(104 days)
Overjet Periapical Radiolucency (PARL) Assist is a radiological, automated, concurrent read computer-assisted detection software intended to aid in the detection of periapical radiolucencies on permanent teeth captured on periapical radiographs. The device provides additional aid for the dentist to use in their identification of periapical radiolucency. The device is not intended as a replacement for a complete dentist's review or their clinical judgment that considers other relevant information from the image or patient history. The system is to be used by professionally trained and licensed dentists.
The Overjet Periapical Radiolucency Assist software is indicated for use on patients 12 years of age or older.
Overjet Periapical Radiolucency "PARL" Assist is a module within the Overjet Platform. The Overjet PARL Assist (OPA) software automatically detects periapical radiolucency on periapical radiographs. It is intended to aid dentists in the detection of periapical radiolucency. It should not be used in lieu of full patient evaluation or solely relied upon to make or confirm a diagnosis. The system is to be used by professionally trained and licensed dentists. Overjet PARL Assist is a software-only device which operates in three layers: a Network Layer, aPresentation Layer, and a Decision Layer. Images are pulled in from a clinic/dental office, and the Machine Learning model creates predictions in the Decision Layer and results are pushed to the dashboard, which are in the Presentation Layer.
Here's a detailed breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary:
Acceptance Criteria and Device Performance
| Acceptance Criteria | Reported Device Performance | Study Type |
|---|---|---|
| Human-in-the-Loop Performance (MRMC Study) | ||
| Image-level AUC improvement (assisted vs. unassisted readers) | 4.8% (95% CI: 0.030, 0.066) improvement | MRMC Reader Study |
| P-value for AUC improvement | < 0.001 (highly statistically significant) | MRMC Reader Study |
| Image-level sensitivity improvement (assisted vs. unassisted readers) | 13.6% (95% CI: 0.110, 0.165) improvement | MRMC Reader Study |
| Image-level specificity change (assisted vs. unassisted readers) | -7.1% (95% CI: -0.099, -0.042) decrease (from 83.2% to 76.1%) | MRMC Reader Study |
| Polygon (instance) level sensitivity improvement (assisted vs. unassisted readers) | 16.2% (95% CI: 0.125, 0.194) improvement | MRMC Reader Study |
| Standalone Performance (Algorithm Only) | ||
| Image-level sensitivity | 88% (95% CI: 0.847, 0.914) | Standalone Performance Testing |
| Image-level specificity | 82.2% (95% CI: 0.810, 0.847) | Standalone Performance Testing |
| Polygon (instance) level sensitivity | 66.4% (95% CI: 0.615, 0.711) | Standalone Performance Testing |
Study Details
2. Sample size used for the test set and the data provenance:
- MRMC Reader Study Test Set: 379 periapical radiographs (190 images with at least 1 PARL and 189 images with no PARL).
- Data Provenance: Images were obtained from various clinical sites across the U.S. from male and female patients aged 12 years and older. (Retrospective, implied, as it's a collected dataset for testing).
- Standalone Performance Testing Test Set: 763 periapical images + 384 additional images, totaling 1147 images.
- Data Provenance: Images were obtained from various clinical sites across the U.S. from male and female patients aged 12 years and older. (Retrospective, implied).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Number of Experts: 3
- Qualifications of Experts: Endodontists (specific experience/years not detailed, but their specialty implies relevant expertise).
4. Adjudication method for the test set:
- Adjudication Method: Consensus reference standard established by 3 endodontists. (This implies all three experts agreed on the ground truth for each case; no specific 2+1 or 3+1 method is explicitly stated, but "consensus" suggests agreement among all three).
5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:
- MRMC Comparative Effectiveness Study: Yes, an MRMC fully crossed reader improvement study was conducted.
- Effect Size / Improvement with AI Assistance:
- Image-level AUC: 4.8% (95% CI: 0.030, 0.066) improvement in assisted readers compared to unassisted readers.
- Image-level sensitivity: Average increase of 13.6% (0.110, 0.165) for assisted readers.
- Image-level specificity: Decreased by 7.1% from 83.2% to 76.1% (-0.071 difference, Cls (-0.099, -0.042)).
- Instance (polygon) level sensitivity: Average increase of 16.2% (0.125, 0.194) for assisted readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Standalone Performance Testing: Yes, standalone performance of the Overjet PARL Assist device was evaluated.
7. The type of ground truth used:
- Ground Truth Type: Expert consensus from 3 endodontists.
8. The sample size for the training set:
- The document does not explicitly state the sample size for the training set. It only provides details for the test sets used in the MRMC reader study and standalone performance testing.
9. How the ground truth for the training set was established:
- The document does not explicitly state how the ground truth for the training set was established. It only describes the ground truth establishment for the test sets via consensus of 3 endodontists.
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September 21, 2023
Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Overjet, Inc. % Deepthi Paknikar Regulatory & Clinical Manager 560 Harrison Ave #403 BOSTON MA 02118
Re: K231678
Trade/Device Name: Overjet Periapical Radiolucency Assist Regulation Number: 21 CFR 892.2070 Regulation Name: Medical image analyzer Regulatory Class: Class II Product Code: MYN Dated: August 15, 2023 Received: August 15, 2023
Dear Deepthi Paknikar:
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 (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 located 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.
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 803) for
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devices or postmarketing safety reporting (21 CFR 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 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 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).
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
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Indications for Use
510(k) Number (if known) K231678
Device Name Overjet Periapical Radiolucency Assist
Indications for Use (Describe)
Overiet Perianical Radiolucency (PARL) Assist is a radiological, automated, concurrent read computer-assisted detection software intended to aid in the detection of periapical radiolucencies on permanent teeth captured on periapical radiographs. The device provides additional aid for the dentist to use in their identification of periapical radiolucency. The device is not intended as a replacement for a complete dentist's review or their clinical judgment that considers other relevant information from the image or patient history. The system is to be used by professionally trained and licensed dentists.
The Overjet Periapical Radiolucency Assist software is indicated for use on patients 12 years of age or older.
| 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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K231678
510(k) Summary
Prepared on: 2023-06-08
Contact Details
21 CFR 807.92(a)(1)
21 CFR 807.92(a)(5)
| Applicant Name | Overjet, Inc |
|---|---|
| Applicant Address | 560 Harrison Ave #403 Boston MA 02118 United States |
| Applicant Contact Telephone | 6302011612 |
| Applicant Contact | Deepthi Paknikar DDS, MS |
| Applicant Contact Email | deepthi.paknikar@overjet.ai |
21 CFR 807.92(a)(2)
| Device Trade Name | Overjet Periapical Radiolucency Assist |
|---|---|
| Common Name | Medical image analyzer |
| Classification Name | Analyzer, Medical Image |
| Regulation Number | 892.2070 |
| Product Code | MYN |
21 CFR 807.92(a)(3)
| Predicate # | Predicate Trade Name (Primary Predicate is listed first) | Product Code |
|---|---|---|
| K221921 | DTX Studio Clinic 3.0 | MYN |
| K210365 | Second Opinion® | MYN |
21 CFR 807.92(a)(4)
Overjet Periapical Radiolucency "PARL" Assist is a module within the Overjet Platform. The Overjet PARL Assist (OPA) software automatically detects periapical radiolucency on periapical radiographs. It is intended to aid dentists in the detection of periapical radiolucency. It should not be used in lieu of full patient evaluation or solely relied upon to make or confirm a diagnosis. The system is to be used by professionally trained and licensed dentists. Overjet PARL Assist is a software-only device which operates in three layers: a Network Layer, aPresentation Layer, and a Decision Layer. Images are pulled in from a clinic/dental office, and the Machine Learning model creates predictions in the Decision Layer and results are pushed to the dashboard, which are in the Presentation Layer.
Intended Use/Indications for Use
Overjet Periapical Radiolucency (PARL) Assist is a radiological, automated, concurrent read computer-assisted detection software intended to aid in the detection of periapical radiolucencies on periapical radiographs. The device provides additional aid for the dentification of periapical radiolucency. The evice is not intended as a replacement for a complete dentist's review or their clinical judgment that considers other relevant information from the image or patient history. The system is to be used by professionally trained and licensed dentists.
The Overjet Periapical Radiolucency Assist software is indicated for use on patients 12 years of age or older.
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Indications for Use Comparison
The minor differences between the Overjet PARL Assist subject device and the Primary Predicate Device DTX Studio Clinic 3.0 (K221921) and secondary predicate device Pearl Second opinion (K210365) do not constitute a new intended use. The Overjet PARL Assist device shares the same intended use as the primary and secondary predicates, and the device is intended to aid in the detection of PARL on 2D periapical radiographs. The primary predicate device is intended to be used for ages 15+ and the secondary predicate device is intended for ages 12+, same as the Overjet PARL Assist Device. The Overjet PARL Assist Device is a concurrent read device same as the primary predicate device. The primary predicate device outputs a toggleable bounding box whereas the Overjet a segmented polygon of PARL. Both devices are intended to be diagnostic aids, and provide information for clinicians to use as additional information in their clinical examinations. The performance testing for the Overjet PARL Assist device demonstrates that aided readers improve in detection of PARL over unaided readers. In summary, the Overjet PARL Assist Device is substantially equivalent to the predicate device.
Technological Comparison
21 CFR 807.92(a)(6)
The primary predicate device outputs a toggleable bounding box whereas the Overjet device outputs a segmented polygon of PARL. The minor difference in technological characteristics do not raise a concern of substantial equivalence as demonstrated by the performance testing of the Overjet device.
Non-Clinical and/or Clinical Tests Summary & Conclusions 21 CFR 807.92(b)
MRMC Reader Study
Overjet evaluated the Overjet PARL Assist (OPA) in a multi case (MRMC) fully crossed reader improvement study. 19 US licensed dentists were asked to evaluate 379 periapical radiographs (190 images with at least 1 PARL and 189 images with no PARL), lmaqes were obtained from male and female patients aged 12 years and older. The consensus reference standard established by 3 endodontists.
Half of the data set contained unassisted images not run on the OPA device), and the second half contained radiographs that had been "assisted" or processed through the OPA device. The radiographs were presented to the readers in alternating groups. A 4 week washout period was utilized to limit recollection bias. Following the washout, the readers were presented the same data set but with alternate grouping. This means that if a reader saw a radiograph in the unassisted state in Session 1, the Overjet PARL Assist predictions in Session 2.
The results were compared against a consensus ground truth, and the curve (AUC) of the receiver operating characteristic curve (ROC) was evaluated as a primary endpoint to characterize the readers assisted and unassisted by the Overjet PARL Assist device.
The AUC of the ROC at the image level avers all readers showed a 4.8 % (95% Cl's 0.030, 0.066) improvement in assisted readers compared to unassisted readers. The p-value was highly statistically significant at <0.001.
Average Image level sensitivity across all readers increased by 13.6% (0.110, 0.165) when compared to unassisted readers. The average specificity at the image level decreased slightly from 83.2% to 76.1% (-0.071 difference, Cls (-0.099, -0.042)). Reader improvement for assisted readers at the instance (polygon) level sensitivity averaged across all readers was 16.2% (0.125, 0.194),
Standalone Performance Testing
Standalone performance of the Overjet PARL Assist device was evaluated for 763 periapical images + 384 additional standalone study images). The dataset was split with 326 images with no PARL. Images were obtained from male and female patients aged 12 years and older from various clinical sites across the U.S.. The results were compared to the consensus reference standard established by 3 endodontists.
Image level standalone sensitivity was 88%, 95% Cl's (0.847, 0.914). Image Level standalone specificity was 8.2%, 95% Cl's (0.810, 0.847). Polygon (instance) level standalone sensitivity was 66.4%, 95% Cl's (0.615, 0.711).
lmage Level Standalone Sensitivity and Specificity by sensor category is as follows: Dexis - Sensitivity: 0.867 (0.800, 0.933) Specificity: 0.885 (0.827, 0.942) e2v - Sensitivity: 0.861 (0.785, 0.937) Specificity: 0.804 (0.728, 0.875) Gendex - Sensitivity: 0.889 (0.815, 0.951) Specificity: 0.793 (0.712, 0.865) Schick - Sensitivity: 0.908 (0.836, 0.961) Specificity: 0.891 (0.827, 0.945)
21 CFR 807.92(a)(5)
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Not Applicable - the 2 types of device performance testing in this submission include standalone and MRMC performance testing as described in the guidance documents "Computer-Assisted Detection Devices
Applied to Radiology Images and Radiology Device Data - Premarket Notification [510(k)] Submissions" and
"Clinical Performance Assessment: Consider-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data in Premarket Notification (510(k)) Submissions".
The results of the MRMC reader performance assessment demonstrates that readers assisted by the Overjet PARL Assist device improve in detection of PARL when compared to unassisted readers.
§ 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.