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The Digital Wireless Intraoral X-Ray Sensor (Pluto0002XW) is used in conjunction with dental Radiography in medical units. The product is used for dental X-ray examination and the diagnosis of structural diseases. The product is expected to be used in hospitals and clinics, operated and used by trained professionals under the guidance of doctors. This device is not intended for mammography and conventional photography applications.

This device is suitable for providing dental radiography imaging for both adult and pediatric.

The Pluto0002XW Digital Wireless Intraoral X-Ray Sensor (Hereinafter referred to as Pluto0002XW) contains the digital intra-oral sensor. It features a 20um pixel pitch CMOS sensor with directly deposited CsI:Tl scintillator which ensures optimal resolution.

The major function of the Pluto0002XW is to convert the X-ray to digital image, with the application of high resolution X-ray imaging. Pluto0002XW is the key component of intra-oral DR system, enables to complete the digitalization of the medical X-ray imaging with the intra-oral DR system software.

The iRay intra-oral software (iRayDR) is part of the system, it is used to acquire, enhance, analyze, view and share images from the sensor.

This FDA document, K230998, is a 510(k) summary for a Digital Wireless Intraoral X-Ray Sensor. It focuses on demonstrating substantial equivalence to a predicate device rather than detailing extensive performance studies against specific acceptance criteria for diagnostic efficacy. Therefore, much of the requested information regarding "acceptance criteria" and "study that proves the device meets the acceptance criteria" in terms of clinical performance and AI-assistance aspects (e.g., MRMC studies, standalone AI performance) is not present as this device is a hardware component (X-ray sensor) and not an AI/ML-driven diagnostic software.

However, based on the provided text, we can glean information about the non-clinical studies performed to demonstrate safety and effectiveness for substantial equivalence.

Here's an attempt to answer your questions based solely on the provided text:

Acceptance Criteria and Device Performance (Non-Clinical)

The document does not present a table of acceptance criteria for diagnostic performance (e.g., sensitivity, specificity, accuracy) as it's not a diagnostic AI device or a comparative clinical study for human interpretation. Instead, the "acceptance criteria" can be inferred from the standards the device was tested against for safety, electromagnetic compatibility (EMC), wireless functionality, cybersecurity, and biocompatibility. The "reported device performance" is simply that the device "meet[s] the standard requirements" for these tests.

No explicit quantitative acceptance criteria or reported performance for diagnostic accuracy, sensitivity, or specificity are provided because this device is an imaging sensor, not a diagnostic algorithm that interprets images.

Study Details (Non-Clinical as reported in the 510(k) summary)

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

   Acceptance Criterion (Inferred from Standard Compliance)	Reported Device Performance (Summary Statement)
   Electrical Safety (IEC/ES 60601-1, IEC60601-2-65)	"All test results are meet the standard requirements."
   EMC Testing (IEC 60601-1-2)	"All test results are meet the standard requirements."
   Wireless Functionality & Coexistence (ANSI IEEE C63.27-2017)	"All test results are meet the standard requirements."
   Cybersecurity (Federal Food, Drug, and Cosmetics Act section 524B(b)(2))	"...to provide a reasonable assurance that the subject device with its wireless capabilities are cybersecure."
   Biocompatibility (ISO10993-1)	"the evaluation results and test result assured the safety the same as the predicate device."

2. Sample sizes used for the test set and the data provenance:

   • Not applicable for clinical performance data. The document describes non-clinical engineering and safety tests. Thus, "sample size" would refer to the number of devices or test conditions, which are not specified in detail.
   • Data Provenance: Not specified, but generally, these tests are conducted in certified labs (often in the country of manufacture, China in this case, or by accredited third-party labs). The testing is prospective for the purpose of the 510(k) submission.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not applicable. This relates to clinical diagnostic interpretation by experts, which is not evaluated for this hardware device.

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

   • Not applicable. This relates to clinical diagnostic interpretation.

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:

   • No, an MRMC study was not done. This device is an X-ray sensor, not an AI diagnostic tool. The document does not describe any human reader studies, with or without AI assistance.

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

   • No, a standalone algorithm performance study was not done. This device is hardware.

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

   • Not applicable for clinical diagnostic ground truth. For the non-clinical tests, the "ground truth" is compliance with the specified international and national safety and performance standards (e.g., IEC, ANSI IEEE, ISO).

8. The sample size for the training set:

   • Not applicable. This pertains to AI/ML model training, which is not mentioned for this device.

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

   • Not applicable. This pertains to AI/ML model training, which is not mentioned for this device.

In summary: The provided FDA document (a 510(k) summary) for the Digital Wireless Intraoral X-Ray Sensor focuses on demonstrating substantial equivalence through non-clinical performance and safety testing. It does not contain information related to clinical diagnostic performance, AI performance, or human-reader studies, as the device is a data acquisition component (X-ray sensor), not a diagnostic interpretation tool or AI software.

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Used in conjunction with a pulsed dental X-ray machine, the CS 6200 is intended for medical institutions to perform static digital X-ray imaging facilitating the diagnosis of the teeth, jaw, mouth, and other structural diseases.

The CS 6200 is a USB-driven digital intraoral X-ray sensor based on CMOS technology that is specially designed for acquiring real-time high-quality dental diagnostic images. Image data acquired from the device can be transmitted via a USB 2.0 port to a workstation for display and processing.

The provided text is a 510(k) Summary for the iRay Imaging Technology (Haining) Limited Digital Intraoral X-Ray Sensor (Model CS 6200). It does not contain information about acceptance criteria or a specific study proving the device meets these criteria in the format requested.

The document focuses on demonstrating substantial equivalence to a predicate device (iRay Technology Co., Ltd. PlutoX Digital Intraoral X-Ray Imaging System, K210312) through non-clinical testing. It outlines the technological characteristics of both the proposed and predicate devices and states that non-clinical studies were performed to show equivalence in various performance aspects.

Specifically, it mentions:

• Nonclinical Considerations: "According to the Guidance for the Submission of 510(k)s for Solid State X-ray Imaging Devices, the non-clinical studies have been performed and the results have shown that the CS 6200 are substantially equivalent to the predicate devices on the Market (K210312): Dose to output signal transfer function, Signal to noise ratio, uniformity, Defect, Minimum triggering dose rate, Modulation transfer function (MTF), Spatial resolution, Low contrast resolution and Image Acquisition time."
• Clinical Consideration: "Clinical data is not needed to characterize performance and establish substantial equivalence. The non-clinical test data characterizes all performance aspects of the device based on well-established scientific and engineering principles."

Therefore, I cannot populate the table or answer the specific questions about acceptance criteria, sample sizes, expert involvement, or adjudication methods for clinical studies, as the submission explicitly states that clinical data was not required or provided. The study performed was a non-clinical equivalence study based on technical specifications and performance measurements, not a human reader or image interpretation study.

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Digital intraoral X-ray Imaging System, models i-Sensor H2, are used in conjunction with dental Radiography in medical units. The product is used for dental X-ray examination, the diagnosis of structural diseases of teeth, jaws and mouth. The product is expected to be used in hospitals and clinics, operated and used by trained professionals under the guidance of doctors.

This device is not intended for mammography and conventional photography applications.

This device is suitable for providing dental radiography imaging for both adult and pediatric.

The Digital intraoral X-ray Imaging System, models i-Sensor H1 and i-Sensor H2, are the digital intra-oral sensor. It features a 20um pixel pitch CMOS sensor with directly deposited CsI:Tl scintillator which ensures optimal resolution. An easy to use hispeed direct USB interface enables a simple connection to a PC without need for an additional control box. The optional intra-oral software application makes it easy to acquire, enhance, analyze, view and share images from the sensor. The major function of i-Sensor H1 and i-Sensor H2 are to convert the X-ray to digital image, with the application of high resolution X-ray imaging. This detector is the key component of intra-oral DR system, enables to complete the digitalization of the medical X-ray imaging with the intra-oral DR system software.

The provided text is a 510(k) summary for a Digital Intraoral X-ray Imaging System. It describes non-clinical testing performed to establish substantial equivalence to a predicate device. However, it explicitly states that clinical data is not needed to characterize performance and establish substantial equivalence. Therefore, the document does not contain information about acceptance criteria or a study proving the device meets those criteria based on human-in-the-loop performance or diagnostic accuracy.

The document focuses on non-clinical performance characteristics to demonstrate substantial equivalence through technical specifications and comparison to the predicate device.

Here's a breakdown of the requested information based on the provided text, highlighting where information is not present due to the nature of the submission:

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

The document does not present a formal table of acceptance criteria for diagnostic performance against a ground truth and reported device performance against those criteria. It does list several non-clinical tests and states that "All test results are meet the standard requirements." and "the results have shown that the i-Sensor H1 & i-Sensor H2 are substantially equivalent to the predicate devices". These statements imply that the acceptance criteria for these non-clinical tests were that the device's performance met the relevant standard requirements or was equivalent to the predicate.

Here are the non-clinical tests mentioned and the general statement of their performance:

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

Not applicable. No clinical test set or data from human subjects was used. The submission states, "Clinical data is not needed to characterize performance and establish substantial equivalence." This implies no test set of patient data was used for diagnostic performance evaluation.

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)

Not applicable. No clinical test set was used, and thus no expert ground truth establishment for diagnostic purposes was performed.

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

Not applicable. No clinical test set was used.

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 applicable. The device is a "Digital Intraoral X-ray Imaging System" (hardware), not an AI-assisted diagnostic software. No MRMC study was mentioned or performed.

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

This refers to an "algorithm only" performance. Since the device is a hardware imaging system, this isn't directly applicable in the sense of a diagnostic algorithm's standalone performance. The non-clinical tests (e.g., MTF, spatial resolution) demonstrate the inherent image quality capabilities of the device itself, which could be considered its "standalone" performance characteristics as an imaging system.

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

For the non-clinical tests: The "ground truth" was established by engineering standards, physical measurements, and comparison to the predicate device's established performance specifications. For instance, spatial resolution would be measured against a known standard.

8. The sample size for the training set

Not applicable. The provided document details a hardware device. If there is a "software Ai-Dental" mentioned, it seems to be an image acquisition/enhancement software rather than a diagnostic AI that would require a training set in the typical sense (for supervised learning on disease detection). The document discusses the software's compliance with design specifications and test cases, not its training data.

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

Not applicable, as there's no mention of a diagnostic AI algorithm requiring a training set.

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