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510(k) Data Aggregation
(373 days)
The Root Apex Locator is used to detect the apex of root canal. This device must only be used in hospital environments, clinics or dental offices by qualified dental personnel.
The Root Apex Locator C-ROOT I is a oral equipment used for root canal measurement. The device includes a TFT colour display with touch panel displays parameters such as battery status, connection status of test wire and apex position, etc. Users can also set and modify the sound level, brightness level, DR'S CHOICE via a touch panel, and provide a functional check of the device and cable.
The Root Apex Locator is a dental device used to detect the apex of the root canal. The acceptance criteria and the study proving the device meets these criteria are detailed below, primarily based on the accuracy testing.
1. Table of Acceptance Criteria and Reported Device Performance
Acceptance Criteria | Reported Device Performance |
---|---|
Apex position measurement accuracy within ±0.5mm | The Root Apex Locator C-ROOT I meets the requirement of ±0.5mm accuracy for apex position measurement. |
Compliance with general safety and performance standards (e.g., electrical safety, biocompatibility, EMC) | The device complies with all listed standards, including ISO 10993-5, 10993-10, 10993-11, IEC 60601-1, IEC 60601-1-2, IEC 80601-2-60, IEC 62133-2, and IEC 60601-1-6. |
Software verification and validation | Software documentation for moderate level of concern per FDA guidance. |
Sterilization validation | Cleaning/Disinfection Validation and Sterilization of components as per FDA Guidance. |
2. Sample Size Used for the Test Set and Data Provenance
The document mentions "Internal test method" for accuracy testing. It does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective nature of the data).
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
The document does not specify the number of experts used or their qualifications for establishing ground truth for the test set. It only mentions "Internal test method" for accuracy.
4. Adjudication Method for the Test Set
The document does not describe any specific adjudication method (e.g., 2+1, 3+1) for the accuracy test set.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done
No, an MRMC comparative effectiveness study was not done. The study focuses on the device's standalone accuracy and compliance with various non-clinical standards, and substantiates equivalence to a predicate device. There is no mention of human readers improving with AI assistance, as this is not an AI-powered device.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done
Yes, a standalone performance test was done for the device's accuracy. The "Accuracy Testing" section explicitly states that the "Internal test method demonstrated that the apex position measurement accuracy of the proposed Root Apex Locator C-ROOT I meets the requirement of ±0.5mm." This indicates a measurement of the device's intrinsic performance.
7. The Type of Ground Truth Used
The type of "ground truth" for the accuracy testing is implied to be a precise measurement of the actual apex position, against which the device's measurement is compared. The document does not specify if this was established by expert consensus, pathology, or outcomes data. However, for a root apex locator, the ground truth would typically be a verified measurement of the true working length (e.g., direct measurement on extracted teeth or high-resolution imaging).
8. The Sample Size for the Training Set
The device described is a medical instrument (Root Apex Locator), not an AI/ML algorithm that typically requires a 'training set'. Therefore, the concept of a training set and its sample size is not applicable in this context. The performance evaluation is based on non-clinical testing for accuracy, safety, and compliance with standards.
9. How the Ground Truth for the Training Set was Established
As this is not an AI/ML algorithm, there is no training set and thus no ground truth establishment for a training set. The device's functionality is based on bioelectrical principles for sensing the root apex, not on learned patterns from a training dataset.
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