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'eco-x series (eco-x, eco-x-s)' is 4 in 1 digital equipment that provides CT, Panoramic, Cephalometric, and Model Scan images by using X-Ray scan. It provides 2D images for diagnosing cranial bone tissue including adult and pediatric teeth, jaw, oral structures and skull. Also, it provides 3D images by reconstructing images acquired by capturing cervical bone and occipital regions.

In addition, 'eco-x series' is used as diagnosis for general and/or orthodontic treatment, and also is intended to use for ENT (Ear, Nose, and Throat) and dentomaxillofacial diagnosis.

This Equipment is a Dental X-Ray imaging device used for diagnostic purpose in dental treatment. The operating principle of this device is obtaining the tomographic images by rotating arm to get the recombination data, X-ray generator and detector rotate around the patient to irradiate the X-ray, and penetrated X-ray is measured by the detector, When the X-ray is irradiated on the teeth area for instance, large amount of X-ray is attenuated because objects such as bones are highly dense, On the contrast, X-ray is more permeable to small molecules with low density such as skin or tissue, so more X-ray would pass through the subject. By measuring data obtained from measuring the X-ray is reconstructed by the software to display and analyze the anatomical structure for the diagnosis purposes.

The eco-x Series consists of 2 different models. The eco-x-s supports cephalometric scan compared with the eco-x, which do not support CEPH mode.

The provided text is a 510(k) Summary for the eco-x Series, a dental X-ray imaging device. It primarily focuses on demonstrating substantial equivalence to predicate devices based on technological characteristics and non-clinical testing. It does not contain information about acceptance criteria or a study proving that the device meets those criteria, especially in terms of clinical performance or human-in-the-loop studies (MRMC).

The document mentions "non-clinical test summary" and "performance test" related to imaging properties and patient dosage, but these are bench tests and not clinical studies with acceptance criteria for diagnostic performance.

Therefore, I cannot fulfill most of your request as the information is not present in the provided document.

Here's what I can extract based on the limited information related to performance:

1. Table of acceptance criteria and reported device performance:

The document mentions meeting standards like IEC 61223-3-4 for imaging performance and providing MTF and DQE values for detectors. However, it does not explicitly state "acceptance criteria" alongside specific "reported device performance" in a table format for diagnostic accuracy or clinical utility of the device in a human setting. The table below reflects the bench test results compared to predicate devices, rather than clinical acceptance criteria.

Regarding the other points of your request, the document does NOT provide information on:

• 2. Sample size used for the test set and the data provenance: The document mentions "bench testing" but provides no details on "test sets" in the context of patient data, number of cases, or data provenance (country of origin, retrospective/prospective).
• 3. Number of experts used to establish the ground truth... and their qualifications: Not applicable as no clinical ground truth establishment is described.
• 4. Adjudication method for the test set: Not applicable.
• 5. If a multi reader multi case (MRMC) comparative effectiveness study was done...: No, the document explicitly states "Non-Clinical Test Summary" and focuses on comparison to predicate "technological characteristics" rather than clinical effectiveness or human reader performance. This device is a traditional X-ray imaging system, not an AI-assisted diagnostic tool.
• 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The document describes "software validation" for the original software and OTS software (image viewer) and mentions an image reconstruction algorithm (FBP). However, it does not refer to an "algorithm only" performance study in the sense of an AI model's diagnostic accuracy. The performance tests mentioned are related to hardware performance (image quality, dose) of the X-ray system itself.
• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): For the performance tests conducted, the "ground truth" would be physical measurements of image quality parameters (e.g., resolution targets for MTF, dose meters for CTDIw/DAP) and not clinical diagnostic outcomes or expert consensus.
• 8. The sample size for the training set: Not applicable as this is not an AI/machine learning device with a training set.
• 9. How the ground truth for the training set was established: Not applicable.

In summary, the provided document is a 510(k) Summary for a medical imaging device, demonstrating substantial equivalence through technical comparisons and bench testing against established standards and predicate devices. It does not include information on clinical acceptance criteria or studies involving human readers or AI performance.

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Will3D is a software application used for the display and 3D visualization of medical image files from scanning devices, such as Dental CT scanner. It is intended for use by radiologists, clinicians, referring physicians and other qualified individuals to retrieve, process, render, store, print, assist in diagnosis, and distribute images utilizing standard PC hardware. Additionally, Will3D is a preoperative software application and evaluation and evaluation of dental implants, orthodontic planning and surgical treatments.

Will3D is one of the components of a Picture Archiving and Communications System(PACS). Will3D is the software application that provides image viewing and manipulation in the diagnostic imaging setting.

The provided document describes the Will3D software, a medical image management and processing system. The core of the submission is to demonstrate its substantial equivalence to a previously cleared device, K170180 HDX WILL CORP. Will3D. As a result, the document primarily focuses on comparing the new Will3D with its predicate rather than detailing a specific study to prove new acceptance criteria.

The document does not explicitly state "acceptance criteria" and "reported device performance" in a separate section with specific numerical targets. Instead, it asserts substantial equivalence based on identical indications for use and technological characteristics to the predicate device. The "acceptance criteria" are implied to be that the device performs functionally identically and safely to the predicate.

Here's an attempt to extract and infer the requested information based on the provided text, acknowledging that some details might be missing or indirectly stated due to the nature of a 510(k) summary focused on substantial equivalence:

1. Table of Acceptance Criteria and Reported Device Performance

As the submission focuses on substantial equivalence to a predicate device with identical specifications, the "acceptance criteria" are implicitly met by demonstrating that the new device shares the exact same indications for use and technological characteristics as the predicate. The "reported device performance" is, therefore, that it performs as expected for these features.

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

The document does not specify a separate "test set" or its sample size. The review relies on the assertion that "performance testing conducted demonstrate that the subject device is as safe as effective as the predicate." However, no details about the specific test data (e.g., number of cases, retrospective or prospective nature, country of origin) are provided. This is typical for a 510(k) submission where substantial equivalence is claimed based on identical design and function to a predicate.

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

This information is not provided in the document. Given the nature of a substantial equivalence claim for a medical image management and processing system, the "ground truth" for verifying its technical functionalities (like MPR, 3D visualization, measurements) would likely involve comparing output to known standards or expert interpretation, but specific expert involvement for a 'test set' isn't detailed.

4. Adjudication Method for the Test Set

This information is not provided.

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

No MRMC comparative effectiveness study is mentioned. The document focuses on the device's performance and characteristics, not on how human readers improve with or without AI assistance, as the Will3D is described as an image management and processing system rather than a diagnostic AI aid with a comparative effectiveness study.

6. Standalone (Algorithm Only) Performance Study

The device is described as "standalone software" and "one of the components of a Picture Archiving and Communications System(PACS)" that "provides image viewing and manipulation." The performance testing mentioned is implied to be for this standalone software. However, no specific details of a standalone performance study (e.g., metrics, methodologies) are provided beyond the general statement that "performance testing conducted demonstrate that the subject device is as safe as effective as the predicate."

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for performance testing. For a device like Will3D, which handles display, visualization, and processing of medical images, the "ground truth" for its functionalities would typically involve:

• Technical specifications: Verifying that the software accurately renders images, performs measurements, and generates reconstructions according to predefined technical standards and algorithms.
• Expert Consensus/Clinical Evaluation (implied): Ensuring that the processed images and visualizations are clinically acceptable and provide accurate information for diagnostic and planning purposes, likely through evaluation by radiologists, clinicians, and other qualified individuals, as mentioned in its indications for use.
• Comparison to Predicate (implied): Demonstrating that its output and functionality are identical or equivalent to the predicate device.

8. Sample Size for the Training Set

No information about a training set or its sample size is provided. This is expected as Will3D is described as an image processing and visualization software, not a machine learning or AI algorithm that typically requires a separate training set.

9. How Ground Truth for the Training Set Was Established

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

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The DENTRIa series is a Computed Tomography X-Ray imaging device specialized in diagnosing general dental treatments and orthodontic purpose using Panoramic and Cephalometric images respectively. In addition DENTRIC series is used in the field of Otolaryngology by capturing 360 degree rotation sequence of the head and neck areas, including the ENT and dentomaxillofacial areas for a dental treatment in adult and pediatric dentistry and obtains x-ray images from different angles and calculate through computer-processed to produce 3D x-ray tomographic images. The DENTRIC series used by physicians, dentists, and x-ray technologists.

This Equipment is a Dental X-Ray imaging device used for diagnostic purpose in dental treatment. The operating principle of this device is obtaining the tomographic, and panoramic images by rotating arm to get the recombination data. X-ray generator and detector rotate around the patient to irradiate the X-ray, and penetrated X-ray is measured by the detector. When the X-ray is irradiated on the teeth area for instance, large amount of X-ray is attenuated because objects such as bones are highly dense. On the contrast, X-ray is more permeable to small molecules with low density such as skin or tissue, so more X-ray would pass through the subject. By measuring data obtained from measuring the X-ray is reconstructed by the software to display and analyze the anatomical structure for the diagnosis purposes.

The provided text is a 510(k) summary for the DENTRIa series of computed tomography X-ray systems, which focuses on demonstrating substantial equivalence to a predicate device. It contains information about non-clinical testing for imaging performance but does not describe a study that uses acceptance criteria for algorithm performance (e.g., sensitivity, specificity, or accuracy) against a defined ground truth for a test set, which would typically be present for an AI/ML medical device.

Therefore, I cannot provide a table of acceptance criteria and reported device performance for an AI/ML algorithm or details about a study evaluating its performance in the requested format.

However, I can extract information related to the device's technical performance testing that is present in the document.

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

As stated above, this document primarily discusses the substantial equivalence of an X-ray imaging device to a predicate device based on technical characteristics and general performance, not the performance of an AI/ML algorithm against clinical acceptance criteria. The document lists technical specifications rather than specific clinical performance criteria for an AI/ML component.

Below is a table summarizing some of the technical performance evaluations mentioned:

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

The document does not describe a test set or data provenance in the context of validating an AI/ML algorithm's clinical performance. The "performance test" section mentions "bench testing" and "image performance testing" conducted according to IEC 61223-3-4 and SSXI (Solid State X-ray Imaging) Devices guidance. These are hardware and imaging physics performance tests, not clinical evaluations with patient data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

Not applicable. The document does not describe a clinical ground truth establishment process for an AI/ML component.

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:

Not applicable. The submission is for an X-ray imaging device, not an AI/ML software assistance tool for readers.

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

Not applicable. The document describes an X-ray imaging system, which inherently involves human operation and interpretation. The "software validation" section mentions "original software and OTS software as an image viewer" and that the "algorithm type of image reconstruction is FBP (Filtered Back Projection)," but this is about image reconstruction, not a standalone diagnostic AI algorithm.

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

Not applicable. For the technical performance tests mentioned (MTF, DQE), the "ground truth" would be established physical standards and measurement techniques.

8. The sample size for the training set:

Not applicable. There is no mention of a training set for an AI/ML algorithm.

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

Not applicable.

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This X-ray Unit is intended to be used in portable applications, by a qualified/trained dentist or technician on both adult and pediatric subjects for taking diagnostic radiographic exposures of dental structures.

The Xcam is a portable X-ray for taking diagnostic radiographic exposures of body parts and operates on 11.1V DC supplied by a battery. Xcam is composed of x-ray generator including x-ray tubes, equipment controller, power controller, user interface and x-ray collimator. Xcam can be used with an imaging sensor. The image receptor, necessary component for a fully functional x-ray system, is not part of the submitted device.

The provided text is a 510(k) summary for the Xcam dental X-ray system, which focuses on demonstrating substantial equivalence to a predicate device (REXTAR X) based on technical characteristics and compliance with electrical safety, EMC, and radiation protection standards.

It does NOT contain information about acceptance criteria or a study that proves the device meets those criteria, particularly not in the context of AI/ML performance metrics like sensitivity, specificity, or AUC, or human reader studies with AI assistance.

The document discusses:

• Device Description and Indications for Use: The Xcam is a portable X-ray unit for diagnostic radiography of dental structures in adults and pediatric subjects, used by qualified dentists or technicians.
• Substantial Equivalence: It compares the Xcam's technical specifications (dimensions, weight, tube voltage, tube current, exposure time, filtration, focal spot size, etc.) to the predicate device, concluding no significant differences that would adversely affect use.
• Non-Clinical Testing: It mentions compliance with electrical safety (ES60601-1), electromagnetic compatibility (IEC 60601-1-2), radiation protection (IEC 60601-1-3, 60601-2-65), and EPRC standards. It also notes software validation for "MODERATE level of concern software" and cybersecurity considerations.

Therefore, based solely on the provided text, I cannot describe acceptance criteria or a study proving the device meets them in the manner requested (e.g., performance metrics, sample sizes for test/training sets, expert adjudication, MRMC studies, ground truth establishment). The document confirms the device meets regulatory standards for a traditional X-ray system, not an AI/ML-driven diagnostic aid.

To answer your request, if this were an AI/ML device, the missing information would typically be found in a separate section detailing the clinical performance study.

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Star-X is intra-oral X-ray system for dental diagnosis that can radiate X-ray images by irradiating X-rays generated by high voltage to an anode in an X-ray tube and reacting with the receptor (film, sensor).

Star-X is a dental X-ray equipment (extra-oral source system) intended for use by qualified dentist or dental technician for both adult and pediatric patient for producing diagnostic dental radiographs for treatment of diseases of the teeth, jaw, and other oral structures using intra-oral image receptors.

The Star-X is an X-ray generator for dental intra-oral X-ray imaging and intended to be used for producing diagnostic dental radiographs for treatment of disease of teeth, jaw and oral structures. The Star-X is consisted of X-ray generator, beam limiting device (cone), control panel, mechanical arm, support device, chair and backrest. The control panel allows for accurate exposure control and adjustable arm provide for convenient patient positioning.

The provided text is a 510(k) summary for the Star-X Intraoral X-ray System, which focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed acceptance criteria and study results for a new AI/CAD device. Therefore, much of the requested information regarding acceptance criteria, specific study designs (like MRMC or standalone performance), sample sizes, ground truth establishment, and expert qualifications for performance evaluation is not present in the given document.

The document primarily addresses the safety and technical characteristics of the X-ray system itself, comparing it to existing X-ray units. It does not describe a study to prove meeting acceptance criteria in the context of an AI/CAD system for image analysis.

Here's a breakdown of what can be extracted and what is missing based on your specific request:

Acceptance Criteria and Reported Device Performance

The document describes compliance with various electrical safety, electromagnetic compatibility, and radiation protection standards for an X-ray system. It does not present acceptance criteria in terms of diagnostic performance metrics (e.g., sensitivity, specificity, accuracy) for an AI/CAD system.

The "device performance" reported here is about the technical specifications of the X-ray unit, not an AI's diagnostic capabilities.

Missing Information for AI/CAD System Evaluation:

The following information cannot be provided from the given document as it pertains to the regulatory submission for an X-ray imaging device, not an AI/CAD diagnostic software.

1. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable, as this is an X-ray hardware device, not an AI/CAD system being evaluated for diagnostic performance.
2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable.
3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
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: Not applicable.
5. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable.
6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable.
7. The sample size for the training set: Not applicable (for AI model training). The software mentioned is for the control and operation of the X-ray machine.
8. How the ground truth for the training set was established: Not applicable.

Summary of the Document's Content:

The provided document is a 510(k) premarket notification for the "Star-X, Intraoral X-ray System." It seeks to establish substantial equivalence to existing predicate X-ray devices. The "study" referenced in the document is a series of non-clinical tests demonstrating compliance with electrical safety, EMC, radiation protection, and performance standards relevant to X-ray hardware. It also mentions software validation for the control software of the X-ray system, confirming it was designed, developed, verified, and validated for a moderate level of concern, and that cybersecurity information was provided. This is not a diagnostic performance study of an AI algorithm on medical images.

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The DENTIOIII series is intended for dental radiographic examination of the teeth and temporomandibular joints, specifically for panoramic and cephalometric examinations. It is to be used only by dental practitioners and/or radiologists

This equipment is a dental X-ray imaging device used for diagnostic purposes in dental examination. The operating principle of this device is obtaining the panoramic images by rotating the arm to get the recombination data, having the X-ray generator and detector rotate around the patient to irradiate the X-ray, and measuring the penetrated X-ray using the detector. When the X-ray irradiates the teeth area for instance, a large amount of X-ray is attenuated because objects such as bones are highly dense. By contrast, the X-ray permeates more through small molecules with low density such as skin or tissue measuring the X-ray is reconstructed by the software for display and analysis, the anatomical structure can be viewed for the diagnosis purposes. In addition, the Cephalo arm uses the scanning method.

This document is a 510(k) summary for the DENTIOIII series, an Extraoral Source X-Ray System. The summary focuses on establishing substantial equivalence to previously cleared predicate devices, rather than presenting a standalone clinical study to prove new acceptance criteria for an AI/CADe device. Therefore, the information required to directly answer your request about acceptance criteria and study proving device meets those criteria (especially regarding AI performance, human reader improvement, and ground truth establishment methods for large datasets) is not present in this document.

The document primarily addresses the following for comparison with predicate devices:

• Technical characteristics: X-ray tube properties, detector types, pixel size, active area, MTF, DQE, geometry, software, and compliance with electrical safety and radiation protection standards.
• Intended use: Dental radiographic examination of teeth and temporomandibular joints for panoramic and cephalometric images.

However, based on the provided document, here's what can be extracted and inferred, along with explicit statements about missing information for your specific request related to AI/CADe systems:

No AI/CADe Performance Details:
This 510(k) application is for an "Extraoral Source X-Ray System," which is a device for acquiring X-ray images. It is not an AI/CADe device that performs analysis or assists human readers. Therefore, the concepts of "acceptance criteria" and "study proving the device meets acceptance criteria" as they relate to AI/CADe performance (e.g., sensitivity, specificity, human reader improvement, ground truth establishment by experts) are not applicable to this document's content. The "acceptance criteria" discussed implicitly refer to meeting technical specifications and safety standards for an imaging device.

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

The document does not present explicit "acceptance criteria" in the format of pass/fail metrics for a clinical study assessing diagnostic performance of an AI/CADe. Instead, it compares technical specifications of the proposed DENTIOIII series to predicate devices to establish substantial equivalence.

Implicit "Acceptance Criteria" (Technical Specifications for an Imaging Device):

The performance of the device is reported through its technical specifications, compared to predicate devices. These essentially serve as the "performance" data that needs to be "accepted" as substantially equivalent.

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WillCeph is intended for use by specialized dental practices for capturing, storing and presenting patient images and radiographs and to aid in cephalometric analysis, orthodontic treatment planning and case follow-up. Results produced by the software tools are to be interpreted by trained and licensed dental practitioners.

WillCeph is a software device for Cephalometric Analysis & Maxillo-facial Surgical Simulation, Consulting, Diagnosis, Surgical Planning, Analysis, Growth Forecast. It includes features for patient data management, various gallery formats, image fusion and comparing, cephalogram and tracing on digitized X-ray film, treatment simulation (VTO / STO), chart and analysis, superimposition, photo-film image fusion, growth forecast, and PACS integration (Option). It can be used as a client or standalone software on Microsoft Windows 7 or higher.

The provided document, a 510(k) premarket notification for the "WillCeph" device, contains a limited amount of information regarding acceptance criteria and study details. It explicitly states that "No clinical studies were considered necessary and performed." This means there is no study described in this document that proves the device meets specific acceptance criteria through clinical performance.

However, the document does refer to "Non-Clinical Test Summary" and states that "WillCeph ... was designed and developed according to a software development process and was verified and validated." While it doesn't provide specific numerical acceptance criteria or detailed performance reports, it implies that the device was tested against design specifications.

Based on the available information, here's a breakdown of what can be extracted:

1. Table of Acceptance Criteria and Reported Device Performance

As no clinical study was performed, there are no specific performance metrics reported for device clinical performance. The "acceptance criteria" here would be related to software verification and validation, but the document does not detail these criteria or their fulfillment numerically. The comparison table focuses on technological characteristics and intended use, demonstrating substantial equivalence to a predicate device, rather than explicit performance against predefined criteria.

Summary of Technological Characteristics (extracted from the comparison table, serving as a basis for substantial equivalence, not performance):

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

Since "No clinical studies were considered necessary and performed," there is no test set in the traditional sense for clinical performance evaluation. The "test set" would implicitly refer to data used for software verification and validation, but details on sample size, country of origin, or retrospective/prospective nature are not provided.

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

Given the absence of clinical studies, there is no mention of experts or ground truth establishment for a clinical test set. The document focuses on regulatory compliance through substantial equivalence.

4. Adjudication Method for the Test Set

As no clinical test set is described, no adjudication method is mentioned.

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

No MRMC comparative effectiveness study was performed or described. The document does not discuss human reader performance with or without AI assistance.

6. Standalone Performance

The product, WillCeph, is a standalone software that aids in cephalometric analysis. However, its effectiveness is assessed through substantial equivalence to a predicate device, not through a standalone clinical performance study. The statement "Results produced by the software tools are to be interpreted by trained and licensed dental practitioners" indicates human-in-the-loop, but the core functionality is algorithm-only. The document does not provide standalone performance metrics for the algorithm.

7. Type of Ground Truth Used

As no clinical study was performed, there is no explicit mention of ground truth types (e.g., expert consensus, pathology, outcomes data) for clinical performance evaluation. The "ground truth" for software verification and validation would likely be defined by internal software requirements and specifications.

8. Sample Size for the Training Set

The document does not mention a training set or its sample size. This suggests the device's functionality is based on established algorithms and calculations for cephalometric analysis, rather than a machine learning model trained on a large dataset for image interpretation.

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

Since no training set is mentioned, there is no information on how ground truth for a training set was established.

In summary: The WillCeph device received 510(k) clearance based on demonstrating substantial equivalence to a legally marketed predicate device (ORTOMED - K131348). This means the FDA determined that the device is as safe and effective as the predicate device, primarily through a comparison of their intended use and technological characteristics, and through non-clinical software verification and validation activities (which are not detailed in terms of their criteria or results). No clinical studies or performance data against specific numerical acceptance criteria were deemed necessary or provided in this submission for device clearance.

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Will3D is a software application used for the display and 3D visualization of medical image files from scanning devices, such as Dental CT scanner. It is intended for use by radiologists, clinicians, referring physicians and other qualified individuals to retrieve, process, render, review, store, print, assist in diagnosis, and distribute images utilizing standard PC hardware. Additionally, Will3D is a preoperative software application used for the simulation and evaluation of dental implants, orthodontic planning and surgical treatments.

The Will3D is one of the components of a PACS (Picture Archiving and Communications System). The Will3D is the software application that provides image viewing and manipulation in the diagnostic imaging setting.

The provided document is a 510(k) premarket notification for the medical device "Will3D," a software application for dental imaging. It outlines the device's intended use, technological characteristics, and comparison to predicate devices to establish substantial equivalence.

However, the document states: "No clinical studies were considered necessary and performed."

Therefore, the requested information regarding acceptance criteria, device performance, sample sizes, expert ground truth establishment, adjudication methods, multi-reader multi-case studies, standalone performance, and training set details cannot be provided from this document because no clinical studies were conducted to prove the device meets acceptance criteria derived from such studies. The equivalence is established purely on technological characteristics and intended use compared to predicate devices.

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The DENTRIα series is a Computed Tomography X-Ray imaging device specialized in diagnosing general dental treatments and orthodontic purpose using Panoramic and Cephalometric images respectively. In addition DENTRIα series is used in the field of Otolaryngology by capturing 360 degree rotation sequence of the head and neck areas, including the ENT and dentomaxillofacial areas for a dental treatment in adult and pediatric dentistry and obtains x-ray images from different angles and calculate though computer-processed to produce 3D x-ray tomographic images. The DENTRIα series is used by physicians, dentists, and x-ray technologists.

This Equipment is a Dental X-Ray imaging device used for diagnostic purpose in dental treatment. The operating principle of this device is obtaining the tomographic images by rotating arm to get the recombination data, X-ray generator and detector rotate around the patient to irradiate the X-ray, and penetrated X-ray is measured by the detector, When the X-ray is irradiated on the teeth area for instance, large amount of X-ray is attenuated because objects such as bones are highly dense, On the contrast, X-ray is more permeable to small molecules with low density such as skin or tissue, so more X-ray would pass through the subject. By measuring data obtained from measuring the X-rav is reconstructed by the software to display and analyze the anatomical structure for the diagnosis purposes.

The DENTRIα Series are classified as shown below.
DENTRIa: CT Mode + PANORAMA Mode
DENTRI-Ca: CT Mode + PANORAMA Mode + CEPHALO Mode (ONE-SHOT)
DENTRI-Sa: CT Mode + PANORAMA Mode + CEPHALO Mode (SCAN)

This document is a 510(k) summary for the DENTRIα series of Computed Tomography (CT) X-Ray imaging devices. It primarily focuses on demonstrating substantial equivalence to predicate devices rather than providing a detailed study of acceptance criteria and device performance in the context of a clinical trial for a new AI/CAD device. Therefore, much of the requested information regarding acceptance criteria, study design, ground truth establishment, sample sizes, and expert adjudication for an AI device is not explicitly present in the provided text.

However, I can extract the information that is available and indicate where the requested details are not provided due to the nature of this type of submission (a 510(k) for a medical imaging device, not an AI/CAD system).

Here's an attempt to answer based on the provided document:

1. Table of acceptance criteria and the reported device performance

The document does not specify formal "acceptance criteria" for diagnostic performance in the way one would for an AI/CAD system (e.g., sensitivity, specificity thresholds). Instead, it focuses on comparative performance with predicate devices for technical characteristics and demonstrating diagnostic quality through non-clinical and limited clinical data.

The "reported device performance" is primarily presented as technical specifications and compliance with standards, and a general statement about diagnostic quality.

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

The document mentions "clinical images of patients are presented as the clinical data including date and signature by a licensed professional." However, it does not specify the sample size (number of patients/cases) for this clinical data, nor does it explicitly state the data provenance (e.g., country of origin, retrospective or prospective nature of this specific clinical image set). Since it's a Korean manufacturer, the images likely originated from Korea, but this is not explicitly stated. The context implies it was likely a retrospective collection given the wording "clinical images...are presented."

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

The document states: "The report concluded that the images taken with the subject device are of good diagnostic quality." It also mentions "clinical data including date and signature by a licensed professional."

• Number of experts: Not specified. It only mentions "a licensed professional" in the singular, but a formal clinical study usually involves multiple experts.
• Qualifications of those experts: "licensed professional" – no specific qualifications (e.g., radiologist, years of experience) are provided.

4. Adjudication method for the test set

Not specified. Given the lack of detail on the number of experts and how ground truth was established, no adjudication method is mentioned.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• MRMC study: No, an MRMC comparative effectiveness study was not conducted or reported in this 510(k) summary. This submission is for a conventional medical imaging device, not an AI/CAD system necessitating such a study design.
• Effect size of human readers with/without AI assistance: Not applicable, as this is not an AI/CAD device.

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

Not applicable. This is a medical imaging device (hardware and software for image acquisition and reconstruction), not a standalone algorithm. The device produces images for human interpretation.

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

The ground truth for the "clinical data" is implied to be a subjective assessment by a "licensed professional" that the images are of "good diagnostic quality." This is a general statement rather than a rigorous, objective ground truth established by expert consensus, pathology, or outcomes data typically used for evaluating AI/CAD diagnostic performance.

8. The sample size for the training set

Not applicable. This is a conventional CT X-ray system, not an AI/CAD system, so there is no "training set" in the context of machine learning. The device uses established image reconstruction algorithms (FBP is mentioned).

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

Not applicable, as there is no training set for an AI/CAD system mentioned.

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