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RCT700 is CBCT and panoramic x-ray imaging system with cephalometric. Which is intended to radiographic examination of the dento-maxillofacial, sinus, TMJ, Airway and ENT structure for diagnostic support for adult and pediatic patients. And a model scan is included as an option. Cephalometric image is also includes wrist to obtain carpus images for growth and maturity assessment for orthodontic treatment. The device is to be operated and used by dentists or other legally qualified heath care professionals

RCT700 is 3D computed tomography for scanning hard tissues like bone and teeth. By rotating the c-arm which is embedded with high voltage generator all-in-one x-ray tube and a detector on each end, CBCT images of dental maxillofacial is attained by recombining data from the same level that are scanned from different angle. Panoramic image scanning function for attaining image of whole teeth, cephalometric scanning option for attaining cephalic image, and Model Scan option for attaining dental model CBCT image are included.

The provided text describes the updated RCT700 device, a dental panoramic/tomography and cephalometric X-ray system, and compares it to two predicate devices (K182614 and K192737). The 510(k) summary focuses on demonstrating substantial equivalence rather than a detailed study proving novel acceptance criteria for a new AI component. The acceptance criteria and the study conducted are primarily focused on maintaining safety and effectiveness comparable to the predicate devices.

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

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly tied to the performance specifications of the predicate devices and general regulatory standards for medical imaging devices. The "reported device performance" refers to the new device (RCT700) meeting these established specifications.

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

The document does not specify a numerical sample size for the test set used for non-clinical or clinical evaluations.

• Test Set Description: "Bench testing" and "Performance (Imaging performance) testing" were conducted. "Clinical images were provided," and "clinical imaging samples are collected from the all detector on propose device at the 2 offices where the predicate device is installed."
• Data Provenance: The general context of the submission implies that the testing was conducted by the manufacturer (RAY Co., Ltd.) in REPUBLIC OF KOREA. The clinical images were collected from "2 offices where the predicate device is installed," suggesting a retrospective collection for evaluation purposes, though this isn't explicitly stated as purely retrospective vs. prospectively gathered for the study.

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

• Number of Experts: "two licensed practitioners/clinicians" for clinical testing. For the review of clinical images, "A licensed practitioner reviewed the sample clinical images."
• Qualifications of Experts: "licensed practitioners/clinicians." No specific experience level (e.g., "radiologist with 10 years of experience") is provided.

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method like 2+1 or 3+1 for resolving discrepancies in expert opinions. It states that "A licensed practitioner reviewed the sample clinical images and found them to be of acceptable quality," suggesting a single-reader assessment for acceptance of image quality. For the "two licensed practitioners/clinicians" for clinical testing, the method of their "approval" is not detailed.

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 involving human readers and AI assistance was not conducted or described.
• AI Effect Size: The document does not describe AI functionality or an AI component that would require such a study or an effect size. The device is an imaging system, and its software (RayScan) is described as "viewing software programs" and supporting "image generate function," which is not characterized as AI for diagnostic 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. The device is a physical X-ray imaging system, not a standalone algorithm. The software (RayScan) is integrated into the device for image generation and viewing.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

For the clinical image quality evaluation, the "ground truth" was established by the subjective assessment of "licensed practitioners" determining if the images were of "acceptable quality for the intended use." This aligns with expert opinion/assessment of image quality rather than objective ground truth like pathology or outcomes data.

8. The Sample Size for the Training Set

Not applicable. This document describes a medical device (an X-ray system) and its associated software (for image acquisition and viewing), not a machine learning or AI algorithm that would typically require a separate training set.

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

Not applicable, as there is no mention of a machine learning or AI algorithm requiring a training set in this submission.

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CBCT, panoramic x-ray imaging system with cephalostat, is an extra oral source x-ray system, which is intended for dental radiographic examination of the teeth, jaw, and oral structures, specifically for panoramic examinations and implantology and for TMJ studies and cephalometry, and it has the capability, using the CBVT technique, to generate dental maxillofacial 3D images. The device uses cone shaped x-ray beam projected on to a flat panel detector, and the examined volume image is reconstructed to be viewed in 3D viewing stations. 2D Image is obtained using the standard narrow beam technique.

RCT700 is 3D computed tomography for scanning hard tissues like bone and teeth. By rotating the c-arm which is embedded with high voltage generator all-in-one x-ray tube and a detector on each end, CBCT images of dental maxillofacial is attained by recombining data from the same level that are scanned from different angle.

Panoramic image scanning function for attaining image of whole teeth, cephalometric scanning option for attaining cephalic image, and Model Scan option for attaining dental model CBCT image are included.

The provided text describes a 510(k) premarket notification for the RCT700 dental x-ray system, which is intended for dental radiographic examinations. The submission focuses on demonstrating substantial equivalence to predicate devices, rather than proving a new device's performance against specific acceptance criteria for a novel clinical function.

Therefore, the document does not present acceptance criteria in the typical sense of a clinical trial for diagnostic performance (e.g., sensitivity, specificity, AUC) or a comparative effectiveness study with AI assistance. Instead, the "acceptance criteria" are implied by demonstrating that the new device's performance, particularly its imaging capabilities, is similar to or not worse than the predicate devices, and that it meets applicable safety and performance standards.

Here's an attempt to structure the information based on your request, acknowledging the limitations of the provided document in terms of specific acceptance criteria and detailed study designs usually associated with AI/diagnostic device performance.

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

The document does not specify quantitative acceptance criteria. Instead, it relies on demonstrating that the performance of the new device is "similar" to the predicate device and that all tests performed achieved "satisfactory" results. The primary performance attributes considered are related to imaging quality and safety as per relevant IEC standards.

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

• Test Set Sample Size: The document states that "Clinical imaging samples are collected from the new detector on propose device at the 2 offices where the predicate device is installed on clinical consideration report for the clinical test images." It also mentions "random patient age, gender, and size." However, a specific number/sample size for the clinical test set is not provided.
• Data Provenance: The general statement implies the data is retrospective (collected from existing offices where the predicate device is installed) and likely from South Korea, as the manufacturer is based there and no other country is mentioned.

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

• Number of Experts: "two licensed practitioners/clinicians"
• Qualifications: "licensed practitioners/clinicians" – specific medical specialty (e.g., dentist, radiologist) or years of experience are not specified. They observed and verified that the dental X-ray system works as intended and that "the clinical diagnosis and structures are acceptable in the region of interests."

4. Adjudication method for the test set

The document states, "As licensed practitioners or clinician diagnoses of the images, it might be proved that the clinical diagnosis and structures are acceptable in the region of interests." This suggests that the two experts independently reviewed the images and (presumably) reached a consensus or agreement that the images were clinically acceptable. However, no formal adjudication method (e.g., 2+1, 3+1) is explicitly described. It is implied that their assessment confirmed clinical acceptability.

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 comparative effectiveness study involving AI assistance for human readers was not performed or mentioned. The device is an x-ray imaging system, not an AI-powered diagnostic aid. This submission is about demonstrating substantial equivalence of a new imaging system incorporating new detectors, not an AI product.

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

No, a standalone algorithm-only performance study was not performed or mentioned. As stated above, this is an imaging device, not an AI algorithm.

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

The "ground truth" for the clinical images appears to be expert clinical diagnosis/assessment by the "two licensed practitioners/clinicians" that "the clinical diagnosis and structures are acceptable in the region of interests" based on visual inspection of the images. No pathology or outcomes data is mentioned as ground truth.

8. The sample size for the training set

The document describes premarket notification for an imaging device, not a machine learning or AI algorithm. Therefore, no training set (in the context of AI/ML) is mentioned or applicable.

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

As there is no training set for an AI/ML algorithm mentioned, this question is not applicable to the provided document.

Ask a specific question about this device

CBCT, panoramic x-ray imaging system with cephalostat, is an extra oral source x-ray system, which is intended for dental radiographic examination of the teeth, jaw, and oral structures, specifically for panoramic examinations and implantology and for TMJ studies and cephalometry, and it has the capability, using the CBVT technique, to generate dental maxillofacial 3D images. The device uses cone shaped x-ray beam projected on to a flat panel detector, and the examined volume image is reconstructed to be viewed in 3D viewing stations. 2D Image is obtained using the standard narrow beam technique.

RCT700 is 3D computed tomography for scanning hard tissues like bone and teeth. By rotating the c-arm which is embedded with high voltage generator all-in-one x-ray tube and a detector on each end, CBCT images of dental maxillofacial is attained by recombining data from the same level that are scanned from different angle. Panoramic image scanning function for attaining image of whole teeth, and cephalometric scanning option for attaining cephalometric image are included.

Here's an analysis of the acceptance criteria and study details for the RCT700 device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly list specific quantitative "acceptance criteria" in a table format with pass/fail thresholds. Instead, it compares the performance of the proposed device (RCT700) to its predicate devices (RAYSCAN α-Expert3D) based on established imaging quality metrics. The ultimate acceptance criterion communicated is "similar or better than the predicate device."

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

• Test Set Sample Size: The document does not specify a quantitative sample size (e.g., number of patients, number of images) for the clinical test set. It mentions "clinical imaging samples are collected from the new detector on propose device at the 2 offices" and "These images were gathered from the new detector installed with RCT700 on any protocols with random patient age, gender, and size." This suggests a qualitative, observational approach rather than a statistically powered clinical trial.
• Data Provenance: The document doesn't explicitly state the country of origin for the clinical data. It mentions the manufacturer is in Korea, but the location of the 2 offices where clinical images were collected is not specified. The data is prospective, as it states "clinical imaging samples are collected from the new detector on propose device."

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

• Number of Experts: "two licensed practitioners/clinicians observed and verified" the images.
• Qualifications: "licensed practitioners/clinicians." Their specific specialties (e.g., dentist, radiologist) and years of experience are not mentioned.

4. Adjudication Method for the Test Set

The document states, "As licensed practitioners or clinician diagnoses of the images, it might be proved that the clinical diagnosis and structures are acceptable in the region of interests." This indicates that the "ground truth" or verification was established through expert review of the images, effectively serving as an expert consensus. There is no mention of a formal adjudication method like 2+1 or 3+1. It appears to be an agreement by the two practitioners.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size of AI Improvement

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The device is an imaging system (CT, panoramic, cephalometric X-ray), not an AI-powered diagnostic aide designed to improve human reader performance with AI assistance. The performance comparison is between the new imaging hardware/system and older predicate imaging hardware/system, not between human readers with and without AI.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, a standalone performance evaluation was done, but it's for the imaging system's technical image quality (hardware and image reconstruction algorithm), not an AI algorithm for autonomous diagnosis. The bench testing and non-clinical considerations evaluated the inherent imaging capabilities of the device/sensor alone across various technical metrics (MTF, DQE, NPS, pixel size, limiting resolution). This is described as proving "the complete system works as intended" and ensuring "diagnostic image quality of the new sensor is equal or better than those of the predicate device."

7. The Type of Ground Truth Used

• For Technical Performance (Bench Testing/Non-Clinical): Established technical benchmarks and measurements against the predicate device using standardized phantom images and physical characterization methods (e.g., image quality phantoms, test patterns). This is a form of objective technical measurement against established metrics.
• For Clinical Efficacy: Expert consensus ("licensed practitioners/clinicians observed and verified that dental X ray system from RCT700" and their diagnoses determined "the clinical diagnosis and structures are acceptable"). There is no mention of pathology or outcomes data as ground truth.

8. The Sample Size for the Training Set

The document does not describe a "training set" in the context of machine learning or AI. The RCT700 is a medical imaging device (hardware and associated reconstruction software), not an AI diagnostic algorithm that requires a separate training dataset.

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

As no training set is described for an AI algorithm, this question is not applicable. The device's performance is established through technical benchmarks and clinical observation/verification.

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