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PaX-i3D Green Premium (Model: PCT-90LH) is a computed tomography x-ray system intended to produce panoramic, cephalometric or cross-sectional images of the oral anatomy by computer reconstruction of x-ray image data from the same axial plane taken at different angles. It provides diagnostic details of the maxillofacial areas for a dental and ENT treatment in adult and pediatric dentistry. The system also utilizes carpal images for orthodontic treatment. The device is to be operated by physicians, dentists, ENT specialist and x-ray technicians.

PaX-i3D Green Premium (Model: PCT-90LH), a digital radiographic imaging system, acquires and processes multi FOV diagnostic images for a dentist and an ENT specifically designed for dental and ENT radiography, PaX-i3D Green Premium (Model: PCT-90LH) is a complete digital X-ray system equipped with imaging viewers, Xray generator and a dedicated SSXI detector. The digital CBCT system is based on a CMOS digital X-ray detector. The CMOS CT detector is used to capture 3D radiographic images of head, neck and craniofacial anatomy for ENT, oral surgery, implant and orthodontic treatment. PaX-i3D Green Premium (Model: PCT-90LH) can also acquire 2D diagnostic image data in panoramic and cephalometric mode.

The provided text describes the acceptance criteria and performance data for the PaX-i3D Green Premium (Model: PCT-90LH) digital X-ray system, comparing it to a predicate device, PaX-i3D Ortho (TON-95LH).

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with specific numerical targets. Instead, it describes acceptance as "performed similarly or better" than the predicate device and that "all test results were satisfactory." The key performance indicators evaluated are:

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a distinct "test set" in the context of clinical images or specific patient data. The performance evaluations were conducted through non-clinical bench testing:

• Sample Size: Not explicitly stated as a number of images or cases. The tests were performed on the device's components (detector) and the finished X-ray equipment.
• Data Provenance: This was retrospective in the sense that the reference point for performance was the previously cleared predicate device. The tests themselves were conducted in a laboratory setting. The country of origin of the data is implied to be from the manufacturer (VATECH Co., Ltd., based in Korea) as it states "The sponsor tested the subject device in a laboratory and provided a non-clinical performance report."

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

No explicit information is provided regarding the number or qualifications of experts used to establish ground truth for this non-clinical performance evaluation. The "ground truth" for the device's technical performance metrics (MTF, DQE, NNPS, Contrast, Noise, CNR) was established through objective physical measurements using established international standards (e.g., IEC 61223-3-5).

4. Adjudication Method for the Test Set

Not applicable. The performance evaluation was primarily objective, based on physical measurements of device characteristics against established standards and comparison to a predicate device's measured performance. There was no mention of human adjudication for the technical performance metrics.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This submission focuses on the substantial equivalence of an X-ray imaging system, not on an AI-driven image analysis or diagnostic aid that would involve human reader performance improvement.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Study Was Done

Yes, aspects of a "standalone" performance were evaluated, particularly for the iterative reconstruction algorithm. The text states: "The safety and effectiveness of iterative reconstruction algorithm have been demonstrated and confirmed by the previous 510k submission of the reference device, PaX-i3D Smart (PHT-30LFO) (K152106)." This implies an evaluation of the algorithm's output (image quality parameters such as noise reduction) independent of a human reader's diagnostic performance. The document explicitly focuses on the "algorithm only" performance for noise reduction in CT images.

7. The Type of Ground Truth Used

The ground truth used for the technical performance metrics (MTF, DQE, NNPS, Contrast, Noise, CNR) was objective physical measurement based on established international standards (e.g., IEC 61223-3-5). For the iterative reconstruction algorithm, the "ground truth" for its effectiveness was implicitly defined by its ability to produce images with "noise levels less than or equal to images produced by standard filtered back projection reconstruction."

8. The Sample Size for the Training Set

Not applicable. This document describes the performance evaluation of an X-ray imaging system, not a machine learning or AI model that would require a dedicated training set. The "iterative reconstruction algorithm" mentioned is a signal processing algorithm, not a learning-based AI algorithm in the typical sense that would necessitate a "training set."

9. How the Ground Truth for the Training Set Was Established

Not applicable, as there was no training set in the context of machine learning.

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PHT-35LHS is a computed tomography x-ray system intended to produce panoramic, cephalometric or cross-sectional images of the oral anatomy by computer reconstruction of x-ray image data from the same axial plane taken at different angles. It provides diagnostic details of the maxillofacial areas for a dental treatment in adult and pediatric dentistry. The system also utilizes carpal images for orthodontic treatment. The device is operated and used by physicians, dentists and x-ray technicians.

Green Smart (PHT-35LHS) is an advanced 5 in 1 digital X-ray imaging system that incorporates PANO, CEPH (Optional), CBCT, MODEL Scan and 3D PHOTO (Optional) imaging capabilities into a single system. Green Smart (PHT-35LHS), a digital radiographic imaging system, acquires and processes multi FOV diagnostic images for dentists. Specifically designed for dental radiography, Green Smart (PHT-35LHS) is a complete digital X-ray system equipped with imaging viewers, X-ray generator and a dedicated SSXI detector. The digital CBCT system is based on a CMOS digital X-ray detector. The CMOS CT detector is used to capture 3D radiographic images of head, neck, oral surgery, implant and orthodontic treatment. With Auto Pano function, It also reconstructs the 3D CT data and produces 2D panoramic images without an additional X-ray scan. Green Smart (PHT-35LHS) can also acquire 2D diagnostic image data in conventional panoramic and cephalometric imaging.

The provided text is a 510(k) Summary for a medical device (Green Smart, Model PHT-35LHS) seeking FDA clearance, demonstrating substantial equivalence to a predicate device. This document focuses on proving performance similarity rather than establishing new clinical effectiveness with human readers. Therefore, several of the requested sections (like MRMC studies, number of experts for ground truth, adjudication methods, and training set details for AI) are not applicable to this type of regulatory submission, as a human-in-the-loop AI model is not the subject of this 510(k). The "device" in question is an X-ray imaging system, not an AI algorithm.

Here's a breakdown of the available information based on your request:

Acceptance Criteria and Device Performance (as demonstrated for Substantial Equivalence)

The document implicitly defines acceptance criteria by comparing the performance parameters of the subject device (Green Smart) to its predicate device (PaX-i3D Smart). The goal is to show the new device is "equivalent or better" than the predicate in key imaging performance metrics.

Table of Acceptance Criteria and Reported Device Performance:

Study Details:

1. Sample size used for the test set and the data provenance:

   • The document does not specify a sample size in terms of patient images or subjects for the performance evaluations. Instead, it refers to "Non-Clinical Test results" and reports on the performance parameters of the device's components (detectors) and the overall system.
   • The data provenance is a laboratory setting, as indicated by "The sponsor tested the subject device in a laboratory and provided a non-clinical performance report." The country of origin for the data is not explicitly stated, but the manufacturer (VATECH Co., Ltd.) is based in Korea. This is a part of a regulatory submission to the US FDA. The nature of the studies discussed (device performance parameters) makes them inherently prospective in the sense that the new device was built and then tested against a set of standards and against the predicate.

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

   • Not applicable. This submission focuses on the physical performance metrics of an X-ray imaging device (e.g., DQE, MTF, NPS, contrast, noise), not on diagnostic accuracy established by human readers interpreting images. Therefore, expert involvement for ground truth on image interpretation is not a component of this specific type of testing for substantial equivalence.

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

   • Not applicable. As the performance data pertains to technical specifications and physical image quality metrics rather than human interpretation accuracy, no adjudication method for diagnostic outcomes is described or required.

4. 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 comparative effectiveness study was not conducted as this is a 510(k) submission for a conventional X-ray imaging system, not an AI-based diagnostic tool. The document describes the system and its imaging capabilities, not an AI-assisted interpretation workflow.

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

   • No. This is a hardware device (X-ray system) with associated viewing software. There is no standalone external algorithm being proposed for independent performance evaluation in this submission. The "algorithm" here refers to the iterative reconstruction algorithm within the CT system itself, and its impact is evaluated through standard image quality metrics (Contrast, Noise, CNR, MTF).

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

   • The "ground truth" for this type of technical performance testing is established through physical measurements and phantom studies using established standards and methodologies (e.g., IEC 61223-3-4, IEC 61223-3-5, 21 CFR 1020.33). These standards define how metrics like MTF, DQE, noise, and contrast are objectively measured using specialized test objects and equipment, not clinical data or expert interpretations.

7. The sample size for the training set:

   • Not applicable. This is a hardware device clearance, not an AI model requiring a training set.

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

   • Not applicable. As this is not an AI model, there is no training set or associated ground truth establishment for it.

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