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EzSensor Soft [Alternative name : EzSensor Bio] Digital Dental Intra Oral Sensor is intended to collect dental x-ray photons and convert them into electronic impulses that may be stored, viewed and manipulated for diagnostic use by dentists.

EzSensor Soft[Alternative name : EzSensor Bio] Digital Dental Intra Oral Sensor is a device which acquires digital intra oral images. Direct digital systems acquire images with a flexible sensor that is connected to a computer to produce an image almost instantaneously following exposure. The primary advantage of direct sensor systems is the speed with which images are acquired. For patient comfort, the ergonomic design is based on human intraoral anatomy.

The provided document is a 510(k) premarket notification for a dental imaging device, "EzSensor Soft [Alternative name : EzSensor Bio] digital dental image processing system". It compares the new device to a predicate device, "EzSensor". The study primarily focuses on technical performance comparisons and a small-scale image review, rather than a clinical effectiveness study with strict acceptance criteria often seen in AI/CAD device approvals.

Here's an analysis of the acceptance criteria and study findings based on the provided text, while noting the limitations in terms of typical AI/CAD device study requirements:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly state formal acceptance criteria in the manner of quantifiable metrics for diagnostic performance (e.g., sensitivity, specificity, AUC targets). Instead, the performance comparison focuses on technical image quality metrics and a qualitative review by a dentist.

Here's a table based on the provided technical characteristics and the stated comparative performance:

• MTF (6 lp/mm): 0.436 (Full Resolution) / 0.464 (Binning Mode) vs. 0.382 (Predicate). Better performance.
• NPS: Not explicitly quantified, but stated "outperformed EzSensor". Better performance. |
  | Linearity | At least equivalent to the predicate device (EzSensor). | "Very linear and has better linearity than EzSensor in the same dynamic range." Better performance. |
  | Contrast-to-Noise Ratio (CNR) | At least equivalent to the predicate device (EzSensor). | "Superior CNR characteristics compared to EzSensor... direct result of Noise improvement." Better performance. |
  | Resolution/Sharpness | Images should be similar or moderately superior to EzSensor, presenting no difficulty in evaluating anatomical structures. | "Final images generated by both new and predicate sensors are similar or moderately superior to existing EzSensor (Predicate)." "Images of EzSensor Soft [Alternative name : EzSensor Bio] in full resolution mode is generally more sharper and clearer than EzSensor Soft [Alternative name : EzSensor Bio] in binning mode." |
  | Clinical Acceptability | Images present no difficulty in evaluating anatomical structures necessary for correct diagnosis. | "All images present no difficulty in evaluating a range of anatomic structures necessary to provide a correct conclusion..." (based on review by one dentist). |
  | Risk Assessment | All risks and hazardous conditions mitigated to acceptable limits. | "All risks and hazardous conditions identified arising from the design change were successfully mitigated and accepted." Risks identified (electronic shock, device failure, misdiagnosis, tissue damage, serious leakage current, sensor fracture/breakage, cable disconnection) analyzed via FMEA and verified with IEC/EN 60601-1 and drop & vibration tests. |
  | Software Functionality | Easydent and EzDent i have same functionality and performance. | "Easydent and EzDent i image viewing software have the same functionality and performance." Main difference is UI design and new consulting simulation tool for EzDent i. |

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Test Set Sample Size: "Total 30 sets of radiographic images were reviewed."
• Data Provenance: Not explicitly stated, but the submission is from Rayence Co. Ltd. in Korea. The context of a premarket notification for a new device suggests these would likely be newly acquired images for testing, making them prospective to the submission. However, this is an inference; it is not explicitly stated. The country of origin for the images is not specified.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Number of Experts: "a licensed dentist" (singular).
• Qualifications: "a licensed dentist." No information on years of experience or specialization is provided.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Adjudication Method: "none". The document states "Based on the reviewer's conclusion," indicating a single reviewer's assessment without a consensus or adjudication process.

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

• MRMC Study: No. This document describes a medical device (digital dental x-ray sensor), not an AI/CAD system. Therefore, an MRMC study comparing human readers with and without AI assistance is not applicable and was not performed. The study described is a technical comparison of the image sensor and a qualitative review of images by a single dentist.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Standalone Study: Not applicable. This is not an AI/CAD algorithm. The device is an image acquisition sensor. There is no algorithm operating standalone on diagnostic tasks.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• Type of Ground Truth: The "ground truth" for the image review consisted of the qualitative assessment by a single "licensed dentist" regarding the difficulty in evaluating anatomical structures and the sharpness/clearness of images. This is best characterized as expert opinion/review by a single expert. It is not objective ground truth like pathology or outcomes data.

8. The sample size for the training set

• Training Set Sample Size: Not applicable. As this is an image acquisition hardware device (sensor) and not an AI/machine learning algorithm, there is no "training set" in the context of AI. The device's performance is based on its physical and electronic design and confirmed through bench testing.

9. How the ground truth for the training set was established

• Ground Truth for Training Set: Not applicable, as there is no training set for an AI algorithm.

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