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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.

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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, 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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This system 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.

RIOSensor(Model RIS500) is intended to acquire real-time, clinical digital intraoral X-ray images using a solid-state imaging sensor. This system consists of the CMOS sensor and software for image display. This system senses the onset of the X-ray exposure and automatically acquires and save the image data to a PC (software).

Here's a breakdown of the acceptance criteria and study information for the RIO Sensor (RIS 500) based on the provided document:

This device is an intraoral imaging unit (dental X-ray sensor), and the focus of the performance evaluation is on its imaging characteristics, rather than diagnostic AI performance. Therefore, many typical AI-related criteria like MRMC studies, effect size of AI, and ground truth for disease detection are not applicable here. The evaluation centers on the physical and technical performance of the X-ray sensor itself.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The predicate devices have slightly different sizing conventions (e.g., "Size 1.5", "Size 2.0") which makes a direct pixel matrix comparison challenging without knowing the exact dimensions corresponding to "Size 1" and "Size 2" for RIS500 relative to the predicates. However, the theoretical resolution, MTF, and DQE are directly comparable and are the primary performance metrics. The RIS500 meets or exceeds the key imaging performance criteria of the predicate devices.

2. Sample Size for the Test Set and Data Provenance

The document primarily describes bench testing and non-clinical testing for evaluating the technical performance of the imaging device. It does not mention a "test set" in the context of clinical images for diagnostic evaluation, as there is no AI algorithm being evaluated for diagnostic efficacy.

• Bench Testing: Used to assess whether parameters related to imaging properties and patient dosage satisfy designated tolerances. The sample size for this is not explicitly stated but implies testing a sufficient number of devices or iterations to demonstrate compliance with standards.
• Non-Clinical Testing: Included MTF and DQE of the detector. No specific "sample size" of images is mentioned beyond the testing methodology itself.
• Clinical Considerations: "For clinical testing, two licensed practitioners/clinicians observed and verified that Intraoral Imaging Unit from RIOSensor (Model name: RIS500)." This suggests a qualitative observation by clinicians, not a quantitative study with a defined image test set.
• Data Provenance: Not applicable in the traditional sense for an AI model. The tests are on the device's hardware performance.

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

• Ground Truth for Technical Performance: The "ground truth" for the technical performance metrics (MTF, DQE, theoretical resolution) is established by the performance testing standards themselves (e.g., IEC 61223-3-4). These are objective measurements against established physical standards.
• Clinical Observation: Two "licensed practitioners/clinicians" observed and verified the device. Their specific qualifications (e.g., years of experience, specialization) are not detailed beyond being "licensed practitioners/clinicians." This was for general verification, not for establishing diagnostic ground truth on a specific test set of cases.

4. Adjudication Method for the Test Set

Not applicable. There was no diagnostic test set requiring adjudication from multiple experts. The "clinical considerations" involved observation and verification by two practitioners, but not an adjudication process as understood in AI studies for ground truth establishment.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, Effect Size

No, an MRMC comparative effectiveness study was not done. The device is a direct X-ray sensor, not an AI-assisted diagnostic tool.

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

Not applicable. This device is the X-ray sensor itself; it's not a standalone AI algorithm. Its performance is its ability to acquire and convert X-ray photons into electronic impulses for display.

7. The Type of Ground Truth Used

For the technical performance of the device (MTF, DQE, resolution), the ground truth is established by objective physical measurements against industry standards (e.g., IEC 61223-3-4). This is a form of scientific/engineering ground truth.

8. The Sample Size for the Training Set

Not applicable. This is an X-ray sensor, not an AI model that requires a training set.

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

Not applicable, as there is no AI model or training set.

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