(76 days)
These intra-oral Dental Cameras are for use in dentistry to be able to show the patient abnormalities and pathology within the mouth. The cameras are utilized exclusively to inform the patient of conditions in the mouth which require treatment. It is not intended that the dental intra-oral camera be utilized in any dental operative procedure.
The camera is provided NON-STERILE and the camera is not built so that it can tolerate any sterilization process.
The camera system does provide a "clean", optically clear covering for the distal end of the handpiece. This provides a "clean" covering for the distal handpiece, and is intended for one time use only.
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The provided text contains two distinct documents. The first two pages (pages 0 and 1) are an FDA 510(k) clearance letter for a device named "True Vision, True Vision II" (K974542), which is an Intra-Oral Dental Camera. The subsequent pages (starting from page 2) appear to describe a generic deep learning-based object detection and classification system, completely unrelated to the dental camera.
Therefore, the input does not contain information about the acceptance criteria and study proving the dental camera meets those criteria. Instead, it includes a generic description of an AI system.
Based on the only relevant information about a device (the True Vision Intra-Oral Dental Camera) in the FDA 510(k) letter, I cannot provide the requested details because the document does not contain: acceptance criteria, study details, data provenance, expert ground truth, MRMC study, standalone performance, training set details, or ground truth establishment relevant to the dental camera.
The second part of the provided text, which describes a deep learning system, is a generic explanation and not related to the FDA cleared device. If you intended this to be a separate AI device, the information is still very high-level and lacks specific details to answer most of your questions.
However, if I were to hypothetically extract information based on the generic deep learning system described on page 2 and onwards, it would be as follows (but please note this is NOT tied to the FDA clearance document or a specific device):
Hypothetical Analysis based on the Generic Deep Learning System Description (Page 2 onwards - NOT the FDA cleared dental camera):
It is important to reiterate that the following information is extracted from a generic description of a deep learning system provided within the input and does not relate to the FDA-cleared "True Vision, True Vision II" Intra-Oral Dental Camera. The provided description is a high-level overview of an AI system's design and conceptual evaluation, not a detailed regulatory study.
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not explicitly stated in the document. | Accuracy: 90% on a dataset not used for training. |
2. Sample sized used for the test set and the data provenance
- Test Set Sample Size: "a dataset of images that were not used to train the system." - Specific size not mentioned.
- Data Provenance: Not mentioned (e.g., country of origin, retrospective/prospective).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Number of Experts: Not mentioned.
- Qualifications of Experts: Not mentioned.
4. Adjudication method for the test set
- Not mentioned.
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
- Not mentioned. This generic description focuses on standalone algorithm performance, not human-AI collaboration.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Yes. The system "achieved an accuracy of 90% on the dataset," implying standalone performance evaluation.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- Object Detection: "labeled with the bounding boxes of the objects in the images."
- Object Classification: "labeled with the class of the object in the image."
- This implies human annotation/labeling for bounding boxes and class labels, likely by trained annotators, but specific "expert consensus" or other types are not detailed.
8. The sample size for the training set
- Training Set Sample Size: "a large dataset of images." - Specific size not mentioned.
9. How the ground truth for the training set was established
- Object Detection: "training on a large dataset of images. The object detection model is trained on a dataset of images that are labeled with the bounding boxes of the objects in the images."
- Object Classification: "trained on a large dataset of images. The object classification model is trained on a dataset of images that are labeled with the class of the object in the image."
- The ground truth was established by labeling bounding boxes and class labels on the training images. The method or individuals responsible for this labeling are not specified beyond "labeled."
§ 872.6640 Dental operative unit and accessories.
(a)
Identification. A dental operative unit and accessories is an AC-powered device that is intended to supply power to and serve as a base for other dental devices, such as a dental handpiece, a dental operating light, an air or water syringe unit, and oral cavity evacuator, a suction operative unit, and other dental devices and accessories. The device may be attached to a dental chair.(b)
Classification. Class I (general controls). Except for dental operative unit, accessories are exempt from premarket notification procedures in subpart E of part 807 of this chapter subject to § 872.9.