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Green X (Model : PHT-75CHS) is intended to produce panoramic, cephalometric or 3D digital x-ray images. It provides diagnostic details of the dento-maxillofacial, ENT, sinus and TMJ for adult and pediatric patients. The system also utilizes carpal images for orthodontic treatment The device is to be operated by healthcare professionals.

Green X (Model : PHT-75CHS) is an advanced 4-in-1 digital X-ray imaging system that incorporates PANO, CEPH(optional), CBCT and MODEL Scan imaging capabilities into a single system. Green X (Model : PHT-75CHS), a digital radiographic imaging system, acquires and processes multi-FOV diagnostic images for dentists. Designed explicitly for dental radiography. Green X is a complete digital X-ray system equipped with imaging viewers, an 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 the head, neck, oral surgery, implant and orthodontic treatment. Green X (Model : PHT-75CHS) can also acquire 2D diagnostic image data in conventional PANO and CEPH modes.

The provided text describes the Green X (Model: PHT-75CHS) dental X-ray imaging system and its substantial equivalence to a predicate device. However, it does not contain detailed information about a study proving the device meets acceptance criteria for an AI feature with specific performance metrics such as sensitivity, specificity, or AUC calculated on a test set, nor does it describe an MRMC study.

The document discusses improvements and additions to the device, including "Endo mode," "Double Scan function," "Insight PAN 2.0," and the availability of FDK and CS reconstruction algorithms. It mentions some quantitative evaluations for these features, primarily focusing on image quality metrics and stitching accuracy, but not clinical performance metrics typical for AI algorithms (e.g., detection of specific pathologies).

Based on the provided text, here's an attempt to answer the questions, highlighting where information is missing for AI-specific criteria:

Acceptance Criteria and Device Performance (Based on available information):

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

• Test Set Sample Size: Not explicitly stated for any of the described evaluations (Endo mode, Double Scan, Insight PAN 2.0, FDK/CS algorithms, or general image quality). The evaluations seem to be based on a limited number of clinical images/test phantoms rather than large-scale patient datasets.
• Data Provenance: Not specified. It indicates "clinical images generated in Endo mode" and "3D clinical consideration" for Double Scan, and "clinical evaluation" for Insight PAN 2.0. There is no mention of country of origin or whether the data was retrospective or prospective.

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

• Endo Mode: "A US licensed dentist" evaluated the clinical images. The number of dentists is not specified (could be one or multiple). No specific years of experience or sub-specialty are mentioned beyond "US licensed dentist."
• Double Scan Function: "3D clinical consideration and evaluation" was performed. No specific number or qualifications of experts are mentioned.
• Insight PAN 2.0: "Clinical evaluation was performed." No specific number or qualifications of experts are mentioned.
• Other evaluations: The document refers to "satisfying standard criteria" (IEC 61223-3-5, IEC 61223-3-4) and measurements on phantoms, which typically do not involve expert ground truth in the same way clinical AI performance studies do.

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

• None specified. The evaluations appear to involve a single "US licensed dentist" for Endo mode, and "clinical evaluation" without detailing the adjudication process for other features. This is not a typical AI performance study setup where multiple readers independently review and a consensus process might be employed.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

• No, an MRMC comparative effectiveness study was not done. The document describes performance evaluations of the device's features (e.g., image quality, stitching accuracy, clinical utility) but not a comparative study where human readers' performance with and without AI assistance is measured. Thus, no effect size for human improvement is reported.

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

• This is not explicitly an AI-only device where the "algorithm" performs diagnostic tasks autonomously. The features described (Endo mode, Double Scan, Insight PAN 2.0, reconstruction algorithms) are functionalities of an X-ray imaging system that produce images for human interpretation. The "evaluations" described are largely for image quality metrics and technical performance, not for algorithmic detection or classification of disease. Therefore, a standalone performance study in the context of an AI diagnostic aid is not applicable in the way it might be for, say, an algorithm that flags lesions.

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

• Primary Ground Truth:
  • Phantom Measurements: For quantitative image quality metrics (Noise, Contrast, CNR, MTF, line pair resolution, low contrast resolution) according to IEC standards.
  • Calculated Metrics: For stitching accuracy (SSIM, RMSE).
  • Clinical Evaluation: For confirming "diagnostic quality" (Endo mode) and "clinical efficacy" (Double Scan, Insight PAN 2.0), which relies on expert judgement of the generated images, rather than independent pathology or outcomes data. It functions more as a qualitative assessment of the image's utility.

7. The sample size for the training set:

• Not applicable/Not provided. The document describes a traditional X-ray imaging system with new features, some of which might involve algorithms (e.g., stitching algorithm, reconstruction algorithms) but doesn't explicitly state that these features are "AI" in the sense of requiring a large, labeled training dataset of images to learn to perform a diagnostic task. If these features involve machine learning (e.g., for image enhancement or reconstruction), the training data for those specific algorithms is not detailed.

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

• Not applicable/Not provided for the reasons stated above.

Summary of the Device and Evaluation Context:

The FDA 510(k) clearance process for the Green X (Model: PHT-75CHS) system focuses on demonstrating substantial equivalence to a predicate device. The performance evaluations described are primarily related to the physical and technical performance of the X-ray imaging system and its new functionalities (Endo mode, Double Scan, Insight PAN 2.0, FDK/CS algorithms). These evaluations confirm that the device produces images of sufficient quality, that spatial and contrast resolutions meet standards, and that new features like image stitching are accurate.

Crucially, this is not a submission for an AI/ML-driven diagnostic medical device that would typically involve large, diverse test sets, multiple expert readers, detailed ground truth establishment (like pathology or clinical outcomes), and comparative effectiveness studies to measure how much AI improves human reader performance for a specific diagnostic task (e.g., detecting a particular disease from the image). The "performance data" provided relates to the image acquisition capabilities and processing algorithms of the imaging system itself, which are fundamental to any diagnostic interpretation by a human professional rather than an algorithmic diagnosis or detection.

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uCT 760/780 is a computed tomography x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data taken at different angles and indicated for the whole body (including head, neck, cardiac and vascular).

The uCT 760/780 is a multi-slice X-ray computed tomography scanner which features a continuously rotating tube-detector system and functions according to the fan beam principle. The system provides the filter back-projection (FBP) algorithm to reconstruct images in DICOM format. which can be used by post-processing applications.

The system consists of the Gantry, X-ray System, Data Management System, Patient Table, Console, Power Supply Cabinet, Image Processing Computer, Vital Signal Module and Software. The system software is a program used for patient management, data management, X-ray scan control, image reconstruction, and image archive.

A motorized patient table moves the patient through a circular opening in the Gantry. As the patient passes through the Gantry, a source of x rays rotates around the inside of the circular opening. Detectors on the exit side of the patient record the X rays exiting the section of the patient's body being irradiated as an X-ray "snapshot". Many different "snapshots" (angles) are collected during one complete rotation. The data are sent to a computer to reconstruct all of the individual "snapshots" into a crosssectional image (slice) of the internal organs and tissues for each complete rotation of the source of x rays.

There are two features for denoising and reduce metal artifact, which are KARL iterative denoising reconstruction algorithm and MAC Metal artifact correction algorithm.

This proposed device includes two models: uCT 760, uCT 780.The differences between the two models are as follows:

| Spec.
Model | HV Power | Rotation speed | Minimum
slice thickness | Maximum slices
generated per rotation |
|----------------|----------|-------------------------------------|----------------------------|------------------------------------------|
| uCT 760 | 80kW | Up to 0.35 sec per
360° rotation | 0.625mm | 128 |
| uCT 780 | 100kW | Up to 0.3 sec per
360° rotation | 0.5mm | 160 |

The provided text describes a 510(k) summary for the uCT 760 and uCT 780 Computed Tomography X-ray systems, comparing them to a predicate device (Philips Plus CT scanner, models Brilliance 40 & 64). However, the document does not contain specific acceptance criteria and detailed study results for device performance.

Instead, it focuses on demonstrating substantial equivalence to the predicate device through comparisons of:

• Technological Characteristics: Listed in Table 1, detailing various specifications like detector material, slice thickness, power, mA range, kV settings, and image resolution.
• Application Features: Listed in Table 2, highlighting iterative noise reduction (KARL 3D vs. iDOSE) and metal artifact reduction (MAC vs. MAR).

The document states:

• "No Clinical Study is included in this submission." (Page 13)
• Performance data were provided through "Non-Clinical Testing including dosimetry and image performance tests" (Page 11). These tests were conducted during product development, but specific acceptance criteria and the results are not detailed in this provided summary.
• It mentions "Clinical Evaluation for sample clinical images evaluation," "Artifact Evaluation Report for MAC performance study," and "Iterative Denoising Test Report for KARL performance study" as part of performance verification, but the actual data, methodologies, and acceptance criteria for these evaluations are not presented.

Therefore, based solely on the provided text, I cannot extract a table of acceptance criteria with reported device performance or details about the studies that prove the device meets these criteria as requested in your prompt. The document asserts that these tests were performed and the results supported the claim of substantial equivalence, but it does not provide the specifics.

However, I can extract the information that is present about the studies and evaluations:

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

• Not explicitly provided in the document. The document lists technological specifications for the proposed device and compares them to the predicate, with remarks indicating when the proposed device's performance is "better" (e.g., higher spatial resolution, lower image noise with specific CTDIvol) or "equivalent substantially." These are not framed as "acceptance criteria" but rather as comparative technical specifications that support substantial equivalence.

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

• Not provided. The document mentions "sample clinical images evaluation" but does not specify the number of images or their origin.

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

• Not provided. The document refers to "Clinical Evaluation for sample clinical images evaluation" but gives no details about the experts involved or how ground truth was established.

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

• Not provided.

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 MRMC study was done. The device itself is a CT scanner, not an AI-assisted diagnostic tool for human readers in the context of comparative effectiveness for improved diagnostic accuracy. Its features, KARL iterative denoising and MAC metal artifact correction, are integrated reconstruction algorithms. The document states "No Clinical Study is included in this submission."

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

• Yes, for certain aspects. The "Artifact Evaluation Report for MAC performance study" and "Iterative Denoising Test Report for KARL performance study" indicate standalone evaluations of these algorithms. However, explicit criteria and results are not provided. The main "device" (uCT 760/780) is a standalone system for image acquisition and reconstruction.

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

• Not specified. For the "Clinical Evaluation for sample clinical images," the method for establishing ground truth is not detailed. For the technical performance tests (e.g., image noise, spatial resolution), generally, these are measured against established phantoms and physical standards rather than clinical ground truth alone.

8. The sample size for the training set:

• Not applicable/Not provided. This device is a CT scanner with integrated reconstruction algorithms, not a machine learning model that would typically have a separate "training set" in the conventional sense of supervised learning. The algorithms (KARL, MAC) are developed through engineering and physics principles, likely refined using various datasets, but these are not specified as "training sets" in this context.

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

• Not applicable/Not provided. (See point 8)

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PHT-30LFO is a computed tomography x-ray system intended to produce panoramic, cephalometric or cross-sectional images of the oral anatomy on a real time basis by computer reconstruction of x-ray image data from the same axial plane taken at different angles. It provides diagnostic details of the anatomic structures by acquiring 360 rotational image sequences of oral and maxillofacial area for a precise treatment planning in adult and pediatric dentistry . The device is operated and used by physicians, dentists, and x-ray technicians.

PHT-30LFO, a dental radiographic imaging system, consists of three image acquisition modes; panoramic, cephalometric and cone beam computed tomography. Specifically designed for dental radiography of the teeth or jaws, PHT-30LFO is a complete dental X-ray system equipped with x-fay tube, generator and dedicated SSXI detector for dental panoramic, cephalometric and cone beam computed tomographic radiography. The dental CBCT system is based on CMOS digital X-ray detector. CMOS CT detector is used to capture radiographic diagnostic images of oral anatomy in 3D for dental treatment such as oral surgery or implant. The device can also be operated as the panoramic and cephalometric dental x-ray system based on CMOS X-ray detector.

The provided text is a 510(k) Premarket Notification for the Vatech Co., Ltd. PaX-i3D Smart (PHT-30LFO) dental computed tomography x-ray system. The crucial aspect of this document for your request is demonstrating substantial equivalence to a predicate device (PHT-6500), rather than proving the device meets acceptance criteria through a rigorous clinical study of its diagnostic performance against a ground truth.

The document focuses on comparing the technological characteristics and performance of the new device to a previously cleared predicate device. This is a common approach for 510(k) submissions, where direct clinical superiority or a groundbreaking new diagnostic capability is not typically being claimed or required. Instead, the goal is to show that the new device is as safe and effective as a legally marketed device.

Therefore, the information available does not contain the level of detail you would typically find in a clinical study report that directly proves a device meets specific diagnostic acceptance criteria (e.g., sensitivity, specificity, AUC values against a clinical ground truth). There's no mention of a traditional diagnostic performance study with a test set of patient cases, expert readers, or ROC analysis.

However, I can extract the information that is present, framed in the context of a 510(k) submission for substantial equivalence.

Here's an attempt to answer your questions based on the provided text, acknowledging its limitations for traditional diagnostic performance evaluation:

Acceptance Criteria and Device Performance (in the context of Substantial Equivalence)

The "acceptance criteria" in this 510(k) submission are not expressed as specific diagnostic performance metrics (like sensitivity/specificity for a disease). Instead, the acceptance criteria are met by demonstrating that the new device (PHT-30LFO) is substantially equivalent to the predicate device (PHT-6500) in terms of its indications for use, fundamental technological characteristics, safety, and imaging performance.

The study proves the device meets these "acceptance criteria" by showing:

• Similar Indications for Use: Both devices are intended to produce panoramic, cephalometric, or cross-sectional images of the oral anatomy for precise treatment planning in adult and pediatric dentistry.
• Similar Technological Characteristics: The core components, x-ray parameters, and software functionalities are largely similar or improved without adverse impact.
• Non-Clinical Performance Equivalence/Improvement: Bench testing showed the new detectors performed similarly or better than the predicate's detectors in objective image quality metrics.
• Safety and Performance Standard Compliance: The device meets relevant international and FDA standards for medical electrical equipment and radiation safety.
• Software Verification and Validation: The software meets FDA guidance for medical device software.

Table of Acceptance Criteria (as implied by 510(k) substantial equivalence) and Reported Device Performance:

• Produce panoramic, cephalometric or cross-sectional images of oral anatomy.
• Provide diagnostic details for precise treatment planning in adult and pediatric dentistry. | Met: "PHT-30LFO is a computed tomography x-ray system intended to produce panoramic, cephalometric or cross-sectional images of the oral anatomy on a real time basis by computer reconstruction of x-ray image data... It provides diagnostic details of the anatomic structures by acquiring 360° rotational image sequences of oral and maxillofacial area for a precise treatment planning in adult and pediatric dentistry." (Identical to predicate) |
  | 2. Technological Characteristics Equivalence
• Similar core system components and functionalities. | Met: The proposed device and predicate device "have the same indications for use and demonstrate the similar technical characteristics." Key components (X-ray tube, generator, detectors, PC system, imaging software) are outlined, showing largely comparable specifications (e.g., focal spot size, slice width, filtration, software names). Differences (new detectors, free input voltage, different CBCT reconstruction algorithm) were addressed and deemed not to negatively impact performance. |
  | 3. Non-Clinical Image Quality Performance Equivalence/Non-Inferiority
• Image quality metrics (MTF, DQE, NPS) comparable or better. | Met: "Based on Non-Clinical Test results of the new detector Xmaru1404CF... the new Xmaru1404CF sensor has performed similarly or better than the predicate device in terms of the overall DQE performance... The new sensor also exhibits consistently better performances in terms of MTF and NPS." For the other new detector (Xmaru2301CF-O), "test results demonstrated the same characteristics in terms of MTF, NPS, and DQE performance compared to Xmaru2301 CF detector of the predicate device." Concluded: "the diagnostic image quality of the new sensor is equal or better than those of the predicate device." |
  | 4. Safety and Performance Compliance
• Adherence to relevant electrical, mechanical, environmental safety, and radiation control standards. | Met: "Electrical, mechanical, environmental safety and performance testing according to standard IEC 60601-1 (Ed. 3, 2005), IEC 60601-1-3 (Ed. 2, 2008), IEC 60601-2-63 (Ed. 1, 2012) were performed, and EMC testing were conducted in accordance with standard IEC 60601-1-2." Also meets NEMA PS 3.1-3.18 (DICOM), and EPRC standards (21 CFR 1020.30, 31, 33). "The risks of different voltage requirement of the new device is evaluated and mitigated in electrical safety test." |
  | 5. Software Verification & Validation (V&V)
• Software is safe and performs as intended. | Met: "Software verification and validation tests were conducted and documented as recommended by FDA's Guidance... The software for this device was considered as a 'moderate' level of concern... The predicate device and the proposed device utilize the identical image viewing software." "The functionality and safety of the new iterative reconstruction algorithm for the CT capture mode were assured by the company procedures that conform to accepted practices." |
  | 6. Clinical Images Comparative Assessment
• Clinical images from the new device are comparable to the predicate. | Met: "clinical images generated from the subject device were compared to a group of images taken from the predicate devices to provide further evidence... to show that the complete system works as intended and to establish substantial equivalence based on the modifications to the device." (No specific quantitative metrics or reader study details are provided, implying a qualitative assessment of overall image appearance). |

Here are answers to your specific questions, largely reiterating the limitations for a "diagnostic performance study" as typically understood today:

1. A table of acceptance criteria and the reported device performance:
   (See table above) – The "acceptance criteria" are implied by the requirements for substantial equivalence in a 510(k) submission, focusing on safety, effectiveness, and comparable performance to a predicate device, rather than specific diagnostic accuracy metrics.

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

   • Test Set Sample Size: Not specified for "clinical images." The document mentions "clinical images generated from the subject device were compared to a group of images taken from the predicate devices." No numerical sample size is given for this comparison. For non-clinical tests, it implies the new detectors (Xmaru1404CF, Xmaru2301CF-O) were tested, which would be singular units of the device's components.
   • Data Provenance: Not explicitly stated (e.g., country of origin). The submitter (Vatech Co., Ltd.) is based in the Republic of Korea. It is a retrospective comparison of images rather than a prospective trial with new patients.

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

   • No explicit ground truth establishment process involving multiple experts is described for a diagnostic performance test set. The "clinical images" comparison was made by "qualified individuals employed by the sponsor" for "further evidence" of substantial equivalence. No number of experts or their specific qualifications (e.g., "radiologist with 10 years of experience") are mentioned. This is typical for a 510(k) where diagnostic performance isn't being quantitatively proven against a clinical ground truth.

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

   • No adjudication method is described, as there was no formal reader study or establishment of ground truth by multiple readers for diagnostic purposes. The comparison of clinical images appears to be a qualitative assessment by the sponsor's "qualified individuals."

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, a multi-reader multi-case (MRMC) study was not conducted. This document describes a new imaging device comparing its technical performance (image quality, safety) to a predicate, not an AI-powered diagnostic tool requiring a human-in-the-loop performance study.

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

   • This is a medical imaging device (CT scanner), not a standalone diagnostic algorithm. The "algorithm" mentioned is "CBCT reconstruction algorithm," which is an integral part of the image production, not a separate diagnostic algorithm. The performance of this algorithm is evaluated as part of the overall image quality metrics (MTF, DQE, NPS) and verified through V&V.

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

   • For the "clinical images" comparison, no formal "ground truth" (like histopathology or long-term outcomes) is mentioned. The comparison was likely based on visual assessment of image quality and anatomical representation being "as intended" and "similar to the predicate device." For non-clinical performance, the "ground truth" is the physical properties of test phantoms used to measure MTF, DQE, and NPS.

8. The sample size for the training set:

   • Not applicable/Not specified. This document is about a hardware device with inherent image reconstruction algorithms, not a machine learning model that requires a distinct "training set" of images in the typical sense.

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

   • Not applicable, as there is no mention of a machine learning "training set" in the context of this 510(k) submission for a CT imaging system.

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PaX-Zenith3D is a computed tomography x-ray system intended to take panoramic, cross-sectional images of the oral and craniofacial anatomy and provide diagnostic information for children and adults clinical care in dentistry. The device is operated and used by x-ray technicians and dentists including oral surgeons.

PaX-Zenith3D is a dual X-ray machine which combines cone beam CT and panoramic X-ray modality to offer high definition digital diagnostic images in multi FOV for dental practitioners. Separately embedded panorama and CT sensors capture 2D and 3D images based on digital and CT technology while the most advanced digital imaging process enables the capture S/W program to provide one of the most effective image analysis and diagnosis in real time.

The provided 510(k) summary (K102196) for the PaX-Zenith3D X-ray system does not detail specific acceptance criteria in the form of a numerical table or an explicit study proving numerical performance metrics. Instead, it focuses on demonstrating substantial equivalence to a predicate device (Picasso-Duo). This means the primary "acceptance criteria" were that the new device performs similarly enough to the legally marketed predicate device to be considered safe and effective for the stated indications for use.

Here's an analysis of the provided information based on your requested points:

1. Table of Acceptance Criteria and Reported Device Performance

As noted, specific numerical acceptance criteria and a detailed table of device performance against these criteria are not provided in this 510(k) summary. The document emphasizes substantial equivalence based on similarity rather than on meeting specific quantitative thresholds for clinical performance.

The summary states:

• "The actual spatial resolution data for a digital panorama image for Xmarul 501CF for PaX-Zenith3D and S7199-01 for Picasso-Duo are almost identical after considering pixel binning of S7199-01. 5.0 lp/mm(Xmaru1501CF) and 5.2 lp/mm(S7199-01), respectively,"
• "The similar technical characteristics of both devices are further described in the SSXI Non Clinical Report (Tap 25, 26, and 27) in terms of MTF, DQE, and SNR comparison."
• "Finally, the sample clinical images for both devices have been included in the Clinical Report (Tab 28) to demonstrate the similar quality of images taken from both devices as well."

These statements indicate that the device's performance was compared to the predicate device's performance across metrics like spatial resolution, MTF (Modulation Transfer Function), DQE (Detective Quantum Efficiency), and SNR (Signal-to-Noise Ratio), and found to be "almost identical" or "similar." However, the exact acceptance thresholds for these similarities or the detailed numerical results are not included in this accessible summary.

2. Sample size used for the test set and the data provenance

The document does not specify a sample size for a test set for a clinical performance study. The statement "sample clinical images for both devices have been included in the Clinical Report (Tab 28) to demonstrate the similar quality of images taken from both devices as well" suggests that a collection of images was used for comparison, but the number of cases or images is not quantified.

Data provenance (e.g., country of origin, retrospective/prospective) is also not mentioned for any clinical data used for comparison.

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

This information is not provided in the summary. Since the submission relies on substantial equivalence and "similar quality of images" rather than a clinical trial with established ground truth diagnoses, details on expert panel review for ground truth are absent.

4. Adjudication method for the test set

This information is not provided. Given the nature of the submission (substantial equivalence based on technical and image quality similarity), a formal adjudication method for a clinical test set is not described.

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

An MRMC study was not performed according to the summary. This device is an X-ray imaging system, not an AI-powered diagnostic aid. The "performance" being evaluated is the image acquisition capability of the hardware, not the performance of human readers with or without AI assistance.

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

A standalone performance study for an algorithm was not done. This device is hardware (an X-ray system) that produces images for human interpretation, not an algorithm designed for standalone diagnostic performance.

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

The document does not describe the use of clinical ground truth (like pathology or outcomes data) in the context of demonstrating substantial equivalence. The comparison heavily relies on technical image quality metrics (spatial resolution, MTF, DQE, SNR) and a subjective assessment of "similar quality of images."

8. The sample size for the training set

This information is not applicable/not provided. This device is an X-ray imaging system and does not employ a machine learning algorithm that requires a "training set" in the conventional sense.

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

This information is not applicable/not provided as there is no mention of a machine learning algorithm or a training set.

Summary of the Study that Proves Device Meets Acceptance Criteria:

The study proving the device meets its "acceptance criteria" (understood as demonstrating substantial equivalence) is primarily non-clinical technical testing and a comparison of sample clinical images.

The submission relies on:

• Technical performance data: Comparison of spatial resolution (5.0 lp/mm for PaX-Zenith3D vs. 5.2 lp/mm for Picasso-Duo, considering pixel binning), MTF, DQE, and SNR as detailed in the "SSXI Non Clinical Report" (Tabs 25, 26, and 27). The conclusion states these were "similar."
• Safety and EMC testing: Adherence to various IEC standards (IEC 60601 series) to ensure electrical, mechanical, and environmental safety, as well as electromagnetic compatibility. "All test results were satisfactory."
• DICOM compliance: Meeting NEMA PS 3.1-3.18 standards for Digital Imaging and Communications in Medicine.
• Clinical image comparison: "Sample clinical images for both devices have been included in the Clinical Report (Tab 28) to demonstrate the similar quality of images taken from both devices as well." This suggests a qualitative comparison of images.

The overall approach is to show that the PaX-Zenith3D performs as well as or comparably to the predicate device across key technical specifications and relevant safety standards, thereby establishing substantial equivalence. There is no mention of a formal clinical trial with defined endpoints, statistical analysis, or ground truth establishment for diagnostic accuracy.

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Ask a specific question about this device

PaX-Flex3D is a computed tomography x-ray system intended to produce panoramic, cephalometric or cross-sectional images of the oral anatomy on a real time basis by computer reconstruction of x-ray image data from the same axial plane taken at different angles. It provides diagnostic details of the anatomic structures by acquiring 360° rotational image sequences of oral and maxillofacial area for a precise treatment planning in adult and pediatric dentistry. The device is operated and used by physicians, dentists, and x-ray technicians.

PaX-Flex3D is a diagnostic imaging system which consists of multiple image acquisition modes; panorama, cephalometric, and computed tomography for implantation. Specifically designed for dental radiography of the teeth or jaws, PaX-Flex3D is equipped with extra-oral x-ray detector, panoramic radiography with an extra-oral x-ray tube, cephalometric radiography and computed tomographic radiography. The computed tomography is a system based on CMOS digital X-ray detector. CMOS CT detector is used to capture scanned images in 3D for obtaining diagnostic information of teeth for oral surgery or other treatments. The device can also be operated as the panoramic and cephalometirc dental x-ray system based on CMOS X-ray detector.

The provided 510(k) summary for PaX-Flex3D does not contain specific acceptance criteria or a dedicated study proving performance against such criteria. The submission primarily focuses on demonstrating substantial equivalence to a predicate device (PaX-Reve3D) based on shared intended use and similar technical characteristics.

Here's a breakdown of the information available and what is not present:

1. Table of Acceptance Criteria and Reported Device Performance

No explicit acceptance criteria (e.g., minimum resolution, specific accuracy metrics) or a table comparing device performance against such criteria is provided in the document. The "Performance Specification" section compares the technical specifications of the proposed device to the predicate device.

2. Sample size used for the test set and the data provenance

The document states that "Non-clinical & Clinical considerations according to FDA Guidance 'Guidance for the Submissions of 510(k)'s for Solid State X-ray Imaging Devices' was performed." However, it does not provide details on:

• The specific sample size (number of images, cases, or patients) used for any such testing.
• The data provenance (country of origin, retrospective or prospective nature).

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

This information is not provided in the document.

4. Adjudication method for the test set

This information is not provided in the document.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and its effect size

An MRMC study is not mentioned in the document. The submission focuses on device characteristics and regulatory compliance, not comparative effectiveness with human readers.

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

This device is an X-ray imaging system, not an AI or algorithm-only device. Therefore, a standalone algorithm performance study (in the context of AI) would not be applicable or expected for this type of submission. The device's performance is inherently tied to image acquisition and reconstruction.

7. The type of ground truth used

As specific performance studies are not detailed, the type of ground truth used is not specified. The primary "ground truth" implied in this 510(k) is the established performance of the predicate device.

8. The sample size for the training set

This device is an imaging system, not an AI model requiring a "training set" in the machine learning sense. Therefore, this information is not applicable and not provided.

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

As there is no "training set" for an AI model, this information is not applicable and not provided.

Summary of the Study Proving Device Meets Acceptance Criteria (as described in the document):

The submission primarily relies on design and performance specifications comparison to a legally marketed predicate device (PaX-Reve3D) and adherence to recognized electrical, mechanical, environmental safety, and performance standards, as well as FDA guidance for solid-state X-ray imaging devices.

The key "study" mentioned is the execution of "Non-clinical & Clinical considerations according to FDA Guidance 'Guidance for the Submissions of 510(k)'s for Solid State X-ray Imaging Devices'." However, the details of these considerations, including specific test methodologies, results, or data sets, are not enumerated in this summary. The conclusion states that "All test results were satisfactory," implying that the device met internal or regulatory benchmarks relevant to its classification and intended use, aligning with the predicate device's performance and safety profiles. The mechanism for "proving" acceptance is largely through demonstrating substantial equivalence to an already cleared device and compliance with relevant safety standards.

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PaX-Reve3D Plus is a computed tomography x-ray system intended to produce panoramic, cephalometric or cross-sectional images of the oral and craniofacial anatomy on a real time basis by computer reconstruction of x-ray image data from the same axial plane taken at different angles. It provides diagnostic details of the anatomic structures by acquiring 360° rotational image sequences of oral and craniofacial area for a precise treatment planning in adult and pediatric care . The device is operated and used by physicians, dentists, and x-ray technicians.

PaX-Reve3D Plus is a diagnostic imaging system which consists of multiple image acquisition modes; panorama, cephalometric, and computed tomography for implantation. Specifically designed for dental radiography of the oral and craniofacial anatomy, PaX-Reve3D Plus is equipped with extra-oral x-ray detector, panoramic radiography with an extra-oral x-ray tube, cephalometric radiography and computed tomographic radiography. The computed tomography is a system based on CMOS digital X-ray detector. CMOS CT detector is used to capture scanned images in 3D for obtaining diagnostic information for craniofacial surgery or other treatments. The device can also be operated as the panoramic and cephalometire dental x-ray system based on CMOS X-ray detector.

Here's a breakdown of the acceptance criteria and the study information for the PaX-Reve3D Plus, based on the provided text:

Important Note: The provided text is a 510(k) summary for a dental computed tomography X-ray system. These summaries typically focus on demonstrating substantial equivalence to a predicate device rather than comprehensive de novo clinical studies with detailed acceptance criteria and performance metrics against a clinical ground truth as might be found for novel AI devices or efficacy claims. The "performance" discussed here primarily refers to technical specifications and comparison to the predicate, not clinical accuracy or diagnostic improvement.

Acceptance Criteria and Reported Device Performance

The provided document doesn't explicitly state "acceptance criteria" in the traditional sense of a performance study targeting specific clinical endpoints (e.g., sensitivity, specificity, AUC). Instead, it demonstrates substantial equivalence by comparing technical specifications and intended use of the proposed device (PaX-Reve3D Plus) with a legally marketed predicate device (PaX-Reve3D). The implied "acceptance criteria" is that the new device's performance characteristics are either equivalent or improved compared to the predicate, and that it meets relevant safety and performance standards.

Here's a table summarizing the comparison:

Study Details (Relevant to 510(k) Substantial Equivalence)

The provided document describes a technical comparison and safety testing rather than a formal clinical performance study as might be conducted for an AI-powered diagnostic device.

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

   • Test Set Sample Size: Not applicable in the context of a clinical test set with patient data for diagnostic evaluation. The "test set" for this submission would be the physical device itself and its components, undergoing engineering and safety testing.
   • Data Provenance: Not specified for clinical data, as this is not a study assessing diagnostic accuracy on a patient cohort. The non-clinical and performance data refer to engineering and safety bench testing, likely conducted by the manufacturer (Vatech Co., Ltd.) in Korea Republic (manufacturer's location).

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

   • Not applicable. This type of submission relies on technical specifications and adherence to international safety and performance standards for medical devices, rather than expert-derived ground truth from interpreting medical images.

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

   • Not applicable. There was no clinical ground truth established for a test set of images requiring adjudication.

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. This device is a new imaging system hardware, not an AI software intended to assist human readers. Thus, no MRMC study or AI assistance effect size was reported.

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

   • Not applicable. This is a medical imaging hardware device, not a standalone algorithm. Its "performance" is inherent in its image acquisition capabilities, image quality specifications, and adherence to safety standards.

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

   • Not applicable. The "ground truth" here is compliance with established engineering standards (e.g., IEC 60601 series) and the technical specifications of the predicate device.

7. The sample size for the training set:

   • Not applicable. This device is a hardware system, not a machine learning algorithm that requires a training set.

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

   • Not applicable, as there is no training set for a machine learning algorithm.

In summary, the provided document demonstrates substantial equivalence by:

• Comparing the intended use, technical specifications, and performance characteristics of the PaX-Reve3D Plus to a predicate device (PaX-Reve3D).
• Showing that the proposed device is either equivalent or features minor improvements (e.g., larger imaging volume options, wider tube voltage range, slightly higher panoramic resolution) that do not raise new questions of safety or effectiveness.
• Confirming compliance with a comprehensive set of international electrical, mechanical, environmental safety, and performance standards (IEC 60601 series) and relevant FDA guidance.

The "study" that proves the device meets the (implied) acceptance criteria is the documentation of these technical comparisons and the results of the aforementioned safety and performance tests.

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The Picasso-Duo is a computed tomography x-ray system which is a diagnostic x-ray system intended to produce cross-sectional images(optionally with panoramic) for dental examination and diagnosis of diseases of the teeth, jaw and oral structure by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles.

E-WOO Dental Imaging system Picasso-Duo is a Computed Tomography X-ray System. Computed Tomography (CT) provides valuable 3-D imaging of the dental and maxillofacial structures for diagnosis and treatment planning. Uses of CT imaging include for assessment of impacted teeth, root configurations and mandibular condyle evaluation. This technology allow for 3-D imaging but at lower equipment cost, simpler image acquisition and lower patient radiation dose.

Model Picasso-Duo is diagnostic equipment which consists of panoramic dental x-ray system and computed tomography x-ray system. The panoramic dental x-ray is a system based on digital and computed tomography (CMOS CT Sensor to Capture 3D x-ray Computerized tomogram scanned image)

The provided text is a 510(k) summary for the Picasso-Duo Computed Tomography X-ray System. It describes the device, its intended use, and its substantial equivalence to predicate devices. However, this document does not contain information about specific acceptance criteria, a study proving the device meets those criteria, or performance metrics from such a study.

The summary focuses on regulatory compliance, electrical, mechanical, and environmental safety, and performance testing according to established IEC standards (IEC 60601-1, IEC 60601-1-1, IEC 60601-1-3, IEC 60601-2-7, IEC 60601-2-28, IEC 60601-2-32, IEC 60601-2-44, and IEC 60601-1-2 for EMC). It states that all test results were satisfactory, implying the device meets the safety and performance requirements of these standards.

Without a dedicated study section, specific details regarding acceptance criteria for diagnostic accuracy, sample sizes for test or training sets, ground truth establishment methods, or expert qualifications are not present in this document.

Therefore, I cannot populate the requested table or answer most of the questions as the information is not provided in the input text.

However, based on the information provided, here are the answers I can deduce or explicitly state:

1. Table of acceptance criteria and the reported device performance:

• Acceptance Criteria: Not explicitly stated as specific diagnostic performance metrics (e.g., sensitivity, specificity, accuracy for a particular condition). The implied acceptance criteria are compliance with the referenced IEC safety and performance standards.
• Reported Device Performance: "All test results were satisfactory" for electrical, mechanical, environmental safety, and performance testing according to the specified IEC standards. The device is also stated to provide "high quality digital image" and "A clear Tomography image upto minimum 0.1mm at any directions."

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

• Sample Size: Not specified.
• Data Provenance: 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: Not specified.
• Qualifications of Experts: Not specified.

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

• Not specified.

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 mention of an MRMC study or AI assistance. This device is a Computed Tomography X-ray system, not an AI-powered diagnostic tool in this context.

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

• Not applicable, as this is a medical imaging device, not a standalone algorithm in the context of diagnostic performance evaluation mentioned in the request.

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

• Not specified. (Likely relies on physical measurements and image quality metrics rather than clinical ground truth for a diagnostic accuracy study, as none is described).

8. The sample size for the training set:

• Not specified. (Not applicable for this type of device submission which doesn't describe an AI training process).

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

• Not specified. (Not applicable).

Summary of what the document DOES state regarding performance/testing:

The device's safety and performance are affirmed by compliance with a series of IEC standards:

• IEC 60601-1 (General requirements for basic safety and essential performance)
• IEC 60601-1-1 (Safety requirements for medical electrical systems)
• IEC 60601-1-3 (General requirements for radiation protection in diagnostic X-ray equipment)
• IEC 60601-2-7 (Particular requirements for the safety of high-voltage generators for diagnostic X-ray equipment)
• IEC 60601-2-28 (Particular requirements for the safety of X-ray source assemblies and X-ray tube assemblies)
• IEC 60601-2-32 (Particular requirements for the safety of associated equipment for X-ray equipment)
• IEC 60601-2-44 (Particular requirements for the safety of X-ray equipment for computed tomography)
• IEC 60601-1-2 (Electromagnetic compatibility - Requirements and tests)

The document asserts that "All test results were satisfactory" for these standards tests. It also mentions features like "high quality digital image" and "A clear Tomography image upto minimum 0.1mm at any directions."

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The PaX-REVE3D is a computed tomography x-ray system which is a diagnostic x-ray system intended to produce panoramic, cephalometric and cross-sectional images for dental examination and diagnosis of diseases of the teeth, jaw and oral structure by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. The device is operated and used by physicians, dentists, and x-ray technologists.

The PaX-REVE3D is a computed tomography x-ray system which is a diagnostic x-ray system intended to produce panoramic, cephalometric and cross-sectional images for dental examination and diagnosis of diseases of the teeth, jaw and oral structure by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. The device is operated and used by physicians, dentists, and x-ray technologists.

The provided document pertains to a 510(k) premarket notification for the PaX-Reve3D, a Computed Tomography X-Ray System for dental use. However, it does not contain information about specific acceptance criteria, a study proving device performance against those criteria, sample sizes for test or training sets, ground truth establishment methods, expert qualifications, adjudication methods, or multi-reader multi-case (MRMC) studies.

The document primarily focuses on:

• Device Identification: Name (PaX-Reve3D), common name, classification, and predicate devices.
• Description: General overview, product features including input conditions, capture modes (Panoramic, Cephalometric, CT), and X-ray generator specifications.
• Intended Use/Indications for Use: Diagnostic imaging for dental examination and diagnosis of diseases of teeth, jaw, and oral structure.
• Comparison to Predicate Devices: Stating substantial equivalence based on intended use, form factor, material, performance, and safety.
• Safety, EMC, and Performance Data: General statement that testing according to relevant EN/IEC standards was performed and results were satisfactory. This is a very high-level statement and does not provide detailed acceptance criteria or study results.
• Conclusion: Claim of safety, effectiveness, and substantial equivalence.
• FDA Clearance Letter: Official communication from the FDA clearing the device for market.

Therefore, based solely on the provided text, I cannot complete the requested tables and information regarding acceptance criteria and performance study details.

The document only states that "Electrical, mechanical, environmental safety and performance testing according to standard EN/IEC 60601-1, EN/IEC 60601-1-3, EN/IEC 60601-2-7, EN/IEC 60601-2-28, EN/IEC 60601-2-32 and EN/IEC 60601-1-44 was performed, and EMC testing was conducted in accordance with a standard EN/IEC 60601-1-2. All test results were satisfactory." This is not specific enough to extract acceptance criteria or performance metrics for image quality or clinical efficacy studies.

To answer your request, detailed performance report documents, which are typically referenced in a 510(k) but not always fully included in the publicly available summary, would be needed.

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The NewTom VG Computed Tomography X-ray System is a dedicated X-ray imaging device that acquires a 360-degree rotational X-ray sequence of images for use as diagnostic support in radiology of the dento-maxillo-facial complex and in the field of maxillofacial surgery.

The NewTom VG accomplishes this task by reconstructing a three-dimensional matrix of the examined volume, producing two-dimensional views of this volume and displaying both two-dimensional images and three-dimensional renderings.

The NewTom VG Computed Tomography X-Ray System (NewTom VG) is a dedicated X-ray imaging device that acquires a 360-degree rotational X-ray sequence of images. It then reconstructs a three-dimensional matrix of the examined volume and produces two-dimensional views of such volume, displaying both two- and three-dimensional images. The NewTom VG can measure distances and thickness on two-dimensional images. Such images can be printed or exported on magnetic and optical media.

The NewTom VG hardware, including a scanner unit (comprised of an X-ray source, flat panel detector and a motorized arm) and a control box. facilitates the acquisition of a full X-ray sequence by the device software. The NewTom VG software runs on an x86 architecture based workstation. The NewTom VG reconstructs a three-dimensional model of x-ray images similar to the threedimensional model obtained using the parent NewTom 3G Computed Tomography X-Ray System (NewTom 3G).

This 510(k) summary for the NewTom VG Computed Tomography X-Ray System indicates that the device met all requirements during performance testing. However, it does not explicitly state acceptance criteria or provide a detailed study that proves the device meets specific performance criteria. Instead, it relies on substantial equivalence to a predicate device (NewTom 3G Computed Tomography X-Ray System K041137) and general performance testing for electrical safety, EMC/EMI, and verification/validation.

Therefore, much of the requested information cannot be extracted directly from the provided text.

Here's a breakdown of what can be inferred or cannot be found:

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

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

• Sample Size for Test Set: Not specified.
• Data Provenance: 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: Not specified. This type of information would typically be relevant for studies evaluating diagnostic accuracy, which is not detailed in this 510(k) summary.
• Qualifications of Experts: Not specified.

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

• Adjudication Method: Not specified.

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, an MRMC comparative effectiveness study is not mentioned. This device is an X-ray system, not an AI-assisted diagnostic tool. The document focuses on the system's ability to acquire and reconstruct images, not on its impact on human reader performance in conjunction with AI.
• Effect Size of AI Improvement: Not applicable.

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

• Standalone Performance: Not explicitly detailed in terms of qualitative or quantitative metrics. The document describes the system's ability to reconstruct 3D models and produce 2D views, which implies its standalone image generation capabilities. However, no specific performance study of the algorithm's output quality as a standalone component is described with measurable results.

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

• Type of Ground Truth: Not specified. Given the nature of a 510(k) for an imaging device, ground truth for image quality might be related to objective phantom measurements or comparison to established imaging standards. However, details of such a process are not provided in this summary.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. This document describes an X-ray imaging system, not an AI model that requires a training set.

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

• Ground Truth for Training Set Establishment: Not applicable.

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