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bright CT is a computed tomography x-ray system intended to produce 3D, panoramic, and cephalometric diagnostic images of the maxillofacial areas for treatment planning for adult and pediatic patients. The device is operated and used by physicians, dentists, and x-ray technicians.

Rainbow 3D Image Viewer software functions for acquiring, saving, searching, displaying, diagnosing and sending digital X-ray image data in dental practices and clinics.

bright CT is a cone beam CT X-ray device for generating sectional images of dental images such as tooth, nasal cavity and temporomandibular joint. this is a medical diagnostic equipment designed to generate sectional images by placing X-ray source opposite to the imaging detector unit and rotating it around a patient. 2D images of the region of interest are reconstructed using a mathematical algorithm in 3 dimensional volumetric view and displayed on the computer monitor.

The system is composed of X-ray generator, X-ray detector, X-ray collimator, main frame, rotation unit, PC and Monitor, etc. in compliance with US performance standard and regulatory requirement.

Here's an analysis of the acceptance criteria and the study proving the "bright CT" device meets those criteria, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state formal "acceptance criteria" for the performance characteristics in the way one might typically see for a medical device (e.g., "The MTF for CBCT must be greater than X%"). Instead, the performance claims for the "bright CT" are evaluated against those of a predicate device, the "rainbow CT." The key criteria for substantial equivalence appear to be matching or improving upon the predicate's performance.

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

The document does not specify a "test set" in terms of patient images or specific clinical cases. The performance evaluation appears to be based on non-clinical data and performance testing directly on the device's physical components and imaging capabilities.

• Sample Size for performance tests: Not explicitly stated for each test (e.g., how many measurements for MTF/DQE).

• Data Provenance: The document does not describe the use of patient data for performance evaluation in a testing context. The testing instead involved established international and national standards:

  • IEC 60601-1 (Electrical, mechanical, environmental safety)
  • IEC 60601-1-3 (Radiation protection)
  • IEC 60601-1-6 (Usability)
  • IEC 60601-2-63 (Specific requirements for dental x-ray equipment)
  • IEC 60601-1-2 (EMC)
  • NEMA PS 3.1-3.18 (DICOM)
  • FDA Guidance "Guidance for the submissions of 510(k)'s for Solid State X-ray Imaging Devices"
  • IEC 61223-3-4 & IEC 61223-3-5 (Acceptance tests for diagnostic X-ray imaging equipment)

  These tests typically involve physical phantoms and measurement equipment, not clinical patient data. Therefore, questions regarding country of origin or retrospective/prospective nature of data are not applicable to the described performance testing.

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

Not applicable. As noted above, the primary performance evaluation was based on non-clinical, objective measurements of the device's technical specifications against regulatory standards and comparison to a predicate device, not on expert interpretation of clinical images for ground truth.

4. Adjudication Method for the Test Set

Not applicable, as there was no test set requiring expert adjudication.

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 MRMC comparative effectiveness study was done. The device, "bright CT," is a CT X-ray system for acquiring images, not an AI-powered diagnostic aide. Therefore, the concept of human readers improving with AI assistance is not relevant to this submission.

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

Not applicable. The device is a hardware imaging system. There is no standalone algorithm being evaluated for diagnostic performance.

7. The Type of Ground Truth Used

The "ground truth" for evaluating the technical performance claims (MTF, DQE, pixel resolution, etc.) was established through objective physical measurements using standardized phantoms and measurement techniques as prescribed by the mentioned IEC and NEMA standards. For the safety and efficacy evaluation of the overall device, the ground truth was substantial equivalence to a legally marketed predicate device (rainbow CT), demonstrating that the differences do not raise new questions of safety or effectiveness.

8. The Sample Size for the Training Set

Not applicable. This device is an imaging acquisition system, not a machine learning algorithm that requires a training set of data.

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

Not applicable, as there is no training set mentioned or implied for this device.

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rainbow MCT is a computed tomography x-ray system intended to produce 3D and panoramic diagnostic images of the maxillofacial areas for treatment planning for adult and pediatric patients. The device is operated and used by physicians, dentists, and x-ray technicians.

Rainbow 3D Image Viewer software functions for acquiring, saving, searching, displaying, diagnosing and sending digital X-ray image data in dental practices and clinics.

rainbow MCT is a cone beam CT X-ray device for generating sectional images of dental images such as tooth, nasal cavity and temporomandibular joint. this is a medical diagnostic equipment designed to generate sectional images by placing X-ray source opposite to the imaging detector unit and rotating it around a patient. 2D images of the region of interest are reconstructed using a mathematical algorithm in 3 dimensional volumetric view and displayed on the computer monitor.

The system is composed of X-ray generator, X-ray detector, X-ray collimator, main frame, rotation unit, PC and Monitor, etc. in compliance with US performance standard and regulatory requirement.

This document describes the premarket notification (510(k)) for the Dentium Co., Ltd rainbow MCT (K200270), a computed tomography x-ray system. The information provided focuses on demonstrating substantial equivalence to predicate devices rather than a standalone clinical study of an AI algorithm. Therefore, many of the requested criteria related to AI performance, such as multi-reader multi-case (MRMC) studies, effect sizes, and specific details about training/test set ground truth establishment for AI, are not applicable or not explicitly detailed in this document, as the device itself is an imaging system and not an AI/ML software.

The document primarily discusses the technical and performance characteristics of the imaging device itself, ensuring it meets standards comparable to existing predicate devices.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present a formal "acceptance criteria" table in the way one might see for an AI algorithm's performance metrics (e.g., AUC, sensitivity, specificity targets). Instead, it compares the rainbow MCT's technical specifications and imaging performance metrics against those of predicate devices (K181432, ProVecta 3D Prime with VistaSoft; and K102196, PaX-Zenith3D). The "acceptance" is implied by demonstrating similar or better performance compared to these legally marketed devices.

Summary of differences and claims: The document states, "The MTF, DQE and pixel resolution of the subject device performed similar or better than those of the predicate and reference device. All test results were satisfactory."

2. Sample Size for the Test Set and Data Provenance

The document describes non-clinical testing of the device's physical and technical performance (e.g., electrical, mechanical, environmental safety, EMC, imaging properties per IEC standards). It does not refer to a "test set" in the context of a dataset of patient images used to evaluate an AI algorithm's diagnostic performance. Therefore, sample size and data provenance (country, retrospective/prospective) for a patient image test set are not applicable here.

The tests performed were:

• Electrical, mechanical, environmental safety testing according to IEC 60601-1, IEC 60601-1-3, IEC 60601-2-63.
• EMC testing in accordance with IEC 60601-1-2.
• Non-clinical & Clinical considerations according to FDA Guidance "Guidance for the submissions of 510(k)'s for Solid State X-ray Imaging Devices."
• Acceptance test according to IEC 61223-3-4 and IEC 61223-3-5.

All these refer to technical and performance benchmarks, often using test phantoms or controlled environments, not patient data for diagnostic accuracy assessment.

3. Number of Experts and Qualifications for Ground Truth

Not applicable. As this is a 510(k) for an imaging device, not an AI diagnostic algorithm, there's no mention of experts establishing ground truth from patient images for a diagnostic performance study. The ground truth for the performance characteristics (e.g., MTF, DQE) is inherent to the physical properties of the imaging system and measured using standardized methods and phantoms.

4. Adjudication Method for the Test Set

Not applicable. No diagnostic image test set or human interpretation adjudication is described.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No. A MRMC comparative effectiveness study was not done, as the submission is for the imaging device itself, not an AI-assisted interpretation tool. The document explicitly states: "Clinical Data: Not required for a finding of substantial equivalence."

6. Standalone (Algorithm Only) Performance

Not applicable. This submission is for a medical imaging device, not an AI algorithm.

7. Type of Ground Truth Used

The "ground truth" for the device's performance is based on technical specifications and measurements obtained through standardized testing procedures using phantoms and controlled setups (e.g., MTF, DQE measurements, electrical safety tests). It is not based on expert consensus, pathology, or outcomes data from patient cases in a diagnostic context.

8. Sample Size for the Training Set

Not applicable. The device is a hardware imaging system; it does not involve machine learning or training on a dataset of patient images.

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

Not applicable. See point 8.

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rainbow CT is a computed tomography x-ray system intended to produce 3D, panoramic, and cephalometric diagnostic images of the maxillofacial areas for treatment planning for adult and pediatric patients. The device is operated and used by physicians, dentists, and x-ray technicians.

Rainbow 3D Image Viewer software functions for acquiring, saving, searching, displaying, diagnosing and sending digital X-ray image data in dental practices and clinics.

rainbow CT is a cone beam CT X-ray device for generating sectional images of dental images such as tooth, nasal cavity and temporomandibular joint. this is a medical diagnostic equipment designed to generate sectional images by placing X-ray source opposite to the imaging detector unit and rotating it around a patient. 2D images of the region of interest are reconstructed using a mathematical algorithm in 3 dimensional volumetric view and displayed on the computer monitor.

The system is composed of X-ray generator, X-ray detector, X-ray collimator, main frame, rotation unit, PC and Monitor, etc. in compliance with US performance standard and regulatory requirement.

The provided text is a 510(k) summary for the "rainbow CT" device. It describes the device, its indications for use, and a comparison to predicate and reference devices. However, this document does not contain the specific details required to answer your request about acceptance criteria and the study proving the device meets them.

The document states:

• "Performance testing was conducted for the subject device to access whether or not the parameter required for functionalities related to imaging properties of the dental X-ray device meets the designated acceptance criteria. The MTF, DQE and pixel resolution of the subject device performed similar to those of the predicate device. The pixel resolutions of the subject device in CBCT (2x2 binning) and pano mode are superior to that of the reference device. All test results were satisfactory." (Page 8)
• "Non-clinical & Clinical considerations according to FDA Guidance "Guidance for the submissions of 510(k)'s for Solid State X-ray Imaging Devices" were performed. Acceptance test according to IEC 61223-3-4 and IEC 61223-3-5 was performed. All test results were satisfactory." (Page 8)
• "Clinical Data: Not required for a finding of substantial equivalence." (Page 8)

This means the acceptance criteria and study details (like sample size, number of experts, adjudication methods, ground truth, effect sizes) for clinical performance are not present in this 510(k) summary because clinical data was explicitly stated as "Not required for a finding of substantial equivalence."

The performance testing mentioned (MTF, DQE, pixel resolution) refers to technical imaging performance characteristics of the CT system itself, not clinical diagnostic performance of an AI algorithm on patient images. The acceptance criteria for these technical parameters would likely be engineering specifications, and the "study" would be technical measurements in a lab setting, rather than a clinical trial with human readers and patient data.

Therefore, I cannot populate the table or answer the specific questions about clinical performance, human-in-the-loop studies, or ground truth derivation from the provided text. The document focuses on demonstrating substantial equivalence based on technical characteristics and safety standards, rather than a detailed clinical validation study for an AI component.

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Intra Oral Sensor (model: IOX 1 / IOX 2) is intended to collect dental x-ray photons and convert them into electronic impulses that may be stored, views and manipulated for diagnostic use by dentists.

Intraoral Sensor (model: IOX 1 / IOX 2) by Dentium is a medical device that acquires digital images by detecting subject information through X-rays and converting them into electrical image signals to identify teeth and tissues in the mouth. The product consists of the Intraoral Sensor, USB Memory, Sensor Holder, Silicon Cover and Quick Guide.

The provided text describes information related to a 510(k) submission for an Intra Oral Sensor (model: IOX 1 / IOX 2) by Dentium Co., Ltd. The document primarily focuses on demonstrating substantial equivalence to a predicate device through bench testing and adherence to relevant standards.

Here's an attempt to extract the requested information, noting that some details typically found in a full study report (e.g., specific sample sizes for test sets, detailed ground truth establishment for training) are not fully elaborated in this summary document.

Acceptance Criteria and Device Performance:

The document states that "Performance testing was conducted for the subject device to assess whether or not the parameter required for functionalities related to imaging properties of the dental X-ray device meets the designated acceptance criteria. All test results were satisfactory." And specifically mentions: "The tests include the MTF(Modulation Transfer Function) and DOE(Detective Quantum Efficiency) of detector. MTF of detector shows the resolution more than 30 % at 6 lp/mm and The DQE of detector shows the resolution more than 40 % at 2.5 lp/mm."

Based on the information provided, the following table can be constructed:

Study Details:

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

   • Sample Size (Test Set): The document does not specify the exact number of images or cases used in the performance bench testing. It refers to "bench testing" and "performance (imaging performance) testing" according to standard IEC 61223-3-4.
   • Data Provenance: This was a non-clinical bench study focused on the technical performance of the device itself (sensor characteristics), not on clinical images from patients. Therefore, data provenance in terms of country of origin or retrospective/prospective clinical data is not applicable here.

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

   • Not applicable. Since this was a non-clinical bench test of technical performance (MTF, DQE), there were no human experts involved in establishing "ground truth" for diagnostic purposes. The ground truth was based on physical measurements of the sensor's technical specifications as per established engineering standards (e.g., IEC 61223-3-4 for imaging performance).

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

   • Not applicable. As this was a non-clinical bench test, there was no adjudication process involving human reviewers.

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 done. This submission is for an intraoral sensor, which is a hardware device for capturing X-ray images, not an AI-powered diagnostic software. The study focused on the technical performance equivalence of the sensor to its predicate device. Clinical data and comparative effectiveness studies involving human readers or AI assistance were explicitly stated as "Not required for a finding of substantial equivalence."

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

   • No, this is not applicable as this is a hardware device. This submission is for an intraoral sensor, which is a hardware device that acquires images. There is no standalone algorithm being evaluated for diagnostic performance.

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

   • The ground truth for this study was based on physical and engineering measurements of the sensor's imaging performance characteristics (MTF, DQE) according to established international standards (IEC 61223-3-4). It is a technical ground truth, not a clinical one.

7. The sample size for the training set:

   • Not applicable. This device is a hardware sensor, not an AI algorithm that requires a training set for machine learning.

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

   • Not applicable. As there is no training set for an AI algorithm.

Summary of what the document implies about the study:

The study conducted was a non-clinical bench performance study designed to demonstrate that the new Intra Oral Sensor (IOX 1 / IOX 2) meets technical imaging performance specifications (MTF and DQE) that are substantially equivalent to its predicate device. This type of study focuses on the physical properties and output quality of the imaging hardware itself, rather than evaluating diagnostic accuracy with human readers or AI algorithms on clinical cases. The ground truth was based on objective physical measurements and engineering standards.

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