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The X-VIEW, IMAGEN, a panoramic x-ray imaging system with cephalometric capabilities, is an extraoral source x-ray system intended for dental radiographic examination of the teeth, jaw, and oral structures, specifically for panoramic examinations and implantology and for TMI studies and cephalometry. It has the capability of using cone beam volumetric technology techniques to generate dento-maxillofacial 3D images. The device uses a cone shaped x-ray beam projected onto a flat panel detector, and the examined volume image is reconstructed to be viewed via 3D viewing stations. 2D Images are obtained using the standard narrow beam technique. The device is to be operated and used by dentists, radiologists and other legally qualified health care professionals.

X-VIEW, IMAGEN is a 3-dimensional CBCT system that allows the execution of all commonly used x-rays in both dental and orthodontic fields (excluding intraoral radiographs) and also allows the acquisition of tomographic radiographs, or volumetric 3D. It uses CBCT (Cone Beam Computed Tomography) with a flat panel detector to provide high-definition volumetric images.

Here's an analysis of the provided text regarding the acceptance criteria and study information for the X-VIEW, IMAGEN device:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly define acceptance criteria in a quantitative manner (e.g., minimum sensitivity, specificity, or resolution thresholds for clinical performance). Instead, the acceptance is based on demonstrating substantial equivalence to predicate devices through technical specifications and adherence to safety and performance standards.

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

• Sample Size for Test Set: The document mentions that a "Clinical Evaluation report in which volumetric reconstructive images of a human subject were acquired." This indicates a very limited sample size of one human subject for the clinical evaluation component and is explicitly stated as one human subject.
• Data Provenance: The document does not specify the country of origin of the data. It implies a prospective test set since it describes images being "acquired."

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

The document does not provide information on the number of experts used to establish ground truth or their specific qualifications (e.g., radiologist with X years of experience). The clinical evaluation report simply states that "all test results were satisfactory," implying an internal assessment rather than an independent expert review with explicit ground truth labeling.

4. Adjudication method for the test set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1, none) for the test set.

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

There is no indication that a multi-reader multi-case (MRMC) comparative effectiveness study was done, nor is there any mention of human readers improving with or without AI assistance. The device is an imaging system, and the evaluation focuses on its technical performance and equivalence to predicates, not on the impact of AI on human reader performance.

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

This question is not directly applicable in the typical sense of AI algorithms. The "device" here is a hardware imaging system (X-VIEW, IMAGEN) with associated imaging processing software (OnDemand3D). The evaluation is of the imaging system's ability to acquire and reconstruct images, not of a standalone AI algorithm to interpret images. The performance metrics (MTF, DQE, Dynamic Range) relate to the detector's standalone technical performance as an imaging component.

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

The document does not explicitly state the type of ground truth used for the clinical evaluation. Given the nature of a CBCT system, the "satisfactory" test results for images of a human subject likely imply that the images were deemed diagnostically acceptable and of sufficient quality for their intended use by the evaluating party, possibly against known anatomical features or expected image characteristics, but it doesn't specify a formal ground truth like pathology or expert consensus on clinical findings.

8. The sample size for the training set

No training set is mentioned or implied for the evaluation of this device. The X-VIEW, IMAGEN is an imaging acquisition system. Its evaluation focused on hardware specifications, compliance with standards, and a limited clinical evaluation, not on the performance of a machine learning model that would require a training set.

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

Not applicable, as no training set is mentioned.

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The EDLENi sensor is an intra-oral x-ray sensor driven via CMOS technology indicated for the acquirement of intra-oral dental radiographs and intended for dental patients. The EDLENi will be handled via qualified dental and healthcare professionals in order to perform basic dental intra-oral x-ray imaging procedures. The sensors will be used to provide intra-oral radiographic images that ultimately diagnose general dental complications or abnormalities. The EDLENI sensor can be used in combination with positioning devices to facilitate alignment with the x-ray beam, or it may be positioned manually.

The EDLENi sensor is an electronic medical device used to acquire intra-oral radiographic images. The sensor can be operated by Radiologists, Dentists, Dental Assistants and other healthcare professionals, who are both trained and competent to take Dental X-ray radiographs. Intra-oral positioning of the sensor is accomplished by the use of dedicated intra-oral positioning devices that facilitate the accurate alignment of the x-ray beam. The sensor may also be aligned with the assistance of the patient. The EDLENi sensor is an indirect light converting digital x-ray detector. A scintillating device composed of Cesium Iodide (CsI) converts incident x-rays into visible light that is optically coupled to a light detection imager based on CMOS technology. The EDLENi allows for automatic detection of such incident x-rays in order to generate data. Software interprets this data into images used for dental applications. The EDLENi sensor supports USB 2.0 direct connectivity to personal computers and or laptops with dedicated electronics and a sensor software driver.

The provided document is a 510(k) Summary for the EDLENi Intra-Oral Sensor. It focuses on demonstrating substantial equivalence to predicate devices, rather than presenting a study proving the device meets specific acceptance criteria based on diagnostic performance. Therefore, most of the requested information regarding acceptance criteria, study details, expert involvement, and ground truth types is not available in this document.

Here's an analysis of what can be extracted and what is not present:

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

• Acceptance Criteria: Not explicitly stated in terms of diagnostic performance metrics (e.g., sensitivity, specificity, accuracy for specific clinical conditions). The primary acceptance criterion seems to be demonstrating substantial equivalence to predicate devices, particularly regarding safety and effectiveness, and compliance with relevant standards.
• Reported Device Performance: The document states, "Performance testing data according to the Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices have been provided to demonstrate substantial equivalence." However, the specific performance data itself (e.g., image quality metrics like spatial resolution, contrast resolution, DQE, noise) is not detailed in this summary.

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

• Sample Size: Not specified.
• Data Provenance: Not specified.
• The document explicitly states: "Clinical images were not provided; clinical images were not necessary to establish substantial equivalence based on the modifications of the device." This indicates that if performance testing involved image acquisition, it likely did so under phantom or laboratory conditions, not 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. Since clinical images were not used, and the goal was substantial equivalence based on technical modifications rather than diagnostic performance, there was no need for expert ground truth establishment in a diagnostic context.

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

• Not applicable. No clinical test set requiring adjudication was used.

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

• No, an MRMC study was not done. This document pertains to an intra-oral x-ray sensor, which is a hardware device for image acquisition, not an Artificial Intelligence (AI) or CAD software for diagnosis. Therefore, AI assistance is not relevant to this submission.

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

• No. This device is a sensor, not a standalone diagnostic algorithm. Its performance is related to image acquisition quality, which is then interpreted by human professionals.

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

• Not applicable for diagnostic ground truth. The "ground truth" for the performance testing performed would likely revolve around objective technical measurements (e.g., measuring line pairs per millimeter for spatial resolution, signal-to-noise ratios, etc.) against expected or predicate device performance. This is not a clinical ground truth.

8. The sample size for the training set

• Not applicable. This device is a sensor and does not utilize a training set in the context of machine learning or AI.

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

• Not applicable.

Summary of available information:

• Device: EDLENi Intra-Oral Sensor
• Purpose of Submission: Demonstrate substantial equivalence to predicate devices for acquiring intra-oral dental radiographs.
• Key Argument for Substantial Equivalence: The only modifications compared to the predicate are the use of two new Hamamatsu CMOS sensors (instead of CCD) for readout, while maintaining the same detection material (Cesium Iodide).
• Performance Testing: "Performance testing data according to the Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices have been provided to demonstrate substantial equivalence." (Specific metrics are not detailed in this summary).
• Clinical Data: "Clinical images were not provided; clinical images were not necessary to establish substantial equivalence based on the modifications of the device."
• Safety and Effectiveness: Confirmed through compliance with IEC 60601-1-2 (Electromagnetic Compatibility), software design, verification, validation testing, and risk assessment.

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Edlen Imaging's Gemini DUSB sensor is a USB-driven digital X-ray sensor that acquires dental intra-oral X-ray images. The sensor will be operated by trained dental and related healthcare professionals to acquire dental intra-oral radiographs.

The Gemini sensor can be used either in combination with special positioning devices, to facilitate alignment with the x-ray beam, or it may also be positioned manually.

The Gemini sensor is an indirect light converting digital X-ray detector. Incident X-rays are converted to visible light by a scintillating device, (material) such as Csl (Cesium lodide), the light is coupled optically to a light detection imager which is based on CMOS technology.

The design of the sensor allows for automatic detection of incident X-rays to generate a digital data which when coupled with a software program will display images used for dental intra-oral applications.

The Gemini Digital x-ray sensor supports USB 2.0 Direct connectivity to personal computers and or laptops, hence the name Gemini DUSB.

The Gemini sensors employ built-in dedicated electronics and sensor software driver, to allow connectivity to a variety of software packages. The Gemini sensors are coupled with the currently marketed software, Apteryx XrayVision, K983111.

The provided text describes a 510(k) premarket notification for the Edlen Imaging Gemini DUSB Digital X-ray sensor, which demonstrates substantial equivalence to predicate devices rather than providing a detailed clinical study with acceptance criteria and specific performance metrics. Therefore, several requested categories cannot be directly extracted or are not applicable from the provided document.

Here's a breakdown of what can be inferred and what is not available:

1. Table of Acceptance Criteria and Reported Device Performance

Not available. The submission focuses on establishing substantial equivalence to legally marketed predicate devices (Schick Technologies CDR and Suni Medical Imaging Suni Ray II) based on shared indications for use, materials, design, and operational/functional features. It states the device is "as safe, as effective, and performs as well as or better than the predicate devices," but does not provide specific acceptance criteria or quantitative performance results in the format requested.

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

Not applicable. This was a 510(k) submission for substantial equivalence based on technical characteristics and safety standards, not a clinical study with a test set of patient data.

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

Not applicable, as no clinical test set requiring ground truth establishment is described.

4. Adjudication Method for the Test Set

Not applicable, as no clinical test set requiring adjudication is described.

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

Not applicable. The document does not describe an MRMC study comparing human readers with and without AI assistance. The Gemini DUSB is a digital X-ray sensor, not an AI or CAD system designed to assist human readers.

6. Standalone Performance Study (Algorithm Only)

Not applicable. The device is a digital X-ray sensor, which is a hardware component for image acquisition, not an algorithm with standalone performance.

7. Type of Ground Truth Used

Not applicable, as no clinical test set requiring ground truth is described.

8. Sample Size for the Training Set

Not applicable, as the device is a digital X-ray sensor, not an AI or machine learning algorithm that requires a training set in this context.

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

Not applicable, as there is no training set for an AI/machine learning algorithm described.

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