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The Panoura X-ERA PF/NF/MF dental panoramic and cephalometric device is intended for dental radiographic examinations of teeth, jaw and TMJ areas by producing conventional 2D X-ray images as well as X-ray projection images of examined volume for the reconstruction of 3D view. The Panoura X-ERA PF/NF/MF dental panoramic and cephalometric device is intended for general populations. The device must only be operated and used by dentists and other legally qualified professionals.

The Panoura X-ERA PF/NF/MF dental panoramic and cephalometric device is modified from the IMAGEWORKS PANOURA (K122806).

The Panoura X-ERA PF/NF/MF dental panoramic and cephalometric device is intended for dental radiographic examinations of teeth, jaw and TMJ areas by producing conventional 2D X-ray images as well as X-ray projection images of examined volume for the reconstruction of 3D view. The device must only be operated and used by dentists and other legally qualified professionals.

Panoura X-ERA PF/NF/MF consists of a scanner, which is used for generating X-ray and detecting image data, and a Console, which is used for operating the scanner and managing the data. The scan data acquired by the scanner will be transferred to the Console. The Console is new software and classified as moderate level concern. 3D Viewer will then perform the image analysis or image edition such as creating cross-section diagram.

3D sensor is not installed for Panoura X-ERA PF. Medium FOV sensor is installed for Panoura X-ERA NF. Large FOV sensor is installed for Panoura X-ERA MF.

The provided text is a 510(k) summary for the Panoura X-ERA PF/NF/MF dental panoramic and cephalometric device. It focuses on demonstrating substantial equivalence to a predicate device (IMAGEWORKS PANOURA, K122806).

Crucially, this document does not contain information about acceptance criteria for an AI-powered device, nor does it describe a study specifically proving an AI device meets such criteria.

The "Panoura X-ERA PF/NF/MF" is an extraoral source x-ray system, a hardware device for capturing dental images. While it "consists of a scanner... and a Console, which is used for operating the scanner and managing the data. The Console is new software and classified as moderate level concern. 3D Viewer will then perform the image analysis or image edition such as creating cross-section diagram," this software appears to be purely for imaging acquisition, management, and basic viewing/editing, not for AI-driven diagnostic or analytical capabilities. The key changes mentioned are software and the addition of a 3D sensor.

The "studies" mentioned are "Bench Testing" which compared test pattern images and confirmed conformity with various IEC standards (electrical safety, radiation safety, laser safety). Software validation was done according to "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices," which is a general guideline for medical device software, not specific to AI performance.

Therefore, I cannot fulfill your request to describe the acceptance criteria and the study that proves an AI device meets the acceptance criteria, as this document describes a traditional imaging hardware device with associated operational software, not an AI product.

If you have a document specific to an AI medical device, I would be happy to analyze it for the requested information.

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PREXION3D ECLIPSE is intended to produce two-dimensional digital panoramic and cephalometric images, and three-dimensional digital X-ray images of the dento-maxillo-facial region at the direction of healthcare professionals as diagnostic support.

The PREXION3D ECLIPSE consists of scanner and two software including Console software, and Viewer software used for the Image Analysis System and Data processing. A qualified computer named Console computer is distributed with the PREXION3D ECLIPSE.

The PREXION3D ECLIPSE uses the Image Analysis System and the processed data acquired by the scanner to analyze 2D and 3D images, perform image edition, such as creating cross-section views, and output results to a printer or other output device.

During scanning, X-rays are generated from the X-ray tube head mounted in the arm of the scanner and the X-rays passing through a patient are then detected by the flat panel detector of the scanner under the control of the firmware and the Console software installed on the qualified computer. The detected X-ray absorption data is processed by the Console software and viewer software on each computer to reconstruct images. Scanning is performed using X-ray penetration signals of a patient taken from multiple directions to produce two-dimensional and three-dimensional digital X-ray images of the dento-maxillo-facial region for the diagnosis of hard tissue

The PREXION3D ECLIPSE performs three types of scanning including CT scan generating two (2) or three (3) dimensional images, Panoramic scan generating two (2) dimensional images and Cephalometric radiography generating a plain radiographic image.

Here's an analysis of the provided text regarding acceptance criteria and supporting studies:

This submission is for a medical device (PREXION3D ECLIPSE), which is an X-ray system. The information provided heavily focuses on demonstrating substantial equivalence to predicate devices through technical specifications and compliance with recognized standards, rather than clinical performance studies with specific patient outcomes.

Therefore, the answers to some of your questions may indicate that the information is "Not applicable," "Not specified," or "Not performed" based on the nature of this type of regulatory submission (510(k) for substantial equivalence).

Acceptance Criteria and Device Performance

The document does not explicitly state "acceptance criteria" in the typical sense of performance metrics (sensitivity, specificity, accuracy) for a diagnostic algorithm with a defined ground truth. Instead, the "acceptance criteria" are implied by the demonstration that the device's characteristics and performance are substantially equivalent to existing legally marketed predicate devices and that it meets recognized consensus or voluntary standards relating to safety and effectiveness of X-ray systems.

The studies performed are primarily bench tests and verifications of safety and technical specifications, aiming to show that the new device performs at least as well as, or comparably to, its predicates in these areas.

1. Table of Acceptance Criteria and Reported Device Performance

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 explicitly specified in terms of patient cases or images for any of the bench tests mentioned. The "images of all scan mode" were compared for artifact analysis, implying a limited set of test images rather than a large patient cohort.
• Data Provenance: Not specified. The tests are described as "bench tests," suggesting they likely involved phantom or test objects rather than human patient data.

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

• Number of Experts: Not applicable/Not specified. The evaluation criteria are based on objective technical standards (IEC standards, MTF measurements, direct comparison of artifact patterns) rather than expert consensus on diagnostic accuracy for a "ground truth" derived from patient cases.
• Qualifications of Experts: Not applicable/Not specified.

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

• Adjudication Method: Not applicable. The evaluations are technical and objective (e.g., measuring MTF, comparing artifact patterns), not subjective diagnostic interpretations requiring adjudication.

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. This type of study is not mentioned or implied. The device is an X-ray imaging system, not an AI-powered diagnostic aide.
• Effect Size of AI assistance: Not applicable, as this is not an AI-assisted diagnostic device.

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

• Standalone Performance: Not applicable. The device is an imaging system; its performance is evaluated based on its imaging capabilities (resolution, artifact, safety compliance) rather than an algorithm's diagnostic output.

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

• Type of Ground Truth: For the "bench testing" described, the "ground truth" is intrinsically tied to the recognized standards and objective physical measurements. For example:
  • For Laser Safety, the ground truth is compliance with IEC 60825-1.
  • For Spatial Resolution, the ground truth is the derived MTF values meeting specified criteria according to IEC 61223-3-5.
  • For Artifact Analysis, the ground truth is the visual comparability to the images from the predicate device (FINECUBE, K063622).
  • For Software Validation, the ground truth is adherence to FDA guidance documents.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable/Not specified. This is an X-ray hardware and software system, not a machine learning model that requires a "training set" in the conventional sense. Software validation would involve testing against requirements, but not a "training set."

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

• Ground Truth for Training Set: Not applicable. As there's no mention of a machine learning training set, this question is not relevant to the provided documentation.

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PreXion 3D is intended to produce two-dimensional and three-dimensional digital X-ray images of the dento-maxillo-facial regions at the direction of healthcare professionals as diagnostic support.

The PREXION 3D consists of scanner and two software including Console software, and Viewer software used for the Image Analysis System and Data processing. A qualified computer named Console computer is distributed with the PREXION 3D.

The PREXION 3D uses the Image Analysis System and the processed data acquired by the scanner to analyze 2D and 3D images, perform image edition, such as creating cross-section views, and output results to a printer or other output device.

During scanning, X-rays are generated from the X-ray tube head mounted in the arm of the scanner and the X-rays passing through a patient are then detected by the flat panel detector of the scanner under the control of the firmware and the Console software installed on the qualified computer. The detected X-ray absorption data is processed by the Console software and viewer software on a computer to reconstruct images. Scanning is performed using X-ray penetration signals of a patient taken from multiple directions for the diagnosis of hard tissue including bones and teeth of the jaw and mouth cavity. The PREXION 3D performs two types of scanning including CT scan generating two (2) and three (3) dimensional images and Panoramic scan generating two (2) dimensional images.

Here's a summary of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Device Name: PREXION 3D
K Number: K120948

1. Table of Acceptance Criteria and Reported Device Performance

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

The provided summary does not specify a sample size for a test set in the context of clinical or image-based studies with human subjects. The testing described focuses on technical performance metrics (MTF, artifact analysis) and safety standards, likely using phantoms or laboratory setups rather than patient data for "test sets" in the diagnostic performance sense.

Data Provenance: Not applicable in the context of the described technical bench testing.

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

Not applicable. The ground truth for the technical tests (like MTF and artifact analysis) is established by measurement against defined physical standards or comparison to a predicate device's known performance, not by expert interpretation.

4. Adjudication Method for the Test Set

Not applicable, as the tests were technical and did not involve human interpretation or adjudication.

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. The submission focuses on technical substantial equivalence, not comparative diagnostic effectiveness with human readers.

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

No, a standalone algorithm performance study was not explicitly described. The device, an X-ray system, produces images for human healthcare professionals to interpret; it is not presented as an AI-driven diagnostic algorithm. The software validation mentioned pertains to the software's functional correctness for image processing and system control.

7. The Type of Ground Truth Used

• For Laser Safety: Compliance with IEC 60825-1 (international standard).
• For Modulation-Transfer Function (Spatial Resolution): Measurements in accordance with IEC 61223-3-5 (international standard).
• For Artifact Analysis: Comparison to the predicate device (FineCube K063622). The "ground truth" here is the established and accepted performance of the predicate device.
• For Software Verification/Validation: Compliance with FDA guidance for software in medical devices.
• For EMC, Electrical Safety, X-ray Radiation Safety: Compliance with relevant IEC 60601 series standards.
• For Risk Analysis: Compliance with ISO 14971:2007.

8. The Sample Size for the Training Set

Not applicable. This device is an imaging system, not an AI/ML algorithm that requires a training set of data. The software validation is for the image acquisition and processing functions, which are deterministic, not for a learning algorithm.

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

Not applicable, as there is no "training set" for this type of device.

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The Opelaser Family is CO2 Medical Laser Systems (and the delivery system that are used to deliver laser energy) are indicated for use in surgical applications requiring the ablation, vaporization, excision, incision, and coagulation of soft tissue in medical specialties including: aesthetic (dermatology and plastic surgery), podiatry, otolaryngology (ENT), gynecology (including laparoscopy), neurosurgery, orthopedics (soft tissue), arthroscopy (knee), general and thoracic surgery (including open and endoscopic), dental and oral surgery and genitourinary surgery.

These are CO2 Laser devices designed for surgical applications. The maximum power output for the PRO model is 7 watts, and for the LITE model, 5 watts. Type of laser is a CO2 gas laser (class 4); Oscillation system: RF discharge excitation oscillation; Light guiding system: SiO2 hollow fiber or 6 point articulated arm depending on model; Oscillation wavelength: 10.6um.

The provided text is a 510(k) summary for the OPELASER PRO II and OPELASER Lite II surgical laser devices. It outlines the device's identification, description, indications for use, and a comparison to predicate devices. Crucially, it primarily discusses the regulatory process of demonstrating "substantial equivalence" to already legally marketed devices, rather than a clinical study establishing specific performance metrics against pre-defined acceptance criteria for a novel AI/software component.

Therefore, many of the requested details about acceptance criteria, clinical study specifics, ground truth, and expert involvement are not present in the provided document, as this type of information is typically associated with studies demonstrating the performance of a diagnostic or algorithmic device, especially in the context of AI. This document focuses on the safety and effectiveness of a physical surgical laser, not an AI or software-based diagnostic tool.

Here's an attempt to answer the questions based only on the provided text, indicating where information is not available:

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

Note: The document focuses on regulatory compliance and substantial equivalence rather than specific performance metrics (e.g., sensitivity, specificity, accuracy) typically associated with AI or diagnostic device studies.

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 for Test Set: Not applicable/Not provided. The submission describes non-clinical safety, bench, and software validation tests directly on the device, not a test set of data.
• Data Provenance: Not applicable/Not provided.

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/Not provided. This type of expert involvement would be relevant for a diagnostic AI study, but the provided text concerns a physical surgical laser.

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

Not applicable/Not provided.

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. This document does not mention an MRMC study or any AI component designed to assist human readers.

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

Not applicable. The device is a surgical laser, not a standalone algorithm.

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

Not applicable/Not provided. The "truth" in this context is the device's ability to meet safety standards and perform its intended surgical functions as demonstrated through engineering tests and comparison to predicates.

8. The sample size for the training set

Not applicable/Not provided. There is no mention of a training set as this is not an AI/machine learning submission.

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

Not applicable/Not provided.

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The PANOURA 18S dental panoramic and cephalometric device is intended for dental radiographic examinations of teeth, jaw and TMJ areas by producing conventional 2D X-ray images as well as X-ray projection images. The device must only be operated and used by dentists and other legally qualified professionals.

The PANOURA 18S dental panoramic and cephalometric device is intended for dental radiographic examinations of teeth, jaw and TMJ areas by producing conventional 2D X-ray images as well as X-ray projection images. The PANOURA 18S is equipped with an X-ray generator and a Sensor unit at Arm unit supported by Column unit and Sliding body unit. While rotating around the patient's teeth and jaw, the PANOURA 18S irradiates X-ray and detects X-ray absorbed data at the Sensor unit multiple times. Detected multiple data are transferred to an image processing unit and the data are superimposed with appropriate shift value according to the X-ray moving speed from the arm rotation to acquire image.

The Panoura 18S is an extraoral x-ray system, which is a medical device and not an AI/ML algorithm. The provided documentation does not contain information about acceptance criteria or a study proving the device meets those criteria in the context of AI/ML performance. Instead, the document focuses on demonstrating substantial equivalence to a predicate device (ORTHOPANTOMOGRAPH K093683) by comparing technical, operational, and intended use characteristics, and compliance with recognized consensus or voluntary standards for medical electrical equipment and radiation protection.

Therefore, I cannot provide the requested information regarding AI/ML acceptance criteria and performance studies. The provided text describes a traditional medical device submission, not an AI/ML-driven one.

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FineCube is an x-ray device (cone beam computed tomography) that acquires a single 360 degree rotational sequence of the head and neck areas, including the ENT and dentomaxillofacial area for use in diagnostic support. The device is operated and used by physicians, dentists, and x-ray technologists.

The FineCube consists of two main elements: (1) The X-Ray generation and acquisition system and (2) the image analysis software. The X-Ray generation/acquisition system creates a cone-shaped x-ray field which is acquired on a flat panel sensor with a resolution of 608 x 616 x 200 um. FineCube is an X-ray imaging device that constructs a three dimensional model from images taken during a rotational X-ray sequence. FineCube is intended to be used whenever a dentist, oral surgeon, or other physician needs 3D information of high contrast objects

The provided 510(k) summary for the FineCube Dental CT System (K063622) does not contain detailed information about acceptance criteria and specific studies demonstrating the device meets those criteria. Instead, it focuses on establishing substantial equivalence to predicate devices based on indications for use and specification comparisons.

Here's a breakdown of the available information and what is missing based on your request:

1. Table of Acceptance Criteria and Reported Device Performance

Not available in the provided document. The 510(k) summary only provides a table comparing the specifications of the FineCube with two predicate devices. It doesn't list defined "acceptance criteria" for performance metrics like image quality, diagnostic accuracy, or specific measurements, nor does it present the FineCube's performance against such criteria. The document states "The results of bench, test laboratory and clinical testing indicates that the new device is as safe and effective as the predicate devices," but no specific performance data or statistical analysis is provided to support this claim against defined criteria.

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

Not explicitly stated in the document. The document mentions "bench, test laboratory and clinical testing," but does not provide details on the sample sizes used for these tests or the provenance (country of origin, retrospective/prospective) of any clinical data.

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

Not available in the provided document. There is no information regarding the establishment of ground truth by experts for any testing conducted.

4. Adjudication Method for the Test Set

Not available in the provided document.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. The FineCube is an imaging device (cone beam computed tomography) and not an AI-powered diagnostic algorithm designed to assist human readers. Therefore, an MRMC comparative effectiveness study involving AI assistance would not be relevant to this device's submission.

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

Not applicable. This is not an AI algorithm. It is a medical imaging device.

7. The Type of Ground Truth Used

Not available in the provided document.

8. The Sample Size for the Training Set

Not applicable. As a medical imaging device, the concept of a "training set" in the context of machine learning algorithms is not directly relevant. The device undergoes engineering and performance validation rather than algorithmic training.

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

Not applicable, as there is no "training set" in the machine learning sense for this device.

Summary of what is available and what is missing:

The 510(k) summary for K063622 primarily relies on demonstrating substantial equivalence to predicate devices through a comparison of their indications for use and technical specifications. While it generally asserts that safety and effectiveness were demonstrated through "bench, test laboratory and clinical testing," it lacks the specific details requested regarding acceptance criteria, study methodologies, sample sizes, ground truth establishment, expert involvement, or any data related to AI performance metrics. This level of detail is often not included in the public 510(k) summary and would typically be found in the complete 510(k) submission that is reviewed by the FDA.

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