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The Dental Cone Beam Computed Tomography System is intended to be used by medical institutions for X-ray image diagnosis on oral and maxillofacial areas through X-ray Cone Beam Computed Tomography (CBCT).

The system consists of X-ray tube assemblies, collimators, flat panel detector, control devices (the touch screen, the control panel, the console panel and the exposure switch), auxiliary positioning devices(the lifting seat and the chin rest) and U-shape gantry (the X-ray tube assembly support, the base and the Detector Support), and workstation. The system is an open design that allows patients to sit upright during a procedure. An electric powered seat is built into the scanner for proper patient positioning. The system has 2 configurations: the Configuration 1 has two X-ray tube assemblies(X-ray tube assembly A and X-ray tube assembly B) and two collimators(A and B), the Configuration 2 only has X-ray tube assembly A and collimator A. The proposed device is a CBCT dental system utilizes X-ray to obtain cross-sectional images of patient's oral and maxillofacial areas. The image software can reconstruct data and clearly display the 3D anatomical structure of the oral and maxillofacial areas, and achieve the multi-slice at any level, accurate measurement, contour plots and so on in the workstation software, greatly facilitating the clinical application of stomatology. The Lifting Seat moves up and down via buttons on the system Control Panel allowing positioning and adjustment of patients at different heights; the Touch Screen facilitates the adjustment of projection parameters and the control of actions of the device; the Gating and the shielding door are the safety interlock of the X-ray beam exposure to prevent the dose leakage caused by abnormal operation; the Exposure Switch controls the exposure output of the whole machine in real time during the scanning process. There are four FOV sizes; 20cm x 18cm high resolution is for diagnostic imaging of maxillary region, mandibular region, TMJ region and other craniofacial region, and it is only available for configuration 1; 17cm x 10.5cm high resolution , 8cm x8cm Super Resolution and 4cm x4cm Super Resolution are for diagnostic imaging of maxillary region and mandibular region. Under High Resolution mode, patients will receive lower radiation dose compared to Super Resolution. Dose radiation for each mode is different for pediatric and adult patients.

The provided text does not contain detailed information about the acceptance criteria or a specific study proving the device meets those criteria for AI performance. The document is a 510(k) clearance letter for a Dental Cone Beam Computed Tomography System, which is a medical imaging device, not an AI-powered diagnostic tool.

The "Clinical Performance Data" section mentions that "Sample clinical images obtained from the subject device were provided to demonstrate its imaging performance. An ABR-certified radiologist with over 5 years of experience post-certification independently reviewed the sample scans and evaluated essential image quality. The overall image quality was acceptable for all cases and image types in various scanning modes for both adult and pediatric applications."

However, this review is about the imaging performance of the CBCT system itself, not about the performance of an AI algorithm within the system. There are no mentions of:

• Specific AI acceptance criteria (e.g., sensitivity, specificity, AUC)
• A test set for AI
• Ground truth establishment methods for AI
• MRMC studies or standalone AI performance
• Training set details for AI

Therefore, I cannot fulfill your request for information related to AI acceptance criteria and study data based on the provided text. The document focuses on the substantial equivalence of the CBCT system as an imaging device to a predicate device, covering aspects like electrical safety, EMC, biocompatibility, and general image quality assessment by a human radiologist.

To provide the information you requested, the input document would need to be a 510(k) for an AI/ML-powered device, or indicate that the "Dental Cone Beam Computed Tomography System" includes an AI component whose performance was evaluated for clearance.

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FinScan F350 is CBCT, panoramic and cephalometric x-ray imaging system. The device is intended to radiographic examination of the dento-maxillofacial and TMJ structure for diagnostic support for adult and pediatric patients. Cephalometric image 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 health care professionals.

Dental Cone-beam Computed Tomography (model: FinScan F350) uses cone-beam computed tomography (CBCT) technology through X-ray cone-beam, panoramic radiography, cephalometric radiography to produce images of the dento-maxillofacial and TMJ structure to provides diagnostic details for dental clinics and dental hospitals.

This product mainly consists of column, rotating unit, tube head assembly, CBCT/panoramic detector, cephalometric detector, the workstation and image processing software.

The provided text describes the 510(k) premarket notification for the Dental Cone-beam Computed Tomography (model: FinScan F350) and its substantial equivalence to a predicate device. However, the document does not contain explicit acceptance criteria or a comprehensive study demonstrating that the device meets specific performance criteria in a quantitative manner as typically expected for device performance studies.

The document focuses on regulatory compliance, outlining how the FinScan F350 is substantially equivalent to the predicate device RCT700 (K213226) based on technological characteristics and adherence to recognized standards. It mentions "Performance bench testing" and "Clinical images acquired using FinScan F350 were evaluated by two US board certified experienced dentists to be of acceptable clinical effectiveness," but it does not provide specific metrics or the study details requested.

Here's an analysis based on the available information, noting what is not present:

1. Table of Acceptance Criteria and Reported Device Performance:

The document mentions performance bench testing that measured spatial resolution, CNR (Contrast-to-Noise Ratio), and homogeneity for CT image quality. It states that "The results demonstrate that the subject device is as effective, and perform as well as the predicate device." However, specific numerical acceptance criteria or the reported performance values for these metrics are NOT provided in the text.

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

The document briefly mentions "Clinical images acquired using FinScan F350 were evaluated..." but does not specify the sample size (number of images or cases) used for this evaluation, nor does it provide information on the data provenance (e.g., country of origin, retrospective or prospective nature of the image acquisition).

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

"Clinical images acquired using FinScan F350 were evaluated by two US board certified experienced dentists to be of acceptable clinical effectiveness for the proposed indications for use."

• Number of experts: 2
• Qualifications: US board certified experienced dentists. (Specific years of experience are not mentioned).

4. Adjudication Method for the Test Set:

The document states "evaluated by two US board certified experienced dentists," but it does not specify an adjudication method (e.g., whether consensus was required, if a third expert was used in case of disagreement, or if individual evaluations were aggregated).

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

No, an MRMC comparative effectiveness study is NOT mentioned in the provided text. The document describes a substantial equivalence submission, which typically focuses on demonstrating that a new device is as safe and effective as a legally marketed predicate device, rather than quantifying performance improvements with AI assistance. The device is described as an imaging system, not an AI-based diagnostic tool that assists human readers.

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

The FinScan F350 is a medical imaging device (CBCT, panoramic, cephalometric x-ray system) that produces images for diagnostic support. It includes "image processing software." The performance evaluation mentioned (bench testing and clinical image evaluation by dentists) is about the general image quality and clinical effectiveness of the imaging system itself. The concept of "standalone algorithm performance" (without human-in-the-loop) as typically applied to AI/CADe systems is not directly applicable or discussed for this device based on the provided text, as it's an image acquisition system, not primarily a standalone diagnostic algorithm.

7. The Type of Ground Truth Used:

For the "clinical images evaluation report," the ground truth seems to be implicitly expert judgment/consensus of the two US board-certified experienced dentists, who deemed the image quality "acceptable clinical effectiveness." Pathology or outcomes data are not mentioned as ground truth sources for this evaluation.

8. The Sample Size for the Training Set:

The document does not mention any training set size. This is expected as the document describes a hardware imaging device with associated standard image processing software, not a machine learning or AI algorithm that requires a "training set" in the context of deep learning. The software mentioned (FinScan and FinScanFW) performs control, image acquisition, reconstruction, storage, browsing, and processing, which are typically deterministic software functions, not machine learning models.

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

As no training set is mentioned as applicable to this type of device (traditional imaging system with processing software, not an AI/ML algorithm), this information is not applicable and not provided.

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KP-View 3D is intended to produce two-dimensional digital x-ray images including panoramic and cephalometric image, and three-dimensional digital x-ray images of the dental, oral, maxillofacial region, at the direction of healthcare professionals as diagnostic support for adult and pediatric patients.

This device is not intended for use on patients less than approximately 21 kg (46 lb) in weight and 113 cm (44.5 in) in height; these height and weight measurements approximately correspond to that of an average 5 year old.

The product ( Dental Cone-beam Computed Tomography, Model: KP-View 3D) uses cone-beam computed tomography (CBCT) through X-ray cone-beam, panoramic radiography, cephalometric radiography to produce images of the dental, oral and maxillofacial areas to provides diagnostic details for the medical facilities. This product consists of Frame, X-ray generator (including Integrated X-ray source tube head, X-ray tube, Collimator), Image receptor and Image processing system (including Computer, Monitor, and Software workstation).

The provided document is a 510(k) summary for the Yian Medical Technology (Haining) Co., Ltd. KP-View 3D Dental Cone-beam Computed Tomography system.

Here's an analysis of the acceptance criteria and study information, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" in a tabular format for the device's performance in the way a clinical study might. Instead, it demonstrates substantial equivalence to a predicate device (iDT901X1 Dental Cone-beam Computed Tomography, K232710) by comparing technical specifications and performance characteristics. The acceptance is based on meeting safety and performance standards relevant to dental X-ray equipment.

Here's a table summarizing key comparable technical and performance characteristics, as presented in the document, which implicitly serve as a basis for acceptance by showing equivalence to the already-cleared predicate:

The "acceptance criteria" can be inferred to be that the subject device's performance characteristics, as demonstrated through non-clinical bench testing, are equivalent to those of the predicate device, and that it complies with relevant recognized standards for safety and performance (e.g., IEC 60601 series for electrical and radiation safety, IEC 62366 for usability, IEC 62304 for software lifecycle, ISO 14971 for risk management).

2. Sample Size for the Test Set and Data Provenance

The document states, "The subject of this premarket submission, did not require clinical studies to support substantial equivalence." Therefore, there is no test set of patient data from clinical studies. The testing relies on non-clinical bench testing to demonstrate equivalence. The provenance of any underlying data that informed the predicate device's clearance is not mentioned here, as this is a Special 510(k) focused on modifications to an already cleared device.

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

Not applicable, as no clinical study with a test set requiring expert-established ground truth was conducted. The assessment is based on technical equivalence and compliance with standards.

4. Adjudication Method for the Test Set

Not applicable, as no clinical study with a test set was conducted.

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

Not applicable. This device is a dental cone-beam CT system, not an AI-powered diagnostic software. The submission focuses on hardware and software equivalence for image acquisition and processing.

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

Not applicable. This is not an algorithm-only device. It is a medical imaging system. Its performance is assessed through its ability to produce images that meet quality and safety standards, as demonstrated through bench testing.

7. The Type of Ground Truth Used

For non-clinical testing, the "ground truth" would be established by reference to engineering specifications, phantom measurements, and compliance with recognized standards (e.g., image quality metrics like spatial resolution (5lp/mm) measured with phantoms, radiation dose measurements, electrical safety checks).

8. The Sample Size for the Training Set

Not applicable. This is not an AI/machine learning device that requires a training set in the conventional sense. The device is a traditional medical imaging system.

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

Not applicable, as no training set was used.

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Dental Cone-beam Computed Tomography (Model:iDT901X1) is intended to produce two-dimensional digital x-ray images including panoramic and cephalometric image, and three-dimensional digital x-ray images of the dental, oral, maxillofacial region, at the direction of healthcare professionals as diagnostic support for adult and pediatric patients.

This device is not intended for use on patients less than approximately 21 kg (46 lb) in weight and 113 cm (44.5 in) in height; these height and weight measurements approximately correspond to that of an average 5 year old.

( Product Name: Dental Cone-beam Computed Tomography, The product Model:iDT901X1) uses cone-beam computed tomography (CBCT) through X-ray cone-beam, panoramic radiography, cephalometric radiography to produce images of the dental, oral and maxillofacial areas to provides diagnostic details for the medical facilities. This product consists of Frame, X-ray generator (including Integrated X-ray source tube head, X-ray tube,Collimator), Image receptor and Image processing system (including Computer, Monitor, Software workstation).

The provided document is a 510(k) Summary for a Dental Cone-beam Computed Tomography device (Model: iDT901X1). It includes a comparison with a predicate device and a summary of testing conducted. Based on the document, here's an analysis of the acceptance criteria and study information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of specific quantitative acceptance criteria for image quality and a direct comparison with reported device performance in a tabular format. However, it broadly states that "The overall image quality was acceptable for all cases and image types in various scanning mode for both adult and pediatric" based on a clinical evaluation.

Instead of clear acceptance criteria for image quality, the document lists various standards and guidance documents that the device complies with, which implicitly define performance expectations in relation to safety, electromagnetic compatibility, radiation protection, usability, software lifecycle, and acceptance tests for imaging performance. These standards serve as the basis for the "acceptance criteria" and the device's adherence to them is the "reported device performance."

Here's an interpretation based on the provided text, focusing on performance aspects mentioned in the comparison table and the summary of non-clinical tests:

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

• Test Set Sample Size: The document refers to "a number of sample scans and diagnostic images" for the clinical evaluation. It does not specify the exact number of cases or images used in this "test set."
• Data Provenance: Not specified. The document states that the manufacturer is "Yian Medical Technology (Haining) Co., Ltd." in CHINA, but does not explicitly mention the country of origin for the clinical evaluation data. It also does not specify if the data was retrospective or prospective.

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

• Number of Experts: "One US radiation-board certified experienced radiologists."
• Qualifications: "US radiation-board certified experienced radiologists." No specific years of experience are mentioned.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable/Not specified. The clinical evaluation was performed by a single radiologist who "studied independently a number of sample scans and diagnostic images to score different essential image quality related items." There is no mention of an adjudication process for consensus or conflict resolution, as only one expert was involved.

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

• MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The clinical evaluation involved a single radiologist.
• Effect Size of Human Readers Improvement with AI vs. without AI: Not applicable, as no AI component for image interpretation by human readers is described in the provided sections, nor was an MRMC study conducted. The device is a Dental Cone-beam Computed Tomography system for image acquisition, not an AI software for interpretation assistance.

6. If a Standalone (i.e. Algorithm only without human-in-the-loop performance) was Done

• Standalone Performance: The document describes the device as a "Dental Cone-beam Computed Tomography" system, which is a hardware device that produces X-ray images. There is no mention of an independent algorithm or AI component that would have standalone performance separate from human-in-the-loop operation. The "image processing system" is part of the overall device. However, the summary of testing includes "overall operation by taking and reviewing test images" and "image quality was acceptable," which implies an evaluation of the device's intrinsic imaging capabilities.

7. The Type of Ground Truth Used

• Type of Ground Truth: The ground truth for the clinical evaluation of image quality was based on expert consensus (or in this case, expert opinion from a single radiologist). The radiologist "scored different essential image quality related items." There is no mention of pathology or outcomes data being used as ground truth.

8. The Sample Size for the Training Set

• Training Set Sample Size: The document does not specify a training set sample size. The device is a hardware imaging system with an image processing system, not a machine learning algorithm or AI model that typically requires a distinct training set in the context of diagnostic performance evaluation for regulatory submission. The term "training set" is not used in relation to device performance testing in this document.

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

• Ground Truth for Training Set: Not applicable, as no training set for a machine learning model is mentioned or implied by the document.

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