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Hasla 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 CBCT X-ray System, Model: Hasla) 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 FDA 510(k) clearance letter and summary for the Yian Medical Technology (Haining) Co., Ltd. Dental CBCT X-ray System (Model: Hasla) indicate that this is a Special 510(k) for a device modification. This means the company is asserting that the modified device is substantially equivalent to their own previously cleared predicate device (Dental Cone-beam Computed Tomography, Model: iDT901X1, K232710) and that the modifications do not raise any new safety or effectiveness concerns.

Therefore, the submission relies on the previous clearance and asserts that the current device meets the same acceptance criteria as the predicate without requiring new comprehensive studies to prove that from scratch. Instead, it demonstrates that the modifications themselves do not negatively impact the performance, safety, or efficacy previously established.

Given this context, here's a breakdown of the information requested. It's important to understand that the document does not report on a new study for the current device to explicitly prove it meets acceptance criteria with new data. Instead, it leverages the prior clearance through a demonstration of equivalence.

Acceptance Criteria and Device Performance (as related to the predicate device and the claim of equivalence)

The document states: "The subject device and the predicate device are equivalent in the model name, same in the indications for use, patient population, use environment, software functions, and software level of documentation, performance characteristics and technical specification." It further specifies that "Any difference between the predicate device and the subject device have no impact on safety or efficacy of the subject device and do not raise any new potential or increased safety risks."

Therefore, the "acceptance criteria" for the current device are implicitly met by demonstrating that its performance and safety characteristics are unchanged from the predicate device, which had previously met its own acceptance criteria during its clearance process (K232710).

The summary provides a detailed comparison table (Table 1) between the subject device (Hasla) and the predicate device (iDT901X1). Essentially, all listed specifications and performance characteristics are presented as identical between the two.

Table 1: Acceptance Criteria (Implied from Predicate Equivalence) and Reported Device Performance

Study Details (Based on the provided document for the current 510(k) submission)

It's crucial to reiterate that this is a Special 510(k). The document explicitly states: "The modifications of the device have no impact on the product's design, working principle, software function, software documentation level, performance characteristic and technical specification indication for use, patient population, environment of use, user needs, thus no impact on the previously conducted Bench Testing and validation. No new bench testing or validation was needed. The previously conducted and submitted verification and validation test results are still valid for the modified device..."

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

   • Test Set Sample Size: Not applicable/Not specified as new data. The submission relies on the testing of the predicate device (K232710) and general compliance with standards.
   • Data Provenance: Not applicable/Not specified, as no new clinical or standalone performance studies were explicitly conducted for this submission beyond bench testing to confirm compliance with recognized standards. The submitter is Yian Medical Technology (Haining) Co., Ltd. in China.

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

   • Not applicable. This submission does not detail a study involving expert readers or ground truth establishment, as it's a Special 510(k) relying on equivalence to a previously cleared device.

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

   • Not applicable. No such study or adjudication method is described.

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 Dental CBCT X-ray System, which is an imaging acquisition device, not an AI-based diagnostic tool for human reader interpretation improvement. The document explicitly states: "The subject of this premarket submission, did not require clinical studies to support substantial equivalence."

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

   • No. This device is an imaging system, not a standalone algorithm. The clearance is based on the device's ability to produce images compliant with established standards, not on an algorithm's diagnostic performance. The document explicitly states: "The subject of this premarket submission, did not require clinical studies to support substantial equivalence." The bench testing cited ("Bench Testing Summary") confirms the physical and technical specifications against standards (e.g., spatial resolution, focal spot size, etc.), which are standalone performance characteristics of the device (hardware/software combined for image generation), but not a "standalone algorithm" in the typical sense of AI performance.

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

   • Not applicable. No new clinical studies requiring external ground truth were conducted for this Special 510(k). The evaluation revolves around compliance with engineering and radiation safety standards, and performance characteristics (like spatial resolution, scan time) derived from bench testing using phantoms or calibrated equipment.

7. The sample size for the training set:

   • Not applicable. This is not an AI algorithm requiring a training set in the conventional sense. The "software functions" mentioned are for image acquisition, data management, display, processing, and system settings, which are intrinsic to the operation of the imaging device itself.

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

   • Not applicable, as there is no specific training set described for an algorithm in this context.

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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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RCT600 is CBCT and panoramic x-ray imaging system with cephalometric. Which is intended to radiographic examination of the dento-maxillofacial, sinus and TMJ structure for adult and pediatric patients. 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.

RCT600 provides 3D computed tomography for scanning hard tissues such as bone and teeth. By rotating the C-arm, which houses a high-voltage generator, an X-ray tube and a detector on each end, CBCT images of dental maxillofacial structures are obtained by recombining data scanned from the same level at different angles. Functionalities include panoramic image scanning for obtaining images of whole teeth, and a cephalometric option for obtaining cephalometric images.

The RCT600 device, for which K243903 is the 510(k) number, is a Computed Tomography X-Ray System used for radiographic examination of dento-maxillofacial, sinus, and TMJ structures. It also includes cephalometric imaging, specifically wrist images for growth and maturity assessment for orthodontic treatment.

The acceptance criteria are implicitly defined by the safety and effectiveness information provided in the 510(k) summary, specifically by demonstrating substantial equivalence to the predicate device, RCT700 (K213226). The testing performed aims to prove that despite some differences (deletion of one-shot Ceph option, different scan Ceph detector, changes in magnification, and scan times), the RCT600 maintains similar safety and effectiveness characteristics.

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

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are based on demonstrating performance similar to the predicate device (RCT700, K213226) and meeting recognized international standards for medical electrical equipment and X-ray imaging devices.

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

• Sample Size for Test Set: The document mentions "Sample clinical images" were submitted for each imaging dental modality and that features were "clinically tested." However, it does not specify the number of cases or patients used in the clinical imaging test set.
• Data Provenance: The document does not explicitly state the country of origin of the data. It mentions "Clinical images were gathered from all detectors of the RCT600 system." The applicant is Ray Co., Ltd., based in SOUTH KOREA. Given the context, it is reasonable to infer the data either originated from or was managed by the manufacturer. The study is prospective, as it involved gathering clinical images with the new device.

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

• Number of Experts: "A licensed practitioner" (singular) reviewed the sample clinical images.
• Qualifications of Experts: The expert was an "independent, licensed practitioner/clinician DDS/ABOMR."
  • DDS stands for Doctor of Dental Surgery, indicating a dental professional.
  • ABOMR stands for American Board of Oral and Maxillofacial Radiology, indicating specialization in dental and maxillofacial radiology. No years of experience are specified.

4. Adjudication Method for the Test Set

The document states, "A licensed practitioner reviewed the sample clinical images and deemed them to be of acceptable quality for the device intended use." This indicates a single-reader review without an explicit adjudication process defined for multiple readers.

5. If a Multi-reader Multi-case (MRMC) Comparative Effectiveness Study was done

• No, an MRMC comparative effectiveness study was not done. The document describes a "clinical testing" where sample images were reviewed by a single licensed practitioner to confirm diagnostic quality. There is no mention of human readers improving with or without AI assistance, as this is an imaging device, not an AI diagnostic algorithm.

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

• This question is not directly applicable to the RCT600, as it is an X-ray imaging system, not an AI diagnostic algorithm. The "performance" being evaluated implicitly includes the algorithm within the imaging device that processes and reconstructs the images. The focus is on the imaging system's ability to produce diagnostically acceptable images for human interpretation. The clinical evaluation verifies the diagnostic quality of the images produced by the device, which is an assessment of its standalone imaging performance.

7. The Type of Ground Truth Used

The ground truth for the clinical image review was based on expert consensus (from the singular licensed practitioner) regarding the "acceptable quality for the device intended use" of the sample clinical images.

8. The Sample Size for the Training Set

The document does not specify a sample size for a training set. The device being described is an X-ray imaging system, not a machine learning algorithm that typically requires a separate training set. The "software" component mentioned underwent validation, but no details of training data for the software's inherent functions (e.g., image reconstruction algorithms) are provided, as these are typically validated through engineering and bench testing rather than a separate clinical "training set" in the context of device clearance.

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

As no explicit training set for an AI algorithm is mentioned, the concept of ground truth establishment for a training set is not applicable in the context of this device's submission description. The software validation relies on risk analysis and compliance with FDA guidance documents rather than a specific clinical training dataset and ground truth for learning.

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Green X 12 SE (Model : PHT-40CHS) is intended to produce panoramic, cephalometric, or 3D digital x-ray images. It provides diagnostic details of the dento-maxillofacial, 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 12 SE (Model : PHT-40CHS) 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 12 SE (Model : PHT-40CHS), a digital radiographic imaging system, acquires and processes multi-FOV diagnostic images for dentists. Designed explicitly for dental radiography. Green X 12 SE (Model : PHT-40CHS) 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 12 SE (Model : PHT-40CHS) can also acquire 2D diagnostic image data in conventional PANO and CEPH modes.

The provided document does not contain information regarding a study that proves the device meets specific acceptance criteria in the context of AI performance, clinical trials, or comparative effectiveness studies with human readers. The document describes a Computed Tomography X-Ray System named Green X 12 SE (PHT-40CHS) and its substantial equivalence to a predicate device (Green X 12 (PHT-75CHS)).

The performance data section primarily focuses on:

• Substantial Equivalence: Demonstrating that the new device is equivalent to the predicate device despite minor changes in detector technology and some feature deletions.
• Technical Performance Testing: Verification against international standards (IEC 61223-3-5) for general image quality indicators like Contrast, Noise, CNR, and MTF, and dosimetric performance (DAP).
• Safety and EMC: Compliance with relevant IEC standards for electrical, mechanical, environmental safety, and electromagnetic compatibility.
• Software Verification and Validation: Adherence to FDA guidance for device software functions.

Therefore, many of the requested categories for AI-related studies and acceptance criteria are not applicable based on the provided text.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state "acceptance criteria" in a table format for AI performance. Instead, it refers to equivalence to a predicate device and compliance with international performance standards for Computed Tomography X-ray systems.

2. Sample size used for the test set and the data provenance: Not applicable. The document discusses bench testing and comparison to a predicate device, not a test set of patient cases for AI evaluation.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. This information is relevant for AI (CAD) performance studies, which are not described here.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.

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 described. The device is an X-ray imaging system, not an AI-assisted diagnostic tool in the sense of a CADe/CADx system.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. The device is a physical imaging system.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable in the context of AI performance. For the technical performance aspects, the "ground truth" would be established by the specifications and measurements according to IEC standards.

8. The sample size for the training set: Not applicable. This device is an imaging system, not an AI algorithm requiring a training set in the conventional sense.

9. How the ground truth for the training set was established: Not applicable.

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Green X 21 (Model: PHT-90CHO) is intended to produce panoramic, cephalometric, or 3D digital Xray images. It provides diagnostic details of the dento-maxillofacial, sinus, TMJ, and ENT 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 21 (Model : PHT-90CHO) is an advanced 6-in-1 digital X-ray system specifically designed for 2D and 3D dental radiography. This system features six imaging modalities: PANO, CEPH (optional), DENTAL CT, ENT CT, MODEL, and FACE SCAN, all integrated into a single unit.

Green X 21's imaging system is based on digital TFT detectors, accompanied by imaging viewers and an X-ray generator.

The materials, safety characteristics, X-ray source, indications for use and image reconstruction/MAR(Metal Artifact Reduction) algorithm of the subject device are same as the predicate device (PHT-75CHS (K210329)). The subject device differs from the predicate device in the following ways: It is equipped with new X-ray detectors for CT/PANO and CEPH modification results in a different maximum FOV provided in a single scan for CT mode compared to the predicate device. For CEPH modality, the subject device utilizes a one- shot imaging capture method.

Additionally, the subject device includes new modalities such as ENT CT and FACE SCAN with a face scanner, along with new software functions, including Auto Pano, Smart Focus, and Scout.

The provided document describes a 510(k) premarket notification for the "Green X 21 (PHT-90CHO)" digital X-ray system. The core of the submission is to demonstrate substantial equivalence to a predicate device, the "Green X 18 (PHT-75CHS)". The document focuses heavily on comparing technical specifications and performance data to support this claim, particularly for the new components and features of the Green X 21.

However, it's crucial to note that this document does not describe a study that proves the device meets specific acceptance criteria in the context of an AI/human-in-the-loop performance study. Instead, it focuses on engineering performance criteria related to image quality and safety for an X-ray imaging device, and comparing these to a predicate device. The "acceptance criteria" discussed are primarily technical specifications and performance benchmarks for the X-ray system components (detectors, imaging quality metrics), rather than clinical performance metrics in disease detection with AI assistance.

Therefore, many of the requested items (e.g., sample size for test set, number of experts for ground truth, adjudication method, MRMC study, standalone performance) are not applicable or not detailed in this submission because the device in question is an X-ray imaging system, not an AI-based diagnostic tool. The "new software functions" mentioned (Auto Pano, Smart Focus, Scout) are described as image reconstruction or manipulation tools, not AI algorithms for clinical diagnosis.

Here's a breakdown based on the information available, addressing the points where possible and noting when information is absent or not relevant to the provided text:

Acceptance Criteria and Reported Device Performance

Given that this is an X-ray imaging device and not an AI diagnostic tool, the acceptance criteria are generally related to image quality, safety, and functional performance, benchmarked against standards and the predicate device.

Table of Acceptance Criteria and Reported Device Performance (as inferred from the document):

Study Details (as far as applicable and available)

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

   • Test Set Description: The "test set" in this context is referring to data collected through bench testing using phantoms for image quality assessment, and potentially clinical images for subjective comparison, rather than a clinical trial cohort.
   • Sample Size: Not specified in terms of number of patient images. The testing was conducted on the device itself using phantom studies (e.g., test patterns for MTF, line pairs for resolution, phantoms for low contrast).
   • Data Provenance: The bench tests were conducted in a laboratory ("in a laboratory using the same test protocol as the predicate device"). The "Clinical consideration" section for image quality evaluation implies some clinical image review, but the origin (e.g., country, retrospective/prospective) of these potential clinical images is not detailed.

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

   • Not Applicable in the traditional sense of an AI study. Ground truth for an X-ray imaging device's performance is typically established through quantitative measurements using phantoms and technical specifications, not expert consensus on medical findings.
   • The document mentions "Image Quality Evaluation Report and Clinical consideration" and concludes "the subject device performed equivalently or better than the predicate device in overall image quality." This implies some form of expert review for subjective image quality, but the number or qualifications of these "experts" are not detailed.

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

   • Not Applicable. As noted above, this is not an AI diagnostic study with a human-in-the-loop component requiring adjudication of disease findings. The evaluations are primarily technical and quantitative measurements comparing physical properties and image output.

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 study was not described. This is an X-ray imaging system, not an AI diagnostic software that assists human readers. The new software functions (Auto Pano, Smart Focus, Scout) described are image manipulation/reconstruction features, not AI for diagnostic assistance.

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

   • Not applicable in the context of an AI algorithm. The device is an X-ray system; its "standalone" performance refers to its ability to produce images according to technical specifications, which was assessed through bench testing (MTF, DQE, NPS, etc.). There is no mention of a diagnostic AI algorithm that operates standalone.

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

   • Quantitative Phantom Measurements and Technical Specifications: For image quality (MTF, DQE, NPS, line pair resolution, contrast, noise, CNR), the ground truth is established by physical measurements using standardized phantoms and reference values as per IEC standards.
   • Predicate Device Performance: A key "ground truth" for substantial equivalence is the established performance of the predicate device. The new device's performance is compared against this benchmark.
   • Internal Criteria: For functionalities like FACE SCAN, "internally established criteria" were used as a benchmark.

7. The sample size for the training set:

   • Not Applicable. This document describes an X-ray imaging device, not an AI model that requires a training set. The software functions mentioned (Auto Pano, Smart Focus, Scout) are described as computational algorithms for image reconstruction or enhancement, not machine learning models that learn from a training dataset.

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

   • Not Applicable. No training set for an AI model is described.

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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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ORTHOPANTOMOGRAPH™ OP 3D LX is an X-ray device that is intended to be used for imaging of adult and pediatric patients. The device can be configured to take panoramic, cephalometric or 3D images of cranio-maxillofacial complex including the ear, nose and throat (ENT) and airway regions, and cervical spine. The device can be configured to take carpus images.

The device is operated and used by qualified healthcare professionals.

ORTHOPANTOMOGRAPH™ OP 3D LX is a digital panoramic, cephalometric and cone beam computed tomography (CBCT) x-ray device. OP 3D LX is used for imaging of the craniomaxillofacial complex and neck areas including the ear, nose and throat (ENT) for diagnostic and treatment planning purposes.

This FDA 510(k) summary for the Orthopantomograph™ OP 3D LX (K241249) does not contain a typical acceptance criteria table with reported device performance for an AI/ML powered device, nor does it detail a study proving device performance against such criteria. This submission is for an X-ray imaging system, not an AI/ML algorithm.

The core of this submission is to demonstrate substantial equivalence to a previously cleared predicate device (K230505), not to prove specific diagnostic performance against defined criteria in the way an AI algorithm would.

Therefore, the following information, based on the provided document, will focus on what is available, explaining why certain requested details for AI/ML validation are not present in this type of submission.

1. Table of Acceptance Criteria and Reported Device Performance

As this is a filing for an X-ray imaging system and not an AI/ML diagnostic algorithm, there isn't a table of clinical performance acceptance criteria with reported metrics like sensitivity, specificity, AUC, etc. Instead, performance is demonstrated through adherence to international and FDA-recognized consensus standards for medical electrical equipment and X-ray systems.

The device's performance is intrinsically linked to its ability to capture high-quality images and operate safely and effectively, as verified through compliance with these standards.

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

Not applicable. This submission doesn't describe a test set of medical images used to evaluate an AI/ML algorithm's performance. The "testing" involved non-clinical bench testing to ensure the device meets safety and performance standards for an X-ray system.

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

Not applicable. As there's no AI/ML specific diagnostic performance study, there's no ground truth establishment by experts in the context of image interpretation.

4. Adjudication Method for the Test Set

Not applicable.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. This type of study is typically performed to evaluate the impact of an AI assistance tool on human reader performance, which is not relevant to an X-ray imaging hardware submission.

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

Not applicable. This device is an X-ray imaging system, not a standalone algorithm.

7. The Type of Ground Truth Used

Not applicable. Compliance is demonstrated through adherence to engineering and safety standards, rather than diagnostic ground truth.

8. The Sample Size for the Training Set

Not applicable. This submission concerns an X-ray machine, not an AI/ML algorithm that requires a training set.

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

Not applicable.

Summary of Device Acceptance and Performance from the Document:

The acceptance of the Orthopantomograph™ OP 3D LX device is based on its substantial equivalence to a legally marketed predicate device (K230505) and its conformity to a comprehensive list of international and FDA-recognized consensus standards.

The document states:

• "Non-Clinical performance bench testing to international standards (and FDA recognized consensus standards) for Computed tomography x-ray system has been conducted to determine conformance..."
• The subject device shares "the same architectural components and utilizes the same X-ray generation as the Predicate Device."
• "Both devices utilize cone beam x-ray technology to acquire volumetric data."
• "Both the Subject Device have the same image modes, X-ray source, focal spot, image detector scintillator, 2D image performance – DQE / MTF 70kV RAQS, 3D image technique, 3D field of view, 3D total viewing andel size, reconstruction software, 3D's effective exposure time, Ceph exposure time, Patient position, system footprint, 3D image programs, and material patient contacting components."
• The primary difference noted is a minor one regarding the Automatic Exposure Control (Automatic Dose Control) functionality, which is identical to a secondary predicate device.

Key conclusions from the document regarding acceptance:

• "Clinical data is not needed to characterize performance and establish substantial equivalence."
• "The non-clinical test data characterize all performance aspects of the device based on well-established scientific and engineering principles."
• "Based on a comparison of intended use, indications, material composition, technological characteristics, principle of operation, features and performance data, the Orthopantomograph™ OP 3D LX is deemed to be substantially equivalent to the predicate device."

Therefore, the study proving the device meets "acceptance criteria" here is the non-clinical bench testing and engineering analysis demonstrating compliance with relevant standards and substantial equivalence to a predicate device, rather than a clinical performance study with defined diagnostic endpoints.

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T2 PLUS is digital X-Ray imaging equipment for dental professionals that converts X-Ray signals into digital signals to acquire 2D images and reconstruct them into 3D images, using Panoramic (PANO), Cephalometric (CEPH), and Computed Tomography (CT) technology for the diagnosis of the anatomical structure of the oral and maxillofacial area. T2-CS-P provides all three modes, whereas T2-C-P provides the first two modes and excludes the CEPH mode. The devices are operated and used by physicians, dentist and X-Ray technicians.

Use is contraindicated for patients with a head circumference of less than 48 cm or those aged 2 years or younger.

T2 PLUS is a digital X-ray CT, panoramic, and Cephalo imaging system device composed of X-ray generator, X-ray controller, X-ray supporter, image processing unit (sensor), PC, and software. The apparatus attached to the equipment column is a structure that can be rotated 360 by the system control unit. This system control unit actuates the motor control, X-ray generator, and image processing unit (sensor). The height controlling unit controls the column and adjusts the height of the equipment. The X-ray generator and image processing unit (sensor) are attached to the rotating apparatus. When the rotating apparatus starts the rotation, X-ray is irradiated from the Xray generator (generating unit). This X-ray irradiation penetrates the subject and reaches the image processing unit (sensor), and then is converted into electric signals to secure imagery information. Inside the imaging section of the image processing unit (sensor), real time X-ray input is converted into electric signals and consecutively combined, resulting in imagery information. The combined panoramic imagery information is then sent to the PC and saved in patient management software.

The provided document describes the T2 Plus digital X-ray imaging equipment, focusing on its substantial equivalence to a previously cleared predicate device rather than presenting a detailed study with acceptance criteria for a novel AI or diagnostic algorithm. Therefore, many of the requested elements for a study proving a device meets acceptance criteria are not explicitly stated or applicable in this 510(k) summary.

However, I can extract the information pertinent to the device's performance evaluation and a "study" conducted to support substantial equivalence.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria & Reported Device Performance:

The document does not explicitly state quantitative "acceptance criteria" for diagnostic performance in terms of metrics like sensitivity, specificity, or AUC, as it's a submission for substantial equivalence of imaging equipment, not a new diagnostic algorithm that interprets images. The study's aim was to show equivalent image quality to the predicate device.

"In conclusion, the imaging evaluator confirmed that both the proposed device and the predicate device produce radiological images of adequate quality for dental and orthodontic diagnosis." |
| Produce "radiological images of equivalent quality" for diagnosis. | "This confirmed that the proposed device and the predicate device produce radiological images of equivalent quality, making them suitable for diagnosis." |

Note: The acceptance criteria are inferred from the study's objective: to demonstrate equivalent image quality for diagnostic purposes. No specific quantitative thresholds (e.g., "score must be X or higher") are provided.

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

• Test Set Sample Size: A total of 31 cases were reviewed.
  • CT: 10 cases
  • Cephalometric (Ceph): 10 cases
  • Panoramic (Pano): 11 cases
  • For each case, both the proposed device (T2-CS-P) and the predicate device (T2-CS) images were reviewed, meaning 31 image sets from T2-CS-P and 31 image sets from T2-CS.
• Data Provenance: Not explicitly stated regarding country of origin. The study states images were from "patients of the same gender and similar age group, under identical conditions." It implies retrospective image collection from existing patients, as it refers to "patients" not trial participants.
  • Given the manufacturer is based in the Republic of Korea, the data may originate from there, but this is not explicitly stated.

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

• Number of Experts: "A radiologist" (singular) was used as the "evaluator."
• Qualifications of Experts: The qualification provided is "a radiologist." No further details on years of experience, sub-specialty, or board certification are given.

4. Adjudication Method for the Test Set:

• Adjudication Method: Not applicable/None explicitly described. Since only one radiologist served as the evaluator, there was no need for adjudication among multiple readers. The radiologist's assessment served as the primary evaluation.

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

• MRMC Study: No. This was not an MRMC study. It involved a single evaluator (radiologist) comparing images from the proposed device and predicate device.
• Effect Size: Not applicable, as no MRMC study was performed and no quantitative diagnostic performance metrics (e.g., sensitivity, specificity, AUC) were reported. The evaluation was qualitative ("scores... were identical").

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

• Standalone Study: Not applicable. The device is imaging equipment. The "study" evaluated the image quality of the equipment as interpreted by a human radiologist, not the performance of an independent AI algorithm. There is no AI component described that would operate in a "standalone" mode for diagnostic interpretation.

7. The Type of Ground Truth Used:

• Type of Ground Truth: The "ground truth" for purpose of this image quality comparison was the expert consensus/opinion of a single radiologist regarding the image quality and its adequacy for diagnosis. This is not a "true" clinical ground truth from pathology or long-term outcomes, but rather a subjective assessment of image utility.

8. The Sample Size for the Training Set:

• This information is not provided. The document focuses on performance testing for substantial equivalence, not on the development of an AI model that requires a training set. The device is X-ray imaging equipment, not an AI diagnostic algorithm, so the concept of a "training set" for the device itself is not applicable in the typical sense of machine learning.

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

• Not applicable, as there is no description of a "training set" for an AI model within the context of this device's performance evaluation.

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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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