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The Portable X-ray System is used as a portable, extra oral x-ray source for producing diagnostic x-ray images using conventional film, PSP (phosphor plates) or digital sensors. It is intended for adult and pediatric patients. This equipment is available only to trained and qualified dentist or dental technician.

The portable X-Ray system, Canis014D07, is intended to be used by trained dentists and dental technicians as an extra-oral x-ray source for producing diagnostic x-ray images using intraoral image receptors. The images presented in the product screen are for preview purposes only, not for imaging diagnosis. It also includes accessories, which are battery, recharging unit and hand switch.

The provided text describes a 510(k) premarket notification for a Portable X-ray System (Canis014D07). It outlines the device's indications for use, its technological characteristics compared to predicate and reference devices, and a summary of non-clinical testing conducted to demonstrate safety and effectiveness.

However, the document does not contain information related to specific acceptance criteria for diagnostic performance, a study evaluating device performance against such criteria, sample sizes for test or training sets, data provenance, number or qualifications of experts for ground truth, adjudication methods, MRMC studies, or standalone algorithm performance.

The "Summary of Performance Testing" section states that the performance test for the device and predicate are "identical in the indications for use, patient population, intended operation environment, and electrical safety." It also mentions "Validation was performed for overall operation by taking and reviewing test images" and that "the test images were reviewed by a professional with adequate qualifications, and that the images were of diagnostic quality." This suggests a qualitative assessment of image quality rather than a quantitative study with pre-defined diagnostic performance metrics.

Therefore, I cannot fulfill the request to provide a table of acceptance criteria and reported device performance, or details about the study that proves acceptance criteria are met, as this information is not present in the provided text.

Based on the available text, here's what can be extracted with respect to your request, noting the missing information:

Acceptance Criteria and Study Details (Based on Available Information)

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not specified. The document mentions "test images were reviewed," but the number of images is not provided.
• Data Provenance: Not specified.

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

• Number of Experts: "a professional" (singular)
• Qualifications of Experts: "a professional with adequate qualifications" (No specific details like "radiologist with 10 years of experience" are provided).

4. Adjudication method for the test set:

• Adjudication Method: Not specified. It appears a single professional reviewed the images for diagnostic quality.

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 study was not described. The device is an X-ray system, not an AI algorithm for image interpretation.

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

• Standalone Performance: Not applicable. This device is an X-ray imaging system, not an AI algorithm. Its performance refers to its ability to produce diagnostic images, not to interpret them. The document explicitly states, "The images presented in the product screen are for preview purposes only, not for imaging diagnosis."

7. The type of ground truth used:

• Type of Ground Truth: Expert opinion on "diagnostic quality" of the images produced by the device.

8. The sample size for the training set:

• Sample Size for Training Set: Not applicable/not specified. The device is a hardware X-ray system, not an AI algorithm that requires a training set.

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

• Ground Truth for Training Set: Not applicable.

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Mercu1717V Digital Flat Panel Detector is indicated for digital imaging solutions designed to provide general radiographic diagnosis for human anatomy including both adult and pediatric patients. It is intended to replace film/screen systems in all general-purpose diagnostic procedures. The device is not intended for mammography or dental applications.

Mercu1717V Digital Flat Panel Detectors (Hereinafter referred to as Mercu1717V) supports dynamic imaging and static imaging. The sensor plate of Mercu1717V is direct-deposited with CsI scintillator to achieve the conversion from X-ray to visible photon. The visible photons are transformed to electron signals by diode capacitor array within TFT panel, which are composed and processed by connecting to scanning and readout electronics, consequently to form a panel image by transmitting to PC through the cable. The major function of the Mercu1717V is to convert the X-ray to digital image, with the application of high-resolution X-ray imaging. Mercu1717V can get single image and it also can get dynamic image. Both kinds of detectors are the key component of DR system, enable to complete the digitalization of the medical X-ray imaging with the DR system software. The iRay DR used for digital X-ray radiography image from the digital flat panel detectors. iRay DR is used to handle the DICOM protocol (DICOM 3.0). iRay DR has many functions such as image acquisition, image enhancement processing and editing image or information.

The provided document does not contain detailed information about specific acceptance criteria and a study proving the device meets them in the context of clinical performance or human reader studies. Instead, it focuses on the device's technical specifications and substantial equivalence to predicate devices based on non-clinical testing.

Here's an analysis based on the available information, noting where specific details are missing:

1. Table of Acceptance Criteria and Reported Device Performance (Based on provided technical specifications for comparison with predicate devices):

The document primarily compares various technical specifications of the Mercu1717V with its predicate and reference devices, aiming to demonstrate substantial equivalence rather than explicit acceptance criteria and performance against those criteria as would be typical for clinical effectiveness. However, we can extract some performance metrics from the comparison table.

Missing Information:
The document does not explicitly state acceptance criteria in terms of clinical performance or diagnostic accuracy. Instead, it demonstrates an equivalence to predicate devices through technical specifications and compliance with safety standards. The "study that proves the device meets the acceptance criteria" refers to non-clinical testing performed to show substantial equivalence.

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

• Sample Size: Not applicable in the context of clinical images or patient data tests for this submission. The "test set" here refers to the actual device undergoing non-clinical technical evaluations (e.g., electrical safety, EMC, image quality parameters like MTF, DQE).
• Data Provenance: Not applicable. The evaluations are technical measurements of the physical device under specific lab conditions.

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

• Not applicable. This device is a digital flat panel detector, a hardware component for acquiring X-ray images. The submission focuses on its physical characteristics, safety, and image quality parameters, not on the interpretation of images by experts. Ground truth in this context would relate to the objective measurement of physical properties.

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

• Not applicable. No expert adjudication of diagnostic outcomes is mentioned or implied, as this is a device component clearance and not a diagnostic AI software submission.

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 comparative effectiveness study was done as this submission pertains to a digital X-ray detector, not an AI-powered diagnostic tool requiring human-in-the-loop performance evaluation.

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

• No standalone algorithm performance study was done for diagnostic purposes. The device is a hardware component. Its "standalone" performance relates to its physical performance metrics (e.g., DQE, MTF) as measured in a lab setting.

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

• For the non-clinical studies (e.g., MTF, DQE, electrical safety), the "ground truth" would be established by standardized measurement protocols and reference instruments, as per relevant IEC or other industry standards. It's objective, physical measurements rather than clinical ground truth from patient data.

8. The sample size for the training set:

• Not applicable. This document is for a hardware device (digital flat panel detector) and its associated software (iRayDR), which is described as image acquisition, processing, and archiving software, not a machine learning model that requires a training set of medical images.

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

• Not applicable, as there is no mention of a machine learning model or a training set. The software mentioned (iRayDR) performs image acquisition and post-processing, typical for a radiological workstation, not AI-driven diagnosis.

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Venul 717X is indicated for digital imaging solutions designed to provide general radiographic diagnosis for human anatomy including both adult and pediatric patients. It is intended to replace film/screen systems in general-purpose diagnostic procedures.

Venu1717X is a cassette-size tethered X-ray flat panel detector based on amorphous silicon thin-film transistor technology. It is designed to provide the high quality radiographic image which contains an active matrix of 3070×3070 with 139um pixel pitch. The scintillator of Venu1717X is CsI(Caesium Iodide). The technology of CsI direct growth reduces the exposure dose and improves the image quality. Since Venu1717X supports multiple trigger modes, it can satisfy both of the general DR system and retrofit DR system.

iRay SDK(include iDetector) is intend to supply API interface for DR system manufacturers.DR system manufacturer control the detector by SDK interface. SDK is not intended to be used directly by other users beside DR system manufacturers. The iRay SDK is unchanged from the predicate device.

The information provided indicates that the iRay Technology Taicang Ltd. Flat Panel Detector (Venu1717X) is a digital imaging solution for general radiographic diagnosis. While the provided text describes the device's technical specifications and non-clinical studies to establish substantial equivalence to a predicate device (Mars1717V-VSI, K201043), it does not contain details about specific acceptance criteria for diagnostic accuracy metrics (like sensitivity or specificity) for a clinical study.

Instead, the provided text focuses on demonstrating substantial equivalence primarily through technical performance characteristics and a "concurrence study" of clinical images.

Here's an attempt to answer your request based only on the provided text, highlighting what is available and what is missing:

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

The document does not explicitly state "acceptance criteria" in terms of diagnostic performance metrics for a clinical study (e.g., sensitivity, specificity, or AUC with target thresholds). It focuses on demonstrating equivalence through technical 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 for Test Set: 30 clinical images.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective). The document only states "Clinical images were 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)

The document mentions a "concurrence study" but does not specify the number of experts, their qualifications, or how ground truth was established for the 30 clinical images. The statement "There was no significant difference between the images" implies a qualitative comparison by human readers, but details are missing.

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

The document does not specify any adjudication method for the "concurrence study" of the 30 clinical images.

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly described. The study mentioned is a "concurrence study of 30 clinical images" comparing the proposed device to a predicate device, which is different from an MRMC study designed to assess reader improvement with AI assistance. The device itself is a Flat Panel Detector, which is hardware for image acquisition, not inherently an AI-driven diagnostic assistance tool.

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

This section is not applicable as the device is a Flat Panel Detector, a hardware component for imaging, not an AI algorithm performing diagnostic tasks in a standalone manner. The software mentioned (iRay SDK, iDetector) are for controlling the detector and integration, not for standalone diagnostic interpretation.

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

For the "concurrence study" of 30 clinical images, the type of "ground truth" and how it was established is not detailed. The study aimed to show "no significant difference" between images of the proposed and predicate device, rather than assessing diagnostic accuracy against an independent ground truth.

8. The sample size for the training set

This information is not applicable as the description refers to a medical imaging device (Flat Panel Detector) and its associated control software, not an AI model that would typically have a "training set" in the context of machine learning.

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

This information is not applicable for the same reasons as #8.

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Luna 1012X wireless digital flat panel detector is indicated for digital imaging solutions designed to provide general radiographic diagnosis for human anatomy including both adult and pediatric patients. It is intended to replace film/screen systems in all general-purpose diagnostic procedures. The device is not intended for mammography or dental applications.

Luna1012X Wireless Digital Flat Panel Detector (Hereinafter referred to as Luna1012X) is the kind of wireless digital flat panel detector. It supports the single frame mode, with the key component of TFT/PD image sensor flat panel of active area: 31.52cm×25.02cm. The sensor plate of Luna1012X is direct-deposited with CsI scintillator to achieve the conversion from X-ray to visible photon. The visible photons are transformed to electron signals by diode capacitor array within TFT panel, which are composed and processed by connecting to scanning and readout electronics, consequently to form a panel image by transmitting to PC through the user interface. The major function of the Luna1012X is to convert the X-ray to digital image, with the application of high resolution X-ray imaging. Both kinds of detectors are the key component of DR system, enable to complete the digitalization of the medical X-ray imaging with the DR system software. iRay SDK(include iDetector) is intended to supply API interface for DR system manufacturers. DR system manufacturer control the detector by SDK interface. SDK is not intend to be used directly by other users beside DR system manufacturers.

This document is a 510(k) Summary of Safety and Effectiveness for the iRay Technology Taicang Ltd. Wireless Digital Flat Panel Detector (Luna 1012X). It states that the device is substantially equivalent to a predicate device and provides information on its intended use, technological characteristics, and non-clinical testing.

However, the provided text does not contain information about acceptance criteria or a study proving that the device meets those criteria, specifically regarding AI/algorithm performance. The document is a regulatory submission focused on proving substantial equivalence to a predicate device based primarily on hardware specifications, material safety, electrical safety, and general performance parameters relevant to an X-ray detector. It is not an AI/algorithm performance study.

Therefore, I cannot extract the detailed information requested in the prompt (acceptance criteria for an AI algorithm, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, ground truth types, or training set details) from the provided text.

The document discusses the following:

• Device Name: Wireless Digital Flat Panel Detector (Luna 1012X)
• Intended Use: Digital imaging solutions for general radiographic diagnosis of human anatomy (adult and pediatric), replacing film/screen systems. Not for mammography or dental applications.
• Predicate Device: iRay Technology Co., Ltd. Mars1013X Wireless Digital Flat Panel Detector (K220668)
• Testing: Electrical safety and EMC testing (IEC/ES 60601-1, IEC 60601-2-54, IEC 60601-1-2), Biological Evaluation (ISO 10993-1).
• Performance Parameters Mentioned (for substantial equivalence comparison, not acceptance criteria for an AI): Spatial Resolution (Min. 4.3lp/mm), Modulation Transfer Function (MTF) (Min. 0.60 at 1 lp/mm), Detective Quantum Efficiency (DQE) (Min. 0.43 at 1 lp/mm). These are standard technical specifications for an X-ray detector, not for an AI.
• Software Mentioned: iRay SDK (including iDetector) as an API interface for DR system manufacturers, not an AI software for image interpretation.

Without information about an AI or algorithm in the provided text, I cannot fulfill the request.

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The Venul 748V flat panel detector is provided as an imaging component to the system manufacturer. It is mainly used in long bones, spine and other inspection fields. After collecting static imaged data is output to the processing equipment.

This device is suitable for providing radiography imaging for adult via DR system. The remaining notes depend on the final DR system.

It is not intended for mammography, dental applications, neonatal and fluoroscopy.

Digital flat panel detector is a cassette-size wired X-ray flat panel detector based on amorphous silicon thin-film transistor technologies. It was developed to provide X -Ray image, which contains an active matrix of 3064×8696 with 139um pixel pitch. Detector's scintillator is CsI(Cesium iodide). The biggest feature of Venu1748V is that it supports imaging of large-scale objects, including long bones and complete spine detection.

This document is a 510(k) Pre-Market Notification from iRay Technology Taicang Ltd. to the FDA for their Digital Flat Panel Detector, model Venu1748V. It outlines the device's technical characteristics and compares them to a predicate device.

Analysis of Acceptance Criteria and Study Details:

The provided document does not describe a clinical study or a multi-reader, multi-case (MRMC) study to prove the device meets acceptance criteria. Instead, it relies on non-clinical studies (bench testing) to demonstrate substantial equivalence to a predicate device. This is a common approach for imaging components like flat panel detectors where the primary concern is the technical performance of the imaging hardware itself, rather than diagnostic accuracy involving human interpretation when coupled with a full DR system.

Therefore, many of the requested points regarding clinical study design, expert involvement, and human reader performance are not applicable to this submission.

Here's a breakdown based on the provided text, addressing the points where information is available:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" in a separate table with pass/fail thresholds. Instead, it presents a comparison of key technical specifications between the proposed device (Venu1748V) and the predicate device (VIVIX-S 1751S). The implication is that meeting or exceeding the performance of the predicate device for these parameters constitutes "acceptance."

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

• Sample Size: Not applicable. The "test set" here refers to the non-clinical bench testing of the detector's physical performance characteristics. These tests are typically performed on a limited number of manufactured units (e.g., a few samples per batch) to ensure they meet specifications. The document does not specify the exact number of units tested for each parameter.
• Data Provenance: The company is iRay Technology Taicang Ltd., located in Taicang, Jiangsu, CHINA. The testing was performed internally or by a contracted lab. The data is retrospective in the sense that it was collected as part of the device's development and verification, prior to submission.

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

Not applicable. For non-clinical tests of a flat panel detector, "ground truth" is established by physical measurement standards and calibrated equipment, not by human expert consensus or clinical diagnosis. For example, MTF is measured using a phantom and analytical methods, not by radiologists.

4. Adjudication Method for the Test Set:

Not applicable. Since no human experts are establishing ground truth for diagnostic decisions, there's no need for 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:

No. The document explicitly states: "Clinical data is not needed to characterize performance and establish substantial equivalence. The non-clinical test data characterizes all performance aspects of the device based on well-established scientific and engineering principles." This device is a hardware component (a flat panel detector), not an AI algorithm assisting human readers.

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

No. This is a hardware component. There is no "algorithm only" performance in the sense of an AI diagnostic tool. The detector captures raw image data.

7. The Type of Ground Truth Used:

The "ground truth" for the non-clinical tests consists of:

• Physical standards/measurements: For parameters like dimensions, pixel pitch, greyscales, power consumption, temperature/humidity ranges.
• Engineering metrics: For performance characteristics like MTF, DQE, spatial resolution, signal-to-noise ratio, uniformity, defect, minimum triggering dose rate, and low contrast resolution. These are established through standardized testing procedures using phantoms and calibrated instruments.
• Compliance with standards: Electrical safety (IEC/ES 60601-1, IEC60601-2-54) and EMC testing (IEC 60601-1-2) ensure the device meets predefined safety and electromagnetic compatibility benchmarks.
• Software verification: The software "iDetector" hazards, requirements specification, and design specification were tested against the intended design specification.

8. The Sample Size for the Training Set:

Not applicable. This is a hardware device. "Training set" typically refers to data used to train AI/machine learning models.

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

Not applicable, as there is no training set mentioned for an AI/ML model for this hardware device.

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Wireless digital flat panel detector is indicated for digital imaging solutions designed to provide general radiographic diagnosis for human anatomy including both adult and pediatric patients. It is intended to replace film/screen systems in all general-purpose diagnostic procedures. The device is not intended for mammography or dental applications.

Mars1013X Wireless Digital Flat Panel Detector (Hereinafter referred to as Mars1013X) is the kind of wireless digital flat panel detector. It supports the single frame mode, with the key component of TFT/PD image sensor flat panel of active area: 33.18cm×25.28cm. The sensor plate of Mars1013X is direct-deposited with CsI(Cesium Iodide) scintillator to achieve the conversion from X-ray to visible photon. The visible photons are transformed to electron signals by diode capacitor array within TFT panel, which are composed and processed by connecting to scanning and readout electronics, consequently to form a panel image by transmitting to PC through the user interface. The major function of the Mars1013X is to convert the X-ray to digital image, with the application of high resolution X-ray imaging. Both kinds of detectors are the key component of DR system, enable to complete the digitalization of the medical X-ray imaging with the DR system software. iRay SDK(include iDetector) is intended to supply API interface for DR system manufacturers. DR system manufacturer control the detector by SDK interface. SDK is not intend to be used directly by other users beside DR system manufacturers.

The provided text is a 510(k) summary for the iRay Technology Taicang Ltd. Wireless Digital Flat Panel Detector (Mars1013X). It describes the device, its intended use, and its comparison to predicate devices to demonstrate substantial equivalence.

However, the document does NOT contain information about specific acceptance criteria related to a study proving the device meets those criteria in the context of AI/algorithm performance. It primarily focuses on the device's physical and technical specifications, electrical safety, biological evaluation, and comparison to predicate devices, but not on clinical performance metrics that would typically be established for an AI-powered diagnostic device.

The provided text describes a "Wireless Digital Flat Panel Detector", which is a hardware component for X-ray imaging, not an AI or algorithmic diagnostic device. Therefore, the questions related to AI-specific acceptance criteria, test sets, ground truth establishment, expert adjudication, MRMC studies, and standalone algorithm performance are not applicable to the information contained in this document.

The document states:

• "Mars1013X Wireless Digital Flat Panel Detector is the kind of wireless digital flat panel detector. It supports the single frame mode, with the key component of TFT/PD image sensor flat panel of active area: 33.18cm×25.28cm"
• "The major function of the Mars1013X is to convert the X-ray to digital image, with the application of high resolution X-ray imaging."

The performance metrics discussed (Spatial Resolution, MTF, DQE) are physical imaging characteristics of the detector itself, not diagnostic performance of an AI analyzing the images.

Therefore, it is not possible to extract the requested information regarding acceptance criteria and a study proving an AI device's performance from the provided text. The document pertains to the clearance of an X-ray detector, not an AI diagnostic algorithm.

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Focus HD 43 Detector is indicated for digital imaging solutions designed to provide general radiographic diagnosis for human anatomy including both adult and pediatric patients. It is intended to replace film/screen systems in all generalpurpose diagnostic procedures. The device is not intended for mammography or dental applications.

Focus HD 43 Detector is a kind of wireless digital flat panel detector. It supports the single frame mode, with the key component of TFT/PD image sensor flat panel of active area: 42.67cm×42.67cm. The sensor plate of Focus HD 43 Detector is direct-deposited with CsI scintillator to achieve the conversion from X-ray to visible photon. The visible photons are transformed to electron signals by diode capacitor array within TFT panel, which are composed and processed by connecting to scanning and readout electronics, consequently to form a panel image by transmitting to PC through the user interface. The major function of the Focus HD 43 Detector is to convert the X-ray to digital image, with the application of high resolution X-ray imaging. Both kinds of detectors are the key component of DR system, enable to complete the digitalization of the medical X-ray imaging with the DR system software. SDK(include iDetector) is intended to supply API interface for DR system manufacturers. DR system manufacturer control the detector by SDK interface. SDK is not intend to be used directly by other users beside DR system manufacturers.

The provided text is a 510(k) summary for the iRay Technology Taicang Ltd. Focus HD 43 Detector. It states that the device is substantially equivalent to a predicate device (Mars1717X Wireless Digital Flat Panel Detector, K210314).

Crucially, the document does NOT contain information about a study proving the device meets acceptance criteria derived from a performance study with human readers or AI algorithms. Instead, it focuses on demonstrating substantial equivalence through non-clinical testing and comparison of technical specifications with a previously cleared predicate device.

Therefore, I cannot provide the requested information regarding acceptance criteria and a study proving the device meets those criteria, as typically seen in performance claims for AI/CADe devices or new clinical functionalities. The information given indicates a different pathway to market clearance for this type of device (a digital X-ray detector).

However, I can extract the information provided about the device's technical specifications and the comparison to its predicate, which implicitly serves as the "acceptance criteria" for demonstrating substantial equivalence for this type of hardware device.

Here's an attempt to structure the available information relevant to the prompt, acknowledging the absence of a "study" in the typical sense of AI/clinical performance, and focusing on the technical equivalence:

Acceptance Criteria and Study for Focus HD 43 Detector (Based on Substantial Equivalence to Predicate Device)

The "acceptance criteria" for the Focus HD 43 Detector are primarily its substantial equivalence in performance characteristics to the legally marketed predicate device, the Mars1717X Wireless Digital Flat Panel Detector (K210314). The "study" proving this equivalence is a series of non-clinical tests and direct comparison of specifications.

1. Table of Acceptance Criteria and Reported Device Performance

For a device like an X-ray detector, acceptance criteria are generally based on meeting or exceeding the technical specifications of a predicate device, along with demonstrating safety and electromagnetic compatibility. The following table showcases the comparison provided in the 510(k) summary:

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

• Test Set Sample Size: Not applicable in the context of a clinical test set with patient data for performance evaluation. The "tests" were non-clinical engineering and safety tests.
• Data Provenance: The 510(k) summary explicitly states that "Non-clinical studies have been performed," and "All test results are meet the standard requirements." The testing was conducted by the manufacturer, iRay Technology Taicang Ltd. (China). The data refers to engineering and safety performance data, not patient-specific clinical data.

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

• Not applicable. Ground truth as typically defined for clinical/AI performance studies (e.g., expert radiological reads, pathology) was not established for this device's premarket clearance, as it's a hardware device demonstrating substantial equivalence through non-clinical testing.

4. Adjudication Method for the Test Set

• Not applicable. There was no human "adjudication" of images for clinical ground truth. Non-clinical tests typically adhere to defined protocols and measurement standards.

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

• No. An MRMC study is relevant for assessing human reader performance, often with and without AI assistance, or comparing different imaging modalities/interpretations. This 510(k) is for a digital X-ray detector hardware, not an AI or CADe device.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• No. This is not an algorithmic device in the sense of AI/CADe. Its performance is demonstrated through its physical characteristics and image acquisition capabilities, which are then used by human readers and integrated into existing DR systems.

7. Type of Ground Truth Used

• The "ground truth" for this device's acceptance is based on engineering specifications, safety standards compliance (e.g., IEC/ES 60601-1, IEC 60601-1-2, ISO 10993-1), and direct comparison to the technical characteristics of a legally marketed predicate device. There is no clinical or pathological "ground truth" derived from patient outcomes involved in this specific premarket notification.

8. Sample Size for the Training Set

• Not applicable. This device is a hardware component (digital flat panel detector) and does not involve AI or machine learning that requires a "training set" of data.

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

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

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Focus HD 35 Detector is indicated for digital imaging solutions designed to provide general radiographic diagnosis for human anatomy including both adult and pediatric patients. It is intended to replace film/screen systems in all generalpurpose diagnostic procedures. The device is not intended for mammography or dental applications.

Focus HD 35 Detector is a kind of wireless digital flat panel detector. It supports the single frame mode, with the key component of TFT/PD image sensor flat panel of active area: 35cm×43cm.

The sensor plate of Focus HD 35 Detector is direct-deposited with CsI scintillator to achieve the conversion from X-ray to visible photon. The visible photons are transformed to electron signals by diode capacitor array within TFT panel, which are composed and processed by connecting to scanning and readout electronics, consequently to form a panel image by transmitting to PC through the user interface. The major function of the Focus HD 35 Detector is to convert the X-ray to digital image, with the application of high resolution X-ray imaging. Both kinds of detectors are the key component of DR system, enable to complete the digitalization of the medical X-ray imaging with the DR system software.

SDK(include iDetector) is intended to supply API interface for DR system manufacturers. DR system manufacturer control the detector by SDK interface. SDK is not intend to be used directly by other users beside DR system manufacturers.

The provided text is a 510(k) summary for the iRay Technology Taicang Ltd. Focus HD 35 Detector. This document primarily focuses on establishing substantial equivalence to a predicate device (Mars1417X Wireless Digital Flat Panel Detector, K210316) rather than directly presenting a study proving that the device meets specific acceptance criteria related to a diagnostic task or clinical performance.

Medical device 510(k) clearances typically rely on demonstrating that the new device is as safe and effective as a legally marketed predicate device, often through engineering and performance testing. They do not usually involve clinical trials or studies like those required for novel AI/diagnostic devices that predict diagnoses or outcomes.

Therefore, many of the requested points regarding acceptance criteria, study details, human reader improvement with AI, standalone performance, ground truth, and training set information are not applicable or not explicitly detailed in this type of submission.

Here's a breakdown of what can be gleaned from the text, and where information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria here refer to the technical specifications and safety standards required for substantial equivalence, rather than diagnostic performance metrics (e.g., sensitivity, specificity for disease detection). The comparison is primarily against the predicate device's specifications.

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

• Sample size used for the test set: Not applicable for a typical 510(k) submission based on technical equivalence. The "test set" here refers to the actual physical device and its components undergoing engineering and safety tests, not a dataset for diagnostic performance evaluation.
• Data provenance: Not applicable. The data is from engineering and safety tests conducted on the physical device and from a comparison of specifications with the predicate. There is no mention of country of origin for clinical or image data, as such data is not the primary basis for this type of submission. The tests were likely conducted by the manufacturer or accredited labs.

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

• Not Applicable. This device is an X-ray detector, a hardware component. Its clearance is based on technical specifications, safety, and performance as an imaging capture device, not on its ability to produce a specific diagnosis from images or generate ground truth. There is no "ground truth" in the diagnostic sense being established for a test set of images within this submission.

4. Adjudication method for the test set

• Not Applicable. As no clinical diagnostic performance study with images and ground truth is described, there's no adjudication method.

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 is not an AI-powered diagnostic device. It's a digital X-ray detector. Therefore, no MRMC study comparing human readers with and without AI assistance was performed or reported here.

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

• No. This is a hardware device (X-ray detector), not an algorithm.

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

• Not Applicable. For this hardware device submission, "ground truth" relates to physical measurements and compliance with engineering standards (e.g., spatial resolution, DQE, electrical safety standards). There is no clinical ground truth (pathology, expert consensus on images, etc.) associated with this submission.

8. The sample size for the training set

• Not Applicable. This is a hardware device; no "training set" in the context of machine learning or AI is mentioned or relevant to its clearance.

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

• Not Applicable. As no training set is mentioned, no ground truth for it was established.

Summary of the Study (as described in the 510(k) submission):

The "study" referenced in the document is primarily a technical assessment and comparison to establish substantial equivalence with a legally marketed predicate device (K210316, Mars1417X Wireless Digital Flat Panel Detector).

• Methodology:

  • Side-by-side comparison of technical specifications: The document provides tables comparing parameters like intended use, indications for use, physical dimensions, imaging characteristics (spatial resolution, MTF, DQE), environmental operating conditions, and interfaces between the proposed device and the predicate device.
  • Non-clinical testing: This included:
    • Electrical safety and EMC testing: Performed according to IEC/ES 60601-1 and IEC 60601-1-2 standards.
    • Biological Evaluation: Materials contacting operators' or patient's skin evaluated with ISO 10993-1.
    • Functional performance testing: The document implies that areas with minor differences (e.g., power consumption, local load capacity, operating temperature range, atmospheric pressure range) were still considered substantially equivalent following non-clinical studies. The improved spatial resolution (5.0 lp/mm vs. Min. 4.3 lp/mm) was presented as a positive difference.

• Conclusion: The manufacturer concluded that the Focus HD 35 Detector is substantially equivalent to the predicate device regarding safety and effectiveness, based on the non-clinical studies and comparison of technical characteristics. The main identified differences (DC input port, power consumption, local load, operating temperature range, storage/transportation atmospheric pressure) were implicitly determined not to raise new questions of safety or effectiveness.

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Dental Sensors, models NanoPix1 and NanoPix2 are intended to collect dental x-ray photons and convert them into electronic impulses that may be stored, viewed, and manipulated for diagnostic use by dentists. This device must only be used in hospital environments, clinics or dental offices by trained and qualified dental personnel, and not used in the oxygenrich environment. This device is suitable for providing dental radiography imaging for both adult and pediatric.

Dental Sensors, models NanoPix1 and NanoPix2, are feature 20um pixel pitch CMOS sensor with directly deposited CsI:Tl scintillator which ensures optimal resolution. An easy to use hi-speed direct USB interface enables a simple connection to a PC without need for an additional control box. The intra-oral software application makes it easy to acquire, enhance, analyze, view and share images from the sensor. The major function of NanoPix1 and NanoPix2 are to convert the X-ray to digital image, with the application of high resolution X-ray imaging. This detector is the key component of NanoPix system, enables to complete the digitalization of the medical X-ray imaging with the NanoPix software.

The provided text describes a 510(k) premarket notification for Dental Sensors NanoPix1 and NanoPix2. This documentation primarily focuses on establishing substantial equivalence to a predicate device (Digital Intraoral X-ray Imaging System, models Pluto0001X and Pluto0002X, K210312), rather than detailing an independent study to prove the device meets specific acceptance criteria in the way a de novo or PMA submission might.

Therefore, the information you're requesting regarding acceptance criteria, sample sizes, expert involvement, and ground truth establishment for a study proving the device performance is largely not present in the provided 510(k) summary.

However, I can extract the information that is available, including the performance characteristics that were compared to establish substantial equivalence.

Here's a breakdown of the available information:

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

Since this is a 510(k) for substantial equivalence, formal "acceptance criteria" for a specific study proving novel performance are not explicitly stated. Instead, the performance of the proposed devices (NanoPix1, NanoPix2) is compared to the predicate device (Pluto0001X, Pluto0002X) to demonstrate that they are technologically similar and perform equivalently. The "acceptance criteria" here implicitly align with matching or performing comparably to the predicate device.

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 states that "Non-clinical test data characterizes all performance aspects of the device based on well-established scientific and engineering principles". This typically refers to bench testing and physical measurements of the device's characteristics (like spatial resolution, MTF, signal-to-noise ratio, etc.). It does not mention a specific "test set" in terms of patient data or clinical images, nor does it detail sample sizes for such a set or data provenance.

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

This information is not provided. As no clinical test set is detailed, there's no mention of experts establishing ground truth for such a set.

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

This information is not provided, as no clinical test set is detailed.

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 reported or indicated. The submission is for a dental sensor (hardware plus basic image viewing software), not an AI-powered diagnostic tool. The software described is for image acquisition, enhancement, viewing, and reporting, not for automated diagnosis or assistance in a way that would require a comparative effectiveness study with human readers.

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

The device is a dental sensor and associated software for image viewing and manipulation. It is not an algorithm designed for standalone diagnostic performance. Thus, a standalone algorithm performance study is not applicable or mentioned.

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

For the non-clinical studies mentioned, the "ground truth" would be scientific and engineering measurements based on established standards for imaging device performance (e.g., direct measurement of spatial resolution, MTF, signal-to-noise ratio). There is no mention of clinical ground truth (like pathology or expert consensus on clinical images) as clinical studies were not deemed necessary for this 510(k) submission.

8. The sample size for the training set:

This information is not applicable and not provided. The device is an imaging sensor, not a machine learning model that requires a training set in the conventional sense. The "NanoPix software" mentioned performs image processing, viewing, and management functions, not AI-driven diagnostic tasks that would necessitate a large training dataset for model development.

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

This information is not applicable and not provided, as there is no mention of a training set for a machine learning model.

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Digital intraoral X-ray Imaging System, models i-Sensor H2, are used in conjunction with dental Radiography in medical units. The product is used for dental X-ray examination, the diagnosis of structural diseases of teeth, jaws and mouth. The product is expected to be used in hospitals and clinics, operated and used by trained professionals under the guidance of doctors.

This device is not intended for mammography and conventional photography applications.

This device is suitable for providing dental radiography imaging for both adult and pediatric.

The Digital intraoral X-ray Imaging System, models i-Sensor H1 and i-Sensor H2, are the digital intra-oral sensor. It features a 20um pixel pitch CMOS sensor with directly deposited CsI:Tl scintillator which ensures optimal resolution. An easy to use hispeed direct USB interface enables a simple connection to a PC without need for an additional control box. The optional intra-oral software application makes it easy to acquire, enhance, analyze, view and share images from the sensor. The major function of i-Sensor H1 and i-Sensor H2 are to convert the X-ray to digital image, with the application of high resolution X-ray imaging. This detector is the key component of intra-oral DR system, enables to complete the digitalization of the medical X-ray imaging with the intra-oral DR system software.

The provided text is a 510(k) summary for a Digital Intraoral X-ray Imaging System. It describes non-clinical testing performed to establish substantial equivalence to a predicate device. However, it explicitly states that clinical data is not needed to characterize performance and establish substantial equivalence. Therefore, the document does not contain information about acceptance criteria or a study proving the device meets those criteria based on human-in-the-loop performance or diagnostic accuracy.

The document focuses on non-clinical performance characteristics to demonstrate substantial equivalence through technical specifications and comparison to the predicate device.

Here's a breakdown of the requested information based on the provided text, highlighting where information is not present due to the nature of the submission:

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

The document does not present a formal table of acceptance criteria for diagnostic performance against a ground truth and reported device performance against those criteria. It does list several non-clinical tests and states that "All test results are meet the standard requirements." and "the results have shown that the i-Sensor H1 & i-Sensor H2 are substantially equivalent to the predicate devices". These statements imply that the acceptance criteria for these non-clinical tests were that the device's performance met the relevant standard requirements or was equivalent to the predicate.

Here are the non-clinical tests mentioned and the general statement of their performance:

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

Not applicable. No clinical test set or data from human subjects was used. The submission states, "Clinical data is not needed to characterize performance and establish substantial equivalence." This implies no test set of patient data was used for diagnostic performance evaluation.

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. No clinical test set was used, and thus no expert ground truth establishment for diagnostic purposes was performed.

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

Not applicable. No clinical test set 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

Not applicable. The device is a "Digital Intraoral X-ray Imaging System" (hardware), not an AI-assisted diagnostic software. No MRMC study was mentioned or performed.

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

This refers to an "algorithm only" performance. Since the device is a hardware imaging system, this isn't directly applicable in the sense of a diagnostic algorithm's standalone performance. The non-clinical tests (e.g., MTF, spatial resolution) demonstrate the inherent image quality capabilities of the device itself, which could be considered its "standalone" performance characteristics as an imaging system.

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

For the non-clinical tests: The "ground truth" was established by engineering standards, physical measurements, and comparison to the predicate device's established performance specifications. For instance, spatial resolution would be measured against a known standard.

8. The sample size for the training set

Not applicable. The provided document details a hardware device. If there is a "software Ai-Dental" mentioned, it seems to be an image acquisition/enhancement software rather than a diagnostic AI that would require a training set in the typical sense (for supervised learning on disease detection). The document discusses the software's compliance with design specifications and test cases, not its training data.

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

Not applicable, as there's no mention of a diagnostic AI algorithm requiring a training set.

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