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510(k) Data Aggregation
(138 days)
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.
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.
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.
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