(146 days)
Not Found
Yes
The document explicitly states that the device uses "Machine Learning Modules" and "uses machine learning to detect caries." It also mentions "computer vision technology, developed using machine learning techniques" and "neural network-based computer vision algorithms."
No
The device is a computer-aided detection (CADe) software designed to aid dentists in detecting caries by marking suspected regions on radiographs. It does not provide any treatment or therapeutic intervention.
Yes
The "Intended Use / Indications for Use" states that the device is "to aid dentists in the detection of caries" and "aid dental health professionals to review bitewing and periapical radiographs... as a second reader," which directly relates to diagnosing a condition. The "Device Description" also mentions it is "intended to aid in the detection of caries".
Yes
The device description explicitly states that Second Opinion CC consists of three software components (API, ML Modules, Client) and describes the processing of images through these software components. There is no mention of any accompanying hardware that is 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: Second Opinion® CC is a software device that analyzes radiographic images (bitewing and periapical radiographs) to aid in the detection of caries. It does not analyze biological samples from the patient's body.
- Intended Use: The intended use is to aid dental health professionals in reviewing radiographs, not to perform a diagnostic test on a biological sample.
Therefore, while it is a medical device that aids in diagnosis, it does not fit the definition of an In Vitro Diagnostic device.
No
The letter does not 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
Second Opinion® CC is a computer aided detection ("CADe") software to aid dentists in the detection of caries by drawing bounding polygons to highlight the suspected region of interest.
It is designed to aid dental health professionals to review bitewing and periapical radiographs of permanent teeth in patients 19 years of age or older as a second reader.
Product codes (comma separated list FDA assigned to the subject device)
MYN
Device Description
Second Opinion CC (Caries Contouring) is a radiological, automated, computer-assisted detection (CADe) software intended to aid in the detection of caries on bitewing and periapical radiographs using polygonal contours. The device is not intended as a replacement for a complete dentist's review or their clinical judgment which considers other relevant information from the image, patient history, or actual in vivo clinical assessment.
Second Opinion CC consists of three parts:
- Application Programing Interface ("API")
- Machine Learning Modules ("ML Modules")
- Client User Interface ("Client")
The processing sequence for an image is as follows:
- Images are sent for processing via the API 1.
- The API routes images to the ML modules
- The ML modules produce detection output
- The UI renders the detection output
The API serves as a conduit for passing imagery and metadata between the user interface and the machine learning modules. The API sends imagery to the machine learning modules for processing and subsequently receives metadata generated by the machine learning modules which is passed to the interface for rendering.
Second Opinion CC uses machine learning to detect caries. Images received by the ML modules are processed yielding detections which are represented as metadata. The final output is made accessible to the API for the purpose of sending to the UI for visualization. Detected carious lesions are displayed as polygonal overlays atop the original radiograph which indicate to the practitioner which teeth contain which detected carious lesions that may require clinical review. The clinician can toggle over the image to highlight a potential condition for viewing. In addition, the clinician has the ability to edit the detections as they see fit to align with their diagnosis.
Mentions image processing
Images are sent for processing via the API 1.
The API routes images to the ML modules
The ML modules produce detection output
The UI renders the detection output
The API serves as a conduit for passing imagery and metadata between the user interface and the machine learning modules. The API sends imagery to the machine learning modules for processing and subsequently receives metadata generated by the machine learning modules which is passed to the interface for rendering.
Second Opinion CC uses machine learning to detect caries. Images received by the ML modules are processed yielding detections which are represented as metadata. The final output is made accessible to the API for the purpose of sending to the UI for visualization. Detected carious lesions are displayed as polygonal overlays atop the original radiograph which indicate to the practitioner which teeth contain which detected carious lesions that may require clinical review.
Mentions AI, DNN, or ML
- Machine Learning Modules ("ML Modules")
- Second Opinion CC uses machine learning to detect caries. Images received by the ML modules are processed yielding detections which are represented as metadata.
- The cleared device is a computer aided detection (CADe) software device, indicated for use by dental health professionals as an aid in their assessment of bitewing and periapical radiographs of permanent teeth in patients 12 years of age or older, as second reader. The device utilizes computer vision technology, developed using machine learning techniques, to identify and mark potential caries lesions with bounding boxes.
- The Overiet Dental Carries Assist has the same technology (Al software) and intended use as CADe to aid in dental review of radiographs and detection of caries using polygonal contours.
- The fundamental technological principle for both the candidate and predicate devices is the automatic detection of caries using machine learning
- All devices use neural network-based computer vision algorithms for caries detection.
- Utilizes computer vision neural network algorithms, developed from open-source models using supervised machine learning techniques
- Automated, concurrent-read, CADe software that utilizes machine learning
Input Imaging Modality
bitewing and periapical radiographs
Anatomical Site
permanent teeth
Indicated Patient Age Range
patients 19 years of age or older
Intended User / Care Setting
dental health professionals / Not Found
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
The ground truth (GT) was established using the consensus approach based on agreement among at least three out of four expert readers. Each GT expert independently marked areas on any radiograph wherein they marked using the smallest possible polygonal contour to encompass the entire region identified. 500 images were reviewed by all four GT readers and used for the standalone comparison study.
The GT dataset for the clinical evaluation of the Second Opinion CC system is characterized by a diverse distribution of radiographs across various geographical regions, genders, ages, imaging devices, and image types. Geographically, with respect to the United States, the dataset includes 76 radiographs from the Northwest (15.2%), 89 from the Southwest (17.8%), 123 from the South (24.6%), 113 from the East (22.6%), 98 from the Midwest (19.6%), and 1 with an unknown origin (0.2%).
In terms of gender distribution, the dataset comprises 95 radiographs from females (19.0%), 125 from males (25.0%), 38 from other genders (7.6%), and 242 with unknown gender (48.4%). Age-wise, 9 radiographs are from individuals aged 12-18 (1.8%), 233 from those aged 18-75 (46.6%), 13 from those aged 75+ (2.6%), and 245 with unknown age (49.0%).
The imaging devices used include 65 radiographs from Carestream-Trophy KodakRVG6100 (13.0%), 2 from Carestream-Trophy RVG5200 (0.4%), 96 from Carestream-Trophy RVG6200 (19.2%), 1 from DEXIS (0.2%), 59 from DEXIS Platinum (11.8%), 10 from KaVo Dental Technologies DEXIS Titanium (2.0%), 108 from Kodak-Trophy KodakRVG6100 (21.6%), 68 from XDR EV71JU213 (13.6%), and 91 from unknown devices (18.2%).
The image types are nearly evenly split with 249 periapical radiographs (49.8%) and 251 bitewing radiographs (50.2%).
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Clinical evaluation of Second Opinion CC was performed to validate the efficacy of the system in detecting potential caries lesions using polygons instead of bounding boxes on intraoral radiographs. Second Opinion CC was clinically tested as a standalone device in comparison to the predicate device, Second Opinion, using a non-inferiority study.
The Weighted Alternative Free-Response Receiver Operating Characteristic (wAFROC) paradigm was used as the metric of efficacy for the study. The ground truth (GT) was established using the consensus approach based on agreement among at least three out of four expert readers. Each GT expert independently marked areas on any radiograph wherein they marked using the smallest possible polygonal contour to encompass the entire region identified. 500 images were reviewed by all four GT readers and used for the standalone comparison study.
The clinical study design consisted of one non-inferiority trial: comparing the ability of Second Opinion CC (polygonal localization) and Second Opinion (bounding box localization) to identify caries lesions. The 500 unique unannotated images were analyzed by both Second Opinion and Second Opinion CC.
Jaccard Index used for the study was 0.4. The primary performance comparison of Second Opinion CC Second Opinion was conducted using wAFROC-FOM. A secondary analysis was carried out using HR-ROC-AUC.
There were a total number of lesion localizations (mean per image) of 204 (1.5) and 322 (1.6) by Second Opinion and Second Opinion CC respectively resulting from the non-inferiority study. The Obuchowski-Rockette analysis of jackknife and ANOVA estimate of the FOM difference between Second Opinion devices showed the estimated difference (95% CI) was 0.26 (0.22, 0.31). The lower bound of the 95% CI exceeded -0.05 showing Second Opinion CC was non- inferior to Second Opinion at the 5% level of significance. The wAFROC-FOM, 95% Cl for Second Opinion CC was 0.81 (0.77, 0.85) and HR-ROC-AUC, 95% CI was 0.88 (0.85, 0.91). The null hypothesis was rejected.
The HR-ROC analysis further supports that Second Opinion CC's caries detection is non-inferior to that of Second Opinion. Second Opinion CC demonstrated a lesion level sensitivity (95% CI) of 90% (87%, 94%) and average false positives per image (95% CI) of 1.34 (1.20, 1.48).
The segmentation accuracy was assessed to demonstrate clinically-acceptable accuracy using the Dice coefficient. The least squares (LS) mean (95% CI) for the Dice coefficient for true positives (n=322) was 0.73 (0.71, 0.75). The LS mean and 95% Cl are from a linear mixed model with subject as random effect since more than one true positive may be on each image. The lower bound of the 95% CI exceeds the pre-defined clinically justified acceptance criteria of > 0.70.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Sensitivity (lesion level, 95% CI): 90% (87%, 94%)
Average false positives per image (95% CI): 1.34 (1.20, 1.48)
Dice coefficient for true positives (Least Squares Mean, 95% CI): 0.73 (0.71, 0.75)
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s):
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
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.
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Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). The FDA logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA acronym with the full name of the agency on the right. The FDA part of the logo is in blue, with the acronym in a square and the full name written out to the right of the square.
January 16, 2025
Pearl Inc. % William Birdsall Chief Compliance Officer 2515 Benedict Canyon Dr. BEVERLY HILLS, CA 90210
Re: K242522
Trade/Device Name: Second Opinion CC Regulation Number: 21 CFR 892.2070 Regulation Name: Medical Image Analyzer Regulatory Class: Class II Product Code: MYN Dated: August 1, 2024 Received: December 18, 2024
Dear William Birdsall:
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.
1
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).
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.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
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 medical devices and radiation-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-regulatory
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assistance/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).
Sincerely,
Lu Jiang
Lu Jiang, Ph.D. Assistant Director Diagnostic X-Ray Systems Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
3
Indications for Use
510(k) Number (if known) K242522
Device Name Second Opinion CC
Indications for Use (Describe)
Second Opinion® CC is a computer aided detection ("CADe") software to aid dentists in the detection of caries by drawing bounding polygons to highlight the suspected region of interest.
It is designed to aid dental health professionals to review bitewing and periapical radiographs of permanent teeth in patients 19 years of age or older as a second reader.
| 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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510(K) SUMMARY
1. Submitter's Identification
Pearl Inc. 2515 Benedict Canyon Dr. Beverly Hills, CA, 90210 USA (239) 450-8829
Contact Person: William Birdsall
Position: Chief Compliance Officer
Date Summary Prepared: July 23, 2024
2. Trade Name of the Device
Second Opinion CC
3. Common or Usual Name
Medical Image Analyzer
4. Classification Name. Requlatory Classification & Product Code
Classification Name: Medical Image Analyzer Regulatory Classification: 21CFR 892.2070, Class II Product Code: MYN
5. Predicate Information
Predicate device: The proposed primary predicate device is the first clearance of the Second Opinion device, by Pearl Inc., cleared on March 04, 2022 (K210365) classified as a Class II Medical Image Analyzer pursuant to 21 CFR §892.2070, under product code MYN (Analyzer, Medical Image).
The cleared device is a computer aided detection (CADe) software device, indicated for use by dental health professionals as an aid in their assessment of bitewing and periapical radiographs of permanent teeth in patients 12 years of age or older, as second reader. The device utilizes computer vision technology, developed using machine learning techniques, to identify and mark potential caries lesions with bounding boxes.
The subject device is substantially equivalent to the first clearance as the overall intended use and nature of the software remains the same. Caries detection was originally cleared (K210365) based on standalone and MRMC studies. Second Opinion CC has the same intended use for detection of caries in bitewing and periapical radiographs. The only
5
K242522
difference is that Second Opinion CC uses polygons instead of boxes to mark the detection.
The proposed secondary predicate device is Overjet Dental Caries Assist by Overiet Inc., cleared on March 27, 2023 (K222746) under product code MYN. The Overiet Dental Carries Assist has the same technology (Al software) and intended use as CADe to aid in dental review of radiographs and detection of caries using polygonal contours.
6. Device Description
Second Opinion CC (Caries Contouring) is a radiological, automated, computer-assisted detection (CADe) software intended to aid in the detection of caries on bitewing and periapical radiographs using polygonal contours. The device is not intended as a replacement for a complete dentist's review or their clinical judgment which considers other relevant information from the image, patient history, or actual in vivo clinical assessment.
Second Opinion CC consists of three parts:
- · Application Programing Interface ("API")
- · Machine Learning Modules ("ML Modules")
- · Client User Interface ("Client")
The processing sequence for an image is as follows:
- Images are sent for processing via the API 1.
-
- The API routes images to the ML modules
-
- The ML modules produce detection output
-
- The UI renders the detection output
The API serves as a conduit for passing imagery and metadata between the user interface and the machine learning modules. The API sends imagery to the machine learning modules for processing and subsequently receives metadata generated by the machine learning modules which is passed to the interface for rendering.
Second Opinion CC uses machine learning to detect caries. Images received by the ML modules are processed yielding detections which are represented as metadata. The final output is made accessible to the API for the purpose of sending to the UI for visualization. Detected carious lesions are displayed as polygonal overlays atop the original radiograph which indicate to the practitioner which teeth contain which detected carious lesions that may require clinical review. The clinician can toggle over the image to highlight a potential condition for viewing. In addition, the clinician has the ability to edit the detections as they see fit to align with their diagnosis.
7. Indications for Use
Second Opinion CC is a computer aided detection ("CADe") software to aid dentists in the detection of caries by drawing bounding polygons to highlight the suspected region of interest.
6
It is designed to aid dental health professionals to review bitewing and periapical radiographs of permanent teeth in patients 19 years of age or older as a second reader.
8. Summary of Substantial Equivalence:
The primary and secondary predicate devices and subject device are similar CADe devices in the following ways:
-
- Intended use: All three devices are intended to be used to aid dental clinicians in their detection of caries in radiographs of permanent teeth.
-
- Technology characteristics: Both devices employ computer vision and machine learning to output detections, use cloud-based environments to conduct processing, and demarcate detected caries within a user interface with a graphical overlay over radiographs.
-
- Safety: As both the candidate and predicate device are CADe systems, neither pose a direct safety hazard to the patient. The primary hazards for all devices, subject and predicates, are potential false positives and false negatives. In the case of each device, users are not meant to rely solely on detection output for clinical decision-making.
-
- Clinical Performance: Both devices have undergone clinical studies which demonstrate statistically significant improvement in aided reader performance.
| | Subject Device
Second Opinion CC | Primary Predicate
Second Opinion
K210365 | Secondary Predicate
Overjet
K222746 |
|-------------------|-------------------------------------------------------------|----------------------------------------------------------|----------------------------------------------------------|
| Manufacturer | Pearl Inc. | Pearl Inc. | Overjet, Inc. |
| Classification | 892.2070 | 892.2070 | 892.2070 |
| Product Code | MYN | MYN | MYN |
| Image
Modality | Radiograph | Radiograph | Radiograph |
| Intended Use | Dental CADe to aid in
dental radiograph review
by HCP | Dental CADe to aid in dental
radiograph review by HCP | Dental CADe to aid in dental
radiograph review by HCP |
Table 1: Comparison of Second Opinion CC with the predicate devices.
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| Full IFU | Second Opinion CC is a
computer aided detection
("CADe") software to aid
dentists in the detection of
caries by drawing bounding
polygons to highlight the
suspected region of
interest.
It is designed to aid dental
health professionals to
review bitewing and
periapical radiographs of
permanent teeth in patients
19 years of age or older as
a second reader. | 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 | Overjet Caries Assist (OCA) is a
radiological, automated,
concurrent-read, computer-
assisted detection (CADe)
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
a tooth surface suspected of being
carious. The device is not
intended as a replacement for a
complete dentist's review or their
clinical judgment that takes into
account other relevant information |
|----------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| | Subject Device
Second Opinion CC | Primary Predicate
Second Opinion
K210365 | Secondary Predicate
Overjet
K222746 |
|-----------------------|---------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
| Intended
body part | Dental | Dental | Dental |
| Technology | Utilizes computer vision
neural network algorithms,
developed from
open-source models using
supervised machine
learning techniques | Utilizes computer vision neural
network algorithms, developed
from open-source models
using supervised machine
learning techniques | Automated, concurrent-read,
CADe software that utilizes
machine learning |
| Device
Description | Detection of caries using
polygons. | Detection of radiological dental
findings: 5 restorations
(crowns, bridges, implants, root
canals, fillings), 4 pathologies
(caries, margin discrepancy,
calculus, periapical
radiolucency) using bounding
boxes. | Detection and segmentation of
caries using polygons. |
9. Technological Comparison to Predicate Devices
The fundamental technological principle for both the candidate and predicate devices is the automatic detection of caries using machine learning
The candidate and predicate devices are technologically equivalent as follows:
- All are software devices designed to run on Windows operating systems. •
- All devices are designed to process digital intraoral bitewing radiographs. •
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- . All devices use neural network-based computer vision algorithms for caries detection.
- . All devices demarcate detections within the user interface with a graphical overlay on the radiograph.
- All devices produce near-instantaneous detection results.
- . All devices are considered to be of "moderate" level of software concern.
- All devices passed all verification and validation testing requirements.
The candidate and predicate devices are technologically different as follows:
- Second Opinion localizes caries detections as bounding boxes whereas Overjet . Caries Assist and Second Opinion CC localize caries detections with polygons.
Assessment of Benefit-Risk, Safety and Effectiveness, and 10. Substantial Equivalence to Predicate Device
Pearl demonstrated the benefits of the device through a non-inferiority standalone clinical study. The results of the study showed statistically significant non-inferiority in caries detection accuracy in the subject device using polygon contouring to delineate potential caries when compared to the bounding boxes of Second Opinion. This study was conducted using ground truth caries lesions represented as polygons. When the probable benefits and probable risks of Second Opinion CC are weighed against one another, the weight of benefits significantly exceeds that of risks. This judgement can be made based on review of the submitted materials showing that Second Opinion CC meets the design verification and validation and labeling Special Controls required for clearance of Class II medical image analyzers. It is thus concluded that Second Opinion CC can be considered safe and effective such that the device will aid users in the indicated user population in their radiographic detection of caries.
11. Cybersecurity
Pearl developed Security controls and processes in accordance with Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions: Guidance for Industry and Food and Drug Administration Staff dated September 2023 and Postmarket Management of Cybersecurity in Medical Devices: Guidance for Industry and Food and Drug Administration Staff dated December 2016. These processes are used in both the development of Second Opinion CC and in post-market surveillance to ensure the product upholds the highest standards of privacy and security.
12. Discussion of Non-Clinical Tests Performed
The device is a software-only device, so most testable characteristics common to other device types, including Biocompatibility/Materials, Shelf Life/Sterility, Electromagnetic Compatibility and Electrical Safety, Magnetic Resonance (MR) Compatibility, are not applicable to this device.
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13. Discussion of StandaloneTests Performed
Pearl has 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 Radiology Device Data - Premarket Notification [510(k)] Submissions Document" issued on 03 Jul 2012. Clinical evaluation of Second Opinion CC was performed to validate the efficacy of the system in detecting potential caries lesions using polygons instead of bounding boxes on intraoral radiographs. Second Opinion CC was clinically tested as a standalone device in comparison to the predicate device, Second Opinion, using a non-inferiority study.
The Weighted Alternative Free-Response Receiver Operating Characteristic (wAFROC) paradigm was used as the metric of efficacy for the study. The ground truth (GT) was established using the consensus approach based on agreement among at least three out of four expert readers. Each GT expert independently marked areas on any radiograph wherein they marked using the smallest possible polygonal contour to encompass the entire region identified. 500 images were reviewed by all four GT readers and used for the standalone comparison study.
The GT dataset for the clinical evaluation of the Second Opinion CC system is characterized by a diverse distribution of radiographs across various geographical regions, genders, ages, imaging devices, and image types. Geographically, with respect to the United States, the dataset includes 76 radiographs from the Northwest (15.2%), 89 from the Southwest (17.8%), 123 from the South (24.6%), 113 from the East (22.6%), 98 from the Midwest (19.6%), and 1 with an unknown origin (0.2%).
In terms of gender distribution, the dataset comprises 95 radiographs from females (19.0%), 125 from males (25.0%), 38 from other genders (7.6%), and 242 with unknown gender (48.4%). Age-wise, 9 radiographs are from individuals aged 12-18 (1.8%), 233 from those aged 18-75 (46.6%), 13 from those aged 75+ (2.6%), and 245 with unknown age (49.0%).
The imaging devices used include 65 radiographs from Carestream-Trophy KodakRVG6100 (13.0%), 2 from Carestream-Trophy RVG5200 (0.4%), 96 from Carestream-Trophy RVG6200 (19.2%), 1 from DEXIS (0.2%), 59 from DEXIS Platinum (11.8%), 10 from KaVo Dental Technologies DEXIS Titanium (2.0%), 108 from Kodak-Trophy KodakRVG6100 (21.6%), 68 from XDR EV71JU213 (13.6%), and 91 from unknown devices (18.2%).
The image types are nearly evenly split with 249 periapical radiographs (49.8%) and 251 bitewing radiographs (50.2%). Below is a table describing the composition of the dataset with respect to presence of caries.
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Table 2 - Summary of image characteristics.
| | Total
N=500 |
|----------------------------------------------------------|----------------|
| Overall status, n (%) | |
| Healthy | 288 (57.6) |
| Containing Caries | 212 (42.4) |
| Number of lesions on abnormal images | |
| Total, n | 356 |
| Mean (SD) | 1.7 (1.23) |
| Median (range) | 1.0 (1, 9) |
| Lesion size on abnormal images (percent of image pixels) | |
| Mean (SD) | 1.5 (1.95) |
| Median (range) | 0.6 (0,11) |
The clinical study design consisted of one non-inferiority trial: comparing the ability of Second Opinion CC (polygonal localization) and Second Opinion (bounding box localization) to identify caries lesions. The 500 unique unannotated images were analyzed by both Second Opinion and Second Opinion CC. The results for each image were grouped into one of two categories:
- Non-Lesion Localization (NL): A predicted region which does not overlap ● with a ground truth region at or above a specified Jaccard index
- . Lesion Localization (LL): A predicted region which does overlap with a ground truth region at or above a specified Jaccard index
The Jaccard Index used for the study was 0.4. The primary performance comparison of Second Opinion CC Second Opinion was conducted using wAFROC-FOM. A secondary analysis was carried out using HR-ROC-AUC.
There were a total number of lesion localizations (mean per image) of 204 (1.5) and 322 (1.6) by Second Opinion and Second Opinion CC respectively resulting from the non-inferiority study. The Obuchowski-Rockette analysis of jackknife and ANOVA estimate of the FOM difference between Second Opinion devices showed the estimated difference (95% CI) was 0.26 (0.22, 0.31). The lower bound of the 95% CI exceeded -0.05 showing Second Opinion CC was non- inferior to Second Opinion at the 5% level of significance. The wAFROC-FOM, 95% Cl for Second Opinion CC was 0.81 (0.77, 0.85) and HR-ROC-AUC, 95% CI was 0.88 (0.85, 0.91). The null hypothesis was rejected.
The HR-ROC analysis further supports that Second Opinion CC's caries detection is non-inferior to that of Second Opinion. Second Opinion CC demonstrated a lesion level sensitivity (95% CI) of 90% (87%, 94%) and average false positives per image (95% CI) of 1.34 (1.20, 1.48).
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The segmentation accuracy was assessed to demonstrate clinically-acceptable accuracy using the Dice coefficient. The least squares (LS) mean (95% CI) for the Dice coefficient for true positives (n=322) was 0.73 (0.71, 0.75). The LS mean and 95% Cl are from a linear mixed model with subject as random effect since more than one true positive may be on each image. The lower bound of the 95% CI exceeds the pre-defined clinically justified acceptance criteria of > 0.70.
14. Conclusions
Based on the information presented above, Second Opinion CC and its primary predicate device, Second Opinion, are deemed to have similar intended uses as devices which aid in the detection of caries lesions that can appear in dental radiographic imagery. Second Opinion CC's clinical trial results demonstrate that the device effectively performs as well as the primary predicate device.
As Second Opinion CC raises no new or different questions of safety or effectiveness, performs in accordance with its specifications, meets user needs, meets the intended use and therefore was found substantially equivalent to the predicate devices.