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510(k) Data Aggregation
(261 days)
Denti.AI Technology, Inc.
Denti. Al Detect is a Computer-Assisted Detection (CADe) software device intended to be used by dental professionals. comprising dentists and dental specialists, while reading extraoral and intraoral 2D dental radiographs. The device aims to assist in detecting and highlighting uncategorized regions of interest (ROIs) within the teeth area, which include cares and periapical radiolucency, as a second reader. The device is also intended to aid in the measurements of mesial and distal bone levels associated with each tooth.
The device is aimed to be used with images from the patients of 22 years age and older without remaining primary dentition. The device is not intended to replace a complete clinical judgment that considers other relevant information from the image or patient history.
Denti.AI Detect is a prescription use Computer-Assisted Detection (CADe) device aimed to assist dentists and dental specialists in detecting and highlighting regions of interest (ROIs) within the teeth area, which include caries and periapical radiolucency. The device is also intended to aid in the measurements of medial and distal bone levels associated with each tooth.
Denti.AI Detect - Acceptance Criteria and Study Details
1. Acceptance Criteria and Reported Device Performance
For Detection of Caries and Periapical Radiolucency (Stand-alone Study):
| Endpoint | Acceptance Criteria | Reported Device Performance (95% CI) |
|---|---|---|
| Across-category wAFROC AUC | Lower bound of 95% CI > 0.6 | 0.737 (0.713, 0.761) |
For Bone Level Measurement (Stand-alone Study):
| Endpoint | Acceptance Criteria | Reported Device Performance (95% CI) |
|---|---|---|
| Bitewing: CEJ-Bone: Sensitivity | Not explicitly defined, but implied to be high from results | 98.1% (96%, 99.5%) |
| Bitewing: CEJ-Bone: Specificity | Not explicitly defined, but implied to be high from results | 93% (88.7%, 96.7%) |
| Bitewing: CEJ-Bone: Mean Absolute Error (MAE) | Not explicitly defined, but implied to be low from results | 0.513 mm (0.444 mm, 0.593 mm) |
| Bitewing: CEJ-Bone/CEJ-Root Ratio: MAE | Not explicitly defined, but implied to be low from results | 3.8% (3.3%, 4.4%) |
| Periapical: CEJ-Bone: Sensitivity | Not explicitly defined, but implied to be high from results | 98.2% (96.2%, 99.7%) |
| Periapical: CEJ-Bone: Specificity | Acceptance Criterion was met with the exception of the lower CI bound being slightly less than the Acceptance Criterion. | 88.5% (82.8%, 93.4%) |
| Periapical: CEJ-Bone: MAE | Not explicitly defined, but implied to be low from results | 0.572 mm (0.497 mm, 0.653 mm) |
| Periapical: CEJ-Root: Sensitivity | Not explicitly defined, but implied to be high from results | 96.9% (94.2%, 99%) |
| Periapical: CEJ-Root: Specificity | Not explicitly defined, but implied to be high from results | 92.2% (87.9%, 96%) |
| Periapical: CEJ-Root: MAE | Not explicitly defined, but implied to be low from results | 0.735 mm (0.612 mm, 0.868 mm) |
| Periapical: CEJ-Bone/CEJ-Root Ratio: MAE | Not explicitly defined, but implied to be low from results | 4.3% (3.7%, 4.9%) |
| Extraoral: CEJ-Bone-Root: Sensitivity | Not explicitly defined, but implied to be high from results | 91.6% (89.6%, 93.6%) |
| Extraoral: CEJ-Bone-Root: Specificity | Not explicitly defined, but implied to be high from results | 84.3% (70.8%, 95.8%) |
| Extraoral: CEJ-Bone/CEJ-Root Ratio: MAE | Not explicitly defined, but implied to be low from results | 4.7% (4%, 5.5%) |
For Reader Study (Multi-Reader Multi-Case):
| Endpoint | Acceptance Criteria | Reported Device Performance (95% CI) |
|---|---|---|
| Across-category reader performance (wAFROC) | Statistically significant improvement with AI assistance (p |
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(133 days)
Denti.AI Technology Inc.
Denti. Al Auto-Chart is a Medical Image Management and Processing System (MIMPS) device aimed to assist dental professionals (Users), comprising general dental specialists, and dental hygienists, in detecting dental structures and producing dental charting data based on the interpretation of intraoral and extraoral 2D X-Ray images.
Denti.AI Auto-Chart is intended to assist in:
· Detecting natural dental structures: teeth and missing teeth
· Detecting dental structures added through past restment: implants, crowns, pontics, endodonic treatment, fillings
· Choosing treatment options
• Producing dental charts based on image analysis results as well as conditions added manually or produced by integrated CAD devices
The device is aimed to be used with images from the adult population only (≥22 years old and do not have remaining primary teeth). The device is not intended as a replacement for a complete clinical judgment that considers other relevant information from the image or patient history.
Denti.AI Auto-Chart is a Medical Image Management and Processing System (MIMPS) device aimed to assist dental professionals in detecting dental structures and producing dental charting data based on the interpretation of 2D X-Ray images. The device is intended to assist dental professionals in detecting teeth and missing teeth, numbering teeth, and detecting dental structures added through past restorative treatment, including implants, crowns, pontics, endodontic treatment, and fillings.
The Denti.AI Auto-Chart device's performance was evaluated through a standalone study to demonstrate its safety and effectiveness.
1. Acceptance Criteria and Device Performance:
The document explicitly states that "All conducted tests produced results that exceeded predefined acceptance criteria." However, the specific numerical acceptance criteria are not provided in the provided text. Only the "Value" and "95% Confidence Interval" of the device's reported performance are listed.
| Test ID | Test Name | Metric | Value | 95% Confidence Interval |
|---|---|---|---|---|
| 1 | Teeth Detection | Sensitivity (teeth in the field of view) | 97.4% | (96.6%, 98.2%) |
| PPV (Positive Predictive Value) | 99.6% | (99.3%, 99.9%) | ||
| 2 | Teeth Numbering | Overall classification accuracy | 85.9% | (82.6%, 88.9%) |
| 3 | Restorative Findings Identification | Sensitivity averaged across all restoration types | 88.5% | (86.1%, 90.6%) |
| Specificity averaged across all restoration types | 98.3% | (97.8%, 98.7%) | ||
| 4 | Binding Dental Findings to Teeth | Classification accuracy averaged across all findings | 98.3% | (97.5%, 99.0%) |
| 5 | Classifying Filling By Type | Classification accuracy averaged across all types | 98.0% | (96.9%, 98.9%) |
| 6 | Classifying Filling By Surface | Classification accuracy averaged across all surfaces | 88.9% | (87.0%, 90.7%) |
| 7 | Classifying Crowns by Type | Classification accuracy | 94.8% | (92.2%, 97.1%) |
| 8 | Summary Performance | Manual charting reduction rate | 71.2% | (68.2%, 74.1%) |
2. Sample Size for Test Set and Data Provenance:
- Sample Size: The test dataset consisted of 336 images (1 image per patient).
- Data Provenance: The images were "taken from the multiple dental clinics across the US." The patient population was "roughly uniformly distributed by age and gender." The study utilized a retrospective dataset, as the images were "taken from" existing clinics for testing.
3. Number of Experts for Ground Truth and Qualifications:
- Number of Experts: Two (2) experienced dental hygienists and one (1) experienced dentist.
- Qualifications of Experts: The document states "two experienced dental hygienists" and "an experienced dentist." Specific years of experience or board certifications are not specified beyond "experienced."
4. Adjudication Method for the Test Set:
The ground truth was established with the "help of two experienced dental hygienists with an experienced dentist reviewing cases of disagreement." This indicates an adjudication method where an expert (dentist) resolves discrepancies between the initial reviewers (dental hygienists). This can be described as a 2+1 model, where two initial reviewers establish the ground truth, and a third, more senior expert, resolves disagreements.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
No information is provided about a multi-reader multi-case (MRMC) comparative effectiveness study being performed. The study described is a standalone performance evaluation of the device.
6. Standalone (Algorithm Only) Performance:
Yes, a standalone study was conducted. The document states: "Denti.AI completed the standalone study according to the protocol to demonstrate the safety and effectiveness of the Denti.AI Auto-Chart device for its indications for use." The performance metrics listed in the table (Sensitivity, PPV, Accuracy) are characteristic of standalone algorithm performance.
7. Type of Ground Truth Used:
The ground truth used was expert consensus. It was established by "two experienced dental hygienists with an experienced dentist reviewing cases of disagreement."
8. Sample Size for the Training Set:
The sample size for the training set is not mentioned in the provided text. The document only describes the "testing dataset" of 336 images.
9. How Ground Truth for Training Set was Established:
The method for establishing ground truth for the training set is not mentioned in the provided text. Only the method for the testing set's ground truth is detailed.
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