Overjet Calculus Assist (OCalA) is a radiological automated concurrent-read computer-assisted detection software intended to aid in the detection of interproximal calculus deposits on both bitewing and periapical radiographs. The Overjet Calculus Assist surrounds suspected calculus deposits with a bounding box. The device provides additional information for the dentist to use in their diagnosis of a tooth surface suspected of containing calculus deposits. 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 from the image or patient history. The system is to be used by professionally trained and licensed dentists.

Device Description

Overjet Calculus Assist is a module within the Overjet Platform. The Overjet Calculus Assist (OCalA) software automatically detects interproximal calculus on bitewing and periapical radiographs. It is intended to aid dentists in the detection of calculus. 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.

AI/ML Overview

Here's an analysis of the acceptance criteria and study findings for the Overjet Calculus Assist device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

While specific acceptance criteria thresholds are not explicitly stated as numerical values in the document (e.g., "Sensitivity must be >= X%"), the document describes the performance testing conducted and implies that these results met the pre-specified requirements. The performance presented is what the FDA reviewed and deemed acceptable for clearance.

Metric (Type of Test)	Acceptance Criteria (Implied)	Reported Device Performance
Standalone Performance	Meets pre-specified requirements for sensitivity and specificity in calculus detection.	Sensitivity: - Bitewing: 74.1% (95% CI: 66.2%, 82.0%) - Periapical: 72.9% (95% CI: 65.3%, 80.5%) Specificity: - Bitewing: 99.4% (95% CI: 99.1%, 99.6%) - Periapical: 99.6% (95% CI: 99.3%, 99.8%) AFROC AUC: - Bitewing: 0.859 (95% CI: 0.823, 0.894) - Periapical: 0.867 (95% CI: 0.828, 0.903)
Clinical Performance (Reader Improvement)	Demonstrates superiority of aided reader performance versus unaided reader performance.	Reader Sensitivity (Unassisted vs. Assisted): - Bitewing: Improved from 74.9% (68.3%, 80.2%) to 84.0% (78.8%, 88.2%) - Periapical: Improved from 74.7% (69.9%. 79.0%) to 84.4% (78.8%, 89.2%) Reader Specificity (Unassisted vs. Assisted): - Bitewing: Decreased slightly from 98.8% (98.7%, 99.0%) to 98.6% (98.4%, 98.9%) - Periapical: Decreased slightly from 98.1% (97.8%, 98.4%) to 98.0% (97.7%, 98.4%) Reader AFROC AUC (Unassisted vs. Assisted - Average of all readers): - Bitewing: Increased from 0.840 (0.800, 0.880) to 0.878 (0.844. 0.913) (p-value 0.0055) - Periapical: Increased from 0.846 (0.808. 0.884) to 0.900 (0.870, 0.929) (p-value 1.47e-05)

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

Standalone Test Set:
- Bitewing Radiographs: 296
- Periapical Radiographs: 322
- Total Surfaces (Bitewing): 6,121
- Total Surfaces (Periapical): 3,595
- Data Provenance: Not explicitly stated, but subgroup analyses for "sensor" and "clinical site" suggest real-world, diverse data. The document does not specify if the data was retrospective or prospective, or the country of origin.
Clinical Evaluation (Reader Improvement) Test Set:
- Bitewing Radiographs: 292 (85 with calculus, 211 without calculus)
- Periapical Radiographs: 322 (89 with calculus, 233 without calculus)
- Data Provenance: Not explicitly stated regarding retrospective/prospective or geographical origin.

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

Ground Truth Establishment for Clinical Evaluation Test Set:
- Number of Experts: 3 US-licensed dentists formed a consensus for initial labeling. An oral radiologist provided adjudication for non-consensus labels.
- Qualifications of Experts: "US-licensed dentists" and an "oral radiologist." Specific years of experience or specialization within dentistry beyond "oral radiologist" are not provided.

4. Adjudication Method for the Test Set

Clinical Evaluation Test Set Adjudication:
- Ground truth was established by consensus labels of three US-licensed dentists.
- Non-consensus labels were adjudicated by an oral radiologist. This effectively represents a 3-reader consensus with a 1-reader expert adjudication for disagreements.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done, What was the Effect Size of How Much Human Readers Improve with AI vs Without AI Assistance?

Yes, an MRMC comparative effectiveness study was done. It was described as a "multi-reader, fully crossed reader improvement study."
Effect Size (Improvement with AI vs. without AI assistance):
- Sensitivity Improvement:
  - Bitewing: 9.1% (84.0% - 74.9%)
  - Periapical: 9.7% (84.4% - 74.7%)
- AFROC AUC Improvement (Reader Average):
  - Bitewing: 0.038 (0.878 - 0.840), with a p-value of 0.0055 (statistically significant)
  - Periapical: 0.054 (0.900 - 0.846), with a p-value of 1.47e-05 (statistically significant)
- Specificity: There was a slight decrease in specificity (0.1-0.2%) when assisted, which is common in CADe systems where increased sensitivity might lead to a minor trade-off in specificity.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done

Yes, a standalone performance test was conducted.
- The results are detailed in the "Standalone Testing" section, including sensitivity, specificity, and AFROC AUC for the AI algorithm alone.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

For both Standalone and Clinical Evaluation Studies:
- The ground truth was established by expert consensus of US-licensed dentists, with adjudication by an oral radiologist for disagreements. This is a type of "expert consensus" ground truth. The document does not mention pathology or outcomes data.

8. The Sample Size for the Training Set

The document does not provide the sample size of the training set for the AI model. It only details the test set used for performance evaluation.

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

The document does not specify how the ground truth for the training set was established. It only describes the ground truth methodology for the test set used in performance validation.

Summary

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December 16, 2022

Overjet Inc. % Adam Odeh Director, Regulatory Affairs and Ouality Assurance 560 Harrison Ave., Unit 403 BOSTON, MA 02118

Re: K220928

Trade/Device Name: Overjet Calculus Assist Regulation Number: 21 CFR 892.2070 Regulation Name: Medical Image Analyzer Regulatory Class: Class II Product Code: MYN Dated: November 15, 2022 Received: November 17, 2022

Dear Adam Odeh:

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

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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 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 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-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,

2022.12.16
14:56:46
-05'00'
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) K220928

Device Name Overjet Calculus Assist

Indications for Use (Describe)

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

(K220928)

This summary of 510(k) information is being submitted in accordance with the requirements of 21CFR Part 807.92

1. Date Prepared

November 15, 2022

2. Applicant

Overjet, Inc. 560 Harrison Ave Unit 403 Boston, MA 02118 Contact Person: Adam Odeh Email: adam.odeh@overjet.ai

3. Trade Name Overjet Calculus Assist

1. Common Name Medical Image Analyzer

5. Classification 21 CFR 892.2070, Product code MYN, Class 2, Radiology

6. Device Description

7. Indications for Use

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takes into account other relevant information from the image or patient history. The system is to be used by professionally trained and licensed dentists.

8. Predicate Device

Device: Logicon Caries Detector Manufacturer - Carestream Dental PMA:P980025 (down-classified to class 2 under 85 FR 3548, Jan. 22, 2020)

9. Substantial Equivalence

Device	CarestreamLogiconCaries Detector	Overjet Calculus Assist(proposed)
510k	P980025	K220928
Regulation No /Description	CFR 892.2070Medical image analyzer	CFR 892.2070Medical image analyzer
Intended Use	The Logicon Caries Detector is asoftware device that is an aid in thediagnosis of caries that havepenetrated into the dentin, on un-restored proximal surfaces ofsecondary dentition throughthe statistical analysis of digitalintraoral radiographic imagery. Thedevice providesadditional information for the clinicianto use in his/her diagnosis of a toothsurface suspected of being carious. Itis designed to work in conjunctionwith an existing Carestream dentalRVG digital X-ray radiographicsystem with dental imaging software(dis) for Windows XP or higher.	Overjet's Calculus Assist (OCalA)software automatically detectsinterproximal calculus on bitewing andperiapical radiographs. It is intended to aiddentists in the detection of calculus. Itshould not be used in lieu of full patientevaluation or solely relied upon to make orconfirm a diagnosis. The system is to beused by professionally trained and licenseddentists.
Type of CAD	CADe	CADe
End User	Dentist	Dentist
PatientPopulation	Patients requiring dental services, allsexes, noage restriction	Patients requiring dental services, all sexes,18 years of age or older.
Platform	Windows PC	Web - Edge, Chrome, Firefox
OS	Microsoft Window 7, 8, 10	Any
User Interface	Mouse, Keyboard	Mouse, Keyboard, Trackpad

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Image InputSources	Images can be scanned, loaded fromconnected Carestream image solutions	Images imported from the radiographicdevice, or from the practice managementsystem
Image format		JPEG, PNG, JFIF, JIF, TIFF, EOP, BMP,DICOM
ProcessingArchitecture	The software provides graphicalrepresentation of the density change ina tooth, by looking for apattern of density dips starting at thetooth surface, penetrating the enameland going into the dentin. Enamel isrepresented by 10 green lines anddentin by 5 blue lines. If a patternsuggestive of caries exists, the dips arehighlighted with red dots to warn thedentist.	Three layers:1 - The Network layer works with thepractice PACS or EMR to transmit theimage and meta-data to Overjet.2 - The decision layer processes the imageto ensure it is the correct data type, andthen annotates it via the algorithm3 - The presentation layer displays theannotated image in a non-diagnosticviewer. The dentist can filter, display, hide,create and edit the annotations presented.
Data Source	Bitewing radiographs acquired fromCarestreamdental RVG digital X-ray radiographicsystem	Bitewing and periapical radiographs of atleast 500 x 500 pixels.
Output	• Outline of suspected region• Tooth Density• Lesion (caries) probability	Calculus detection on radiograph resultingin bounding box outline of suspectedcalculus
PerformanceTesting	Increase in dentist's sensitivity ofapproximately 20%	Superiority of aided reader versus unaidedreader performance
Level ofConcern	Moderate	Moderate

Overjet Calculus Assist is determined to be substantially equivalent to the Carestream Logicon Caries Detector cleared as P980025. Both systems are software intended to support dental professionals in their diagnosis and treatment planning for their dental patients.

Both software systems automatically annotate suspected areas of interest for the dentist to review. The Logicon software displays suspected carious lesions as dips in annotated lines within the radiograph, green lines for the enamel and blue lines for the dentin. The Overjet Calculus Assist presents suspected calculus deposits as bounding boxes outlining the prediction. Both systems allow users to visualize the radiograph with the annotations, add their own annotations, and use the information as part of their diagnostic process.

Other similarities include both systems have no direct contact with the patient, both systems evaluate oral cavity radiographs, both systems utilize standard image types, and both systems connect to practice management systems. Logicon compatibility is limited to Carestream products.

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Some differences between the systems include the location of the software, the user interface, and the availability of additional features. A primary difference is the Carestream Logicon is a local software while Overjet Calculus Assist is a cloud native application. While Logicon and Overjet Calculus Assist have different user interfaces, both are accessed by computer and are intended for dental professionals to review annotations on dental radiographs. Overjet does not feel that the differences raise a concern of substantial equivalence and these differences do not interfere with the ability of the Overjet software to achieve its intended use.

10. Performance Testing

Overjet 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: Jul 3, 2012, as part of the development process of the calculus model. Performance testing included standalone testing and a clinical reader evaluation. All testing demonstrated that Overjet Calculus Assist software met prespecified requirements.

Standalone Testing

Standalone performance of the overjet AI algorithm for 296 bitewing radiographs and 322 periapical radiographs. Sensitivity and specificity were summarized based on surfaces, and 95% Cis were provided based on treating the subject as the basis of a cluster. A total of 6,121 surfaces were available on bitewing radiographs, and 3,595 surfaces were available on periapical radiographs.

Sensitivity

Overall standalone sensitivity was 74.1% (66.2%, 82.0%) for bitewing radiographs, and 72.9% (65.3%, 80.5%) for periapical radiographs.

Specificity

Overall standalone specificity was 99.4% (99.1%, 99.6%) for bitewing radiographs, and 99.6% (99.3%, 99.8%) for periapical radiographs.

Subgroup Analyses

Subgroup analyses were also performed for age, gender, sensor, and clinical site. Results were generally similar across all subgroups, and any observed differences were not associated with increased risks.

AFROC

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AFROC curves were derived using the polygon model scores associated with true positives and false positives for all areas identified on each image, which were used to generate and AFROC curve and associated AUC. Results are shown in the following table.

Image Type	AUC	95% CI1
Bitewing	0.859	0.823, 0.894
Periapical	0.867	0.828, 0.903
1 Based on m=10000 bootstrap samples.

Clinical Evaluation - Reader Improvement

Overjet evaluated the Overjet Calculus Assist in a multi-reader, fully crossed reader improvement study. 14 US licensed dentists were asked to evaluate 292 bitewing radiographs (85 with calculus and 211 without calculus) and 322 periapical radiographs (89 with calculus and 233 without calculus). Ground truth was established by the consensus labels of three US-licensed dentists, and non-consensus labels were adjudicated by an oral radiologist. Half of the data set contained unannotated images, and the other half contained radiographs that had been processed through Overjet Calculus Assist. The radiographs were presented to the readers in alternating groups.

In Session 1, readers were asked to draw a box around suspected calculus, and to review predictions from the Overjet Calculus Assist model. Each reader was asked to provide a rating of 1-4 for their confidence in the label (1 - low confidence, 4 - high confidence). A 30-day washout period was utilized to limit recollection bias. Following the washout, the readers were presented the same data set but with alternate grouping. If a reader saw a radiograph in the unpredicted state in session 1, they were presented with the Overjet Calculus Assist predictions in session 2, and vice versa.

Results were compared against a consensus ground truth, and the sensitivity, specificity, and alternative free response receiver operating characteristic (AFROC) was evaluated to characterize the performance of the readers with and without viewing the model annotations.

Unassisted vs. Assisted Sensitivity

For bitewing radiographs, overall reader sensitivity improved from 74.9% (68.3%, 80.2%) to 84.0% (78.8%, 88.2%) unassisted vs assisted. For periapical radiographs, overall reader sensitivity improved from 74.7% (69.9%. 79.0%) to 84.4% (78.8%, 89.2%) unassisted vs assisted.

Unassisted vs. Assisted Specificity

For bitewing radiographs, overall reader specificity decreased slightly from 98.8% (98.7%, 99.0%) to 98.6% (98.4%, 98.9%) unassisted vs assisted. For periapical radiographs, overall

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reader specificity also decreased slightly from 98.1% (97.8%, 98.4%) to 98.0% (97.7%, 98.4%) unassisted vs assisted.

Subgroup Analyses

Subgroup analyses were performed for age, gender, sensor, clinical site, and reader experience. While some differences were observed for various factors and interactions, reader improvement (unassisted vs assisted) was observed in nearly all analyses. No observed differences were associated with increased risks.

AFROC

Readers provided confidence scores for any detected calculus, which were used to calculate AUC for weighted AFROC scores. For the average of all readers, AUC increased from 0.840 (0.800, 0.880) to 0.878 (0.844. 0.913) on bitewing radiographs, and from 0.846 (0.808. 0.884) to 0.900 (0.870, 0.929) on periapical radiographs. Both increases were statistically significant.

ImageType	Modality	Reader AvgAUC ofAFROC	StdError	95% CI	p-valueon AUCDifference	Differencein AUCs	StdErr ofDifference	95% CI onDifference
Bitewing	Assisted	0.878	0.017	0.844, 0.913	0.0055	0.038	0.013	0.012, 0.065
	Unassisted	0.840	0.020	0.800, 0.880
Periapical	Assisted	0.900	0.015	0.870, 0.929	1.47e-05	0.054	0.011	0.032, 0.075
	Unassisted	0.846	0.019	0.808, 0.884

11. General Safety and Effectiveness Concerns

The device labeling contains instructions for use and any necessary cautions and warnings to provide for safe and effective use of this device. Risk management was conducted according to ISO 14971 which ensured, via a risk analysis, the identification and mitigation of potential hazards. Any potential hazards were controlled via software development and design, verification, and validation testing. In addition, general and special controls of the FD&C Act established for Radiological Computer Assisted Detection and Diagnosis Software are in place to further mitigate any safety and or effectiveness risks.

12. Assessment of Non-clinical Performance Data

Overjet Calculus Assist has been verified and validated according to Overiet's design control processes. All supporting documentation has been included in this 510(k) Premarket Notification. Verification activity included unit, integration, and system level testing. Validation testing included performing a pivotal reader study to compare the clinical performance of dentists using CAD detections from the Overjet Calculus Assist software when applied to dental radiographs to

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that of dentists not using Overjet Calculus Assist.

13. Conclusion

Overjet Calculus Assist is substantially equivalent to the predicate device, Carestream Logicon Caries Detector. Differences do not raise any concerns about the safety or efficacy of the device.

Regulation Number and Section

§ 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.