DTX Studio Assist is a Software Development Kit (SDK) designed to integrate with medical device software that displays two-dimensional dental radiographs. It contains a selection of algorithms that processes input data (two-dimensional radiographs) from the hosting application and returns a corresponding output to it.

DTX Studio Assist is intended to support the measurement of alveolar bone levels associated with each tooth. It is also intended to aid in the detection and segmentation of non-pathological structures (i.e., restorations and dental anatomy).

DTX Studio Assist contains a computer-assisted detection (CADe) function that analyzes bitewing and periapical radiographs of permanent teeth in patients aged 15 and older to identify and localize dental findings, including caries, calculus, periapical radiolucency, root canal filling deficiency, discrepancy at the margin of an existing restoration, and bone loss.

DTX Studio Assist is not intended as a replacement for a complete dentist's review nor their clinical judgment which takes into account other relevant information from the image, patient history, and actual in vivo clinical assessment.

Device Description

DTX Studio Assist is a software development kit (SDK) that makes a selection of algorithms (including AI-based algorithms) available through a clean, well-documented API. DTX Studio Assist features are only available to licensed customers. The SDK has no user interface and is intended to be bundled with and used through other software products (hosting applications).

Key functionalities of DTX Studio Assist include:

Focus Area Detection on IOR images: The software features the Focus Area Detection algorithm which analyzes intraoral radiographs for potential dental findings (caries, periapical radiolucency, root canal filling deficiency, discrepancy at the margin of an existing restoration, bone loss and calculus) or image artifacts.

Alveolar Bone Level Measurement: The software enables the measurements of mesial and distal alveolar bone levels associated with each tooth.

Detection of historical treatments: The software enables automated detection and segmentation of dental restorations in IOR images to support dental charting which can be used during patient communication. The following restoration types are supported: amalgam fillings, composite fillings, prosthetic crowns, bridges, implants, implant abutments, root canal fillings and posts.

Anatomy Segmentation: The software segments dental structures by assigning a unique label to each pixel in IOR images, including enamel, dentine, pulp, bone, and artificial structures.

AI/ML Overview

Here's a breakdown of the acceptance criteria and the studies that prove the device meets them, based on the provided FDA 510(k) Clearance Letter.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document does not explicitly state pre-defined acceptance criteria for the new features (Restoration Detection, ABL Measurement, Anatomy Segmentation). Instead, it presents the achieved performance metrics, implying that these values were considered acceptable. For the CADe function, the acceptance criteria are implied by the statistically significant improvement observed in the MRMC study.

Feature / Metric	Acceptance Criteria (Implied)	Reported Device Performance
Focus Area Detection (CADe)	Statistically significant increase in AUC (AFROC analysis) when aided by the algorithm compared to unaided reading.	Achieved a highly significant AUC increase of 8.7% overall (p < 0.001) in the aided arm compared to the unaided arm.
Restoration Detection Algorithm	Acceptable standalone sensitivity, specificity, and Dice score for identifying and segmenting 8 types of dental restorations.	Overall Sensitivity: 88.8%Overall Specificity: 96.6%Mean Dice Score: 86.5% (closely matching inter-expert agreement)
Alveolar Bone Level (ABL) Measurement Algorithm	Acceptable standalone sensitivity and specificity for ABL line segment matching, and Mean Average Error (MAE) for ABL length measurements below a specific threshold (e.g., 1.5mm).	Sensitivity (ABL line segment matching): 93.2%Specificity (ABL line segment matching): 88.6%Average Mean Average Error (MAE) for ABL length: 0.26 mm (well below 1.5 mm threshold)
Anatomy Segmentation Algorithm	Acceptable standalone average Dice score, sensitivity, and specificity for identifying and segmenting key anatomical structures (Enamel, Dentine, Pulp, Jaw bone, artificial).	Overall Average Dice Score: 86.5%Overall Average Sensitivity: 89.0%Overall Average Specificity: 95.2%

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

Feature / Study	Test Set Sample Size	Data Provenance	Retrospective/Prospective
Focus Area Detection (CADe)	216 images (periapical and bitewing)	U.S.-based dental offices (using either sensors or photostimulable phosphor plates)	Retrospective
Restoration Detection Algorithm	1,530 IOR images	Collected from dental practices across the United States and Europe. Images sourced from nine U.S. states and multiple European sites.	Retrospective
Alveolar Bone Level (ABL) Measurement Algorithm	274 IOR images	Collected from 30 dental practices across the United States and Europe. Images sourced from multiple U.S and European sites.	Retrospective
Anatomy Segmentation Algorithm	220 IOR images	Collected from dental practices across the United States and Europe.	Retrospective

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

Feature / Study	Number of Experts	Qualifications
Focus Area Detection (CADe)	Not explicitly stated in this document, but the MRMC study involved 30 readers (dentists) who participated in the diagnostic detection and localization tasks. The ground truth for the AFROC analysis would have been established by a panel of expert radiologists/dentists.	Dentists (for the MRMC study readers). For the ground truth establishment, typically board-certified radiologists/dentists with significant experience would be used, though specific qualifications are not detailed here.
Restoration Detection Algorithm	Three experts	Not explicitly stated, but for establishing ground truth in dental imaging, these would typically be board-certified dentists or oral radiologists with significant experience in diagnosing and identifying dental restorations.
Alveolar Bone Level (ABL) Measurement Algorithm	Three experts	Not explicitly stated, but for establishing ground truth in dental imaging, these would typically be board-certified dentists or oral radiologists with significant experience in measuring alveolar bone levels.
Anatomy Segmentation Algorithm	Not explicitly stated, but the "two-out-of-three consensus method" implies at least three experts were involved across the new features for ground truth.	Not explicitly stated, but for establishing ground truth in dental imaging, these would typically be board-certified dentists or oral radiologists with significant experience in identifying and segmenting dental anatomy.

4. Adjudication Method for the Test Set Ground Truth

Feature / Study	Adjudication Method
Focus Area Detection (CADe)	Not explicitly stated in this document. The MRMC study used AFROC analysis, implying a comprehensive ground truth established prior to the reader study.
Restoration Detection Algorithm	Two-out-of-three consensus method
Alveolar Bone Level (ABL) Measurement Algorithm	Two-out-of-three consensus method
Anatomy Segmentation Algorithm	Implied two-out-of-three consensus method (similar to other new features, although not explicitly stated for this specific algorithm).

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

Yes, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done for the Focus Area Detection (CADe) functionality.

Effect Size: The study demonstrated a highly significant AUC increase (p < 0.001) of 8.7% overall in the aided arm (human readers with AI assistance) compared to the unaided control arm (human readers without AI assistance). This indicates that the AI significantly improved dentists' diagnostic detection and localization performance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, standalone performance studies were done for all functionalities mentioned:

Focus Area Detection (CADe): While the primary demonstration of effectiveness was through an MRMC study, the summary states, "The standalone performance testing results supporting this feature are included in that submission [K221921]," indicating standalone testing was performed.
Restoration Detection Algorithm: "A standalone performance assessment was conducted to evaluate the DTX Studio Assist IOR Restoration Detection algorithm independently, without interaction from dental professionals..."
Alveolar Bone Level (ABL) Measurement Algorithm: "A standalone performance assessment was conducted to evaluate the DTX Studio Assist IOR Alveolar Bone Level (ABL) Measurement algorithm independently, without interaction from dental professionals..."
Anatomy Segmentation Algorithm: "A standalone performance assessment was conducted to evaluate the DTX Studio Assist IOR Anatomy Segmentation algorithm independently, without interaction from dental professionals..."

7. The Type of Ground Truth Used

Feature / Study	Type of Ground Truth
Focus Area Detection (CADe)	Expert consensus (implied by the MRMC study setup and AFROC analysis, where an established truth is required for evaluating reader performance).
Restoration Detection Algorithm	Expert consensus (established by a two-out-of-three consensus method).
Alveolar Bone Level (ABL) Measurement Algorithm	Expert consensus (established by a two-out-of-three consensus method).
Anatomy Segmentation Algorithm	Expert consensus (established by a two-out-of-three consensus method, implied).

8. The Sample Size for the Training Set

The document does not provide the specific sample size for the training set for any of the algorithms. It focuses on the validation (test) sets.

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

The document does not provide details on how the ground truth for the training set was established. It only describes the ground truth establishment for the test sets. It does mention that the algorithms are based on "supervised machine learning algorithms," which inherently means they were trained on data with established ground truth.

Summary

FDA 510(k) Clearance Letter - DTX Studio Assist

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November 17, 2025

Nobel Biocare c/o Medicim NV
℅ Mieke Roelants
RA Manager
Stationsstraat 102
MECHELEN, 2800
BELGIUM

Re: K252086
Trade/Device Name: DTX Studio Assist
Regulation Number: 21 CFR 892.2070
Regulation Name: Medical Image Analyzer
Regulatory Class: Class II
Product Code: MYN, QIH
Dated: October 14, 2025
Received: October 14, 2025

Dear Mieke Roelants:

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.

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K252086 - Mieke Roelants Page 2

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 (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-reporting-combination-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-devices/device-advice-comprehensive-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-devices/medical-device-safety/medical-device-reporting-mdr-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/medical-devices/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-devices/device-advice-comprehensive-regulatory-

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K252086 - Mieke Roelants Page 3

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, Ph.D.
Assistant Director
Diagnostic X-Ray Systems Team
DHT8B: Division of Radiologic Imaging
Devices and Electronic Products
OHT8: Office of Radiological Health
Office of Product Evaluation and Quality
Center for Devices and Radiological Health

Enclosure

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FORM FDA 3881 (8/23) Page 1 of 1 PSC Publishing Services (301) 443-6740 EF

DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration

Indications for Use

Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.

510(k) Number (if known): K252086

Device Name: DTX Studio 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)

CONTINUE ON A SEPARATE PAGE IF NEEDED.

This section applies only to requirements of the Paperwork Reduction Act of 1995.

DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.

The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:

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510(k) Summary

510(k) #: K252086
Prepared on: 2025-10-14

Contact Details - 21 CFR 807.92(a)(1)

Applicant Name: Nobel Biocare c/o Medicim NV
Applicant Address: Stationsstraat 102 Mechelen 2800 Belgium
Applicant Contact Telephone: 003215 44 32 00
Applicant Contact: Mrs. Mieke Roelants
Applicant Contact Email: DEN-MEC-DTX-Regulatory@envistaco.com

Device Name - 21 CFR 807.92(a)(2)

Device Trade Name: DTX Studio Assist
Common Name: Medical image analyzer
Classification Name: Medical Image Analyzer
Regulation Number: 892.2070
Product Code(s): MYN, QIH

Legally Marketed Predicate Devices - 21 CFR 807.92(a)(3)

Predicate #	Predicate Trade Name (Primary Predicate is listed first)	Product Code
K231898	DTX Studio Clinic 4.0	MYN
K242437	Smile Dx	MYN

Device Description Summary - 21 CFR 807.92(a)(4)

Key functionalities of DTX Studio Assist include:

Alveolar Bone Level Measurement: The software enables the measurements of mesial and distal alveolar bone levels associated with each tooth.

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fillings, composite fillings, prosthetic crowns, bridges, implants, implant abutments, root canal fillings and posts.

Anatomy Segmentation: The software segments dental structures by assigning a unique label to each pixel in IOR images, including enamel, dentine, pulp, bone, and artificial structures.

Intended Use/Indications for Use - 21 CFR 807.92(a)(5)

Indications for Use Comparison - 21 CFR 807.92(a)(5)

The subject device, DTX Studio Assist and the first predicate device DTX Studio Clinic 4.0 are predominantly equivalent in terms of Indications for Use regarding the CADe functionality (Focus Area Detection):

Both devices are CADe systems that analyze intraoral radiographs to identify and localize dental findings such as caries, calculus, periapical radiolucency, root canal filling deficiency, discrepancy at the margin of an existing restoration, and bone loss. They are intended for use by dental professionals reviewing bitewing and periapical radiographs of permanent teeth in patients aged 15 years or older.

The difference in Indications for Use between the subject and primary predicate device concerns the additional functionalities of DTX Studio Assist, which include:

Measurement of alveolar bone levels
Detection and segmentation of restorations
Segmentation of dental anatomy

These additional functionalities are present in the secondary predicate device, Smile Dx (K242437), which also supports segmentation and measurement capabilities, including bone level assessment and identification of restorations and dental anatomy.

Conclusion

The subject device, DTX Studio Assist, encompasses the combined Indications for Use of both predicate devices. The differences in wording reflect the inclusion of additional functionalities and do not constitute a new intended use. The overall intended use, supporting dental professionals in the review of intraoral radiographs, remains consistent across the subject and predicate devices.

Technological Comparison - 21 CFR 807.92(a)(6)

DTX Studio Assist and primary predicate device (DTX Studio Clinic 4.0 - K231898) share the following characteristics:

Supervised machine learning algorithms
Detection of six dental findings
Use of intraoral radiographs (bitewing and periapical)
Output via bounding boxes and segmentation masks
Intended use by dental professionals

There are no technical differences between the Focus Area detection algorithm in DTX Studio Clinic 4.0 (primary predicate - K231898) and the algorithm in the current subject device.

DTX Studio Assist and the secondary predicate device (Smile Dx - K242437) share the following characteristics:

Supervised machine learning algorithms

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Detection of caries and periapical radiolucencies
Segmentation of dental anatomy
Use of bitewing and periapical radiographs
Support measurement of mesial and distal alveolar bone levels
Intended use as an adjunct tool for dental professionals

DTX Studio Assist is technologically different from the predicate devices as follows:

DTX Studio Assist is technologically different from the primary predicate device in that it includes only a limited subset of functionalities derived from DTX Studio Clinic; key features such as implant planning and other advanced capabilities are not part of DTX Studio Assist.
DTX Studio Assist is a Software Development Kit (SDK) that outputs structured results for integration into a hosting application, unlike predicate devices such as DTX Studio Clinic and Smile Dx which are stand-alone or web-based applications that display findings directly on radiographs; this architectural difference does not raise safety or effectiveness concerns, as the SDK's output is consistently interpreted and visualized by the host software in a controlled, documented, and validated environment.

Non-Clinical and/or Clinical Tests Summary & Conclusions - 21 CFR 807.92(b)

DTX Studio Assist is designed and manufactured under the Quality System Regulations as outlined in 21 CFR § 820 and ISO 13485:2016 Standards. This device is in conformance with the applicable parts of IEC 62304:2006/Amd 1:2015 standard. Design Control Activities, including risk management following the ISO 14971:2019, verification/validation testing, were conducted and are included in this submission.

The performance of the subject device was verified and validated following the guidance provided in FDA Guidance General Principles of Software Validation. This documentation includes testing which demonstrates that the requirements for the features have been met. Software documentation for Basic Documentation Level of Concern description of respective V&V activities, per the FDA guidance document, "Content of Premarket Submissions for Device Software Functions" issued on June 14, 2023, is also included as part of this submission.

Nonclinical Performance Testing Summary

I. Focus Area Detection (CADe functionality-MYN)
This functionality was originally cleared under K221921. The standalone performance testing results supporting this feature are included in that submission.

II. New software feature performance testing
Three new stand-alone validation studies were conducted to support the determination of substantial equivalence for the newly developed software features: the restoration detection and the Alveolar Bone level measurement algorithm.

1. Restoration Detection Algorithm
A standalone performance assessment was conducted to evaluate the DTX Studio Assist IOR Restoration Detection algorithm independently, without interaction from dental professionals, in identifying and segmenting eight types of dental restorations in intraoral radiographs (IORs). The study included 1,530 IOR images collected from dental practices across the United States and Europe. The dataset was composed of 41% bitewing and 59% periapical images, with 91% captured using digital sensors and 9% using photostimulable phosphor (PSP) plates. The patient population was 38% male and 62% female, with a broad age distribution. Images were sourced from nine U.S. states and multiple European sites to ensure geographic and demographic diversity. The study used a two-out-of-three consensus method to establish a reference standard. The algorithm achieved an overall sensitivity of 88.8% and specificity of 96.6%, with segmentation accuracy confirmed by a mean Dice score of 86.5%, closely matching inter-expert agreement.

2. Alveolar Bone Level (ABL) Measurement Algorithm
A standalone performance assessment was conducted to evaluate the DTX Studio Assist IOR Alveolar Bone Level (ABL) Measurement algorithm independently, without interaction from dental professionals, in identifying anatomical landmarks and calculating mesial and distal ABL measurements in intraoral radiographs (IORs). The study included 274 IOR images collected from 30 dental practices across the United States and Europe. The dataset consisted of 45% bitewing and 55% periapical images, with 86% captured using digital sensors and 14% using photostimulable phosphor (PSP) plates. The patient population was 39% male and 60% female, with a broad age distribution. Images were sourced from multiple U.S and European sites to ensure geographic and demographic diversity. A two-out-of-three consensus method was used to establish a reference standard. The algorithm achieved a sensitivity of 93.2% and specificity of 88.6% for ABL line segment matching. In addition, with an average mean average error of 0.26 mm, the ABL length measurements showed an MAE well below the 1.5 mm threshold, confirming the length is correctly measured by the algorithm.

3. Anatomy Segmentation Algorithm
A standalone performance assessment was conducted to evaluate the DTX Studio Assist IOR Anatomy Segmentation algorithm independently, without interaction from dental professionals, in identifying and segmenting key anatomical structures in intraoral radiograph images (Enamel, Dentine, Pulp, Jaw bone, artificial). The study included 220 IOR images collected from dental practices

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across the United States and Europe. The dataset was composed of 51% bitewing and 49% periapical images. This study report demonstrated that the IOR Anatomy Segmentation Algorithm performs with high accuracy and reliability across a diverse set of 220 intraoral radiographs. The algorithm demonstrated strong performance, achieving an overall average Dice score of 86.5%, an overall average sensitivity of 89.0%, and an overall average specificity of 95.2%. The results are deemed reliable as attested by their tight confidence intervals. The results demonstrate that the algorithm is well suited for integration into dental workflows as a tool to support dental professionals in patient communication.

Clinical performance data were submitted in support of the Focus Area Detection functionality, which was previously cleared under 510(k) K221921. A retrospective, fully crossed, multi-reader, multi-case (MRMC) study was conducted to evaluate the clinical performance of the IOR Focus Area Detection algorithm.

The study demonstrated that the aid of the algorithm significantly improved dentists' diagnostic detection and localization performance for dental findings in intraoral radiographs (IORs). The primary endpoint was a statistically significant increase in the Area Under the Curve (AUC) in the aided arm compared to the unaided control arm, based on AFROC (Alternative Free Response Receiver Operating Characteristic) analysis.

The dataset included 216 periapical and bitewing IOR images acquired in U.S.-based dental offices using either sensors or photostimulable phosphor plates.

Thirty readers participated in the study. Half began with unaided reading, while the other half used the AI-assisted system. After a 4-week washout period, the groups switched modalities. The analysis showed a highly significant AUC increase (p < 0.001) of 8.7% overall in the aided arm.

For detailed results and analysis, please refer to the original 510(k) submission K221921.

Based on the comparison of the intended use, the features and workflows, the technical characteristics, and based on the software verification/validation activities described in this submission, DTX Studio Assist is found to be substantially equivalent to the identified Predicate Devices.

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.