean FPs per Image | 0.73 | 1.02 | -0.29 (-0.37, -0.22) | ||
| Case-level Sensitivity | 26.7% | 19.6% | 7.1% (4.8%, 9.5%) | ||
| Case-level Specificity | 85.8% | 96.2% | -10.4% (-16.5%, -4.8%) |
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1 Excludes reader 1784 for Periapical View since the reader did not have all results for all aided assessments.
Subgroup analysis was also performed for readers' experience, gender, age, imaging sensors, collection sites and primary and secondary caries.
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Conclusion
Velmeni Inc. has the same intended use as the predicate device, specifically to detect pathological and/or non-pathological conditions on dental radiographs for use by trained dental professionals. While there are some small differences in technological characteristics between the proposed device, VELMENI for DENTISTS (V4D), and the predicate device, these differences do not raise different questions of safety and effectiveness. The results of the stand-alone and MRMC reader studies demonstrate that the performance of V4D is as safe, as effective, and performs equivalent to that of the predicate device, and VELMENI has demonstrated that the proposed device complies with applicable Special Controls for Medical Image Analyzers. Therefore, VELMENI for DENTISTS (V4D) can be found substantially equivalent to the predicate device.