Search Results

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.

Ask a specific question about this device

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.

Ask a specific question about this device

Mars 1417X 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.

Mars1417X Wireless Digital Flat Panel Detector is a wireless digital flat panel detector that supports single frame mode. Its key component is a TFT/PD image sensor flat panel with an active area of 35cm×43cm. The sensor plate has a direct-deposited CsI scintillator to convert X-ray to visible photon. These visible photons are transformed to electron signals by a diode capacitor array within the TFT panel, which are composed and processed by connecting to scanning and readout electronics, forming a panel image transmitted to a PC through the user interface. The main function is to convert X-ray to digital image for high resolution X-ray imaging. It is a key component of a DR system, enabling the digitalization of medical X-ray imaging with DR system software. iRay SDK (including iDetector) provides an API interface for DR system manufacturers to control the detector.

The provided text describes a 510(k) premarket notification for the Mars 1417X Wireless Digital Flat Panel Detector. This document primarily focuses on demonstrating substantial equivalence to a predicate device (Mars1417V-TSI) rather than presenting a standalone clinical study with detailed acceptance criteria and performance data.

Therefore, the information required to answer most of your questions (especially those related to a clinical study, reader performance, and ground truth establishment) is not present in the provided text. The document states that "clinical consideration may not necessary for changes in the dimensions of the image receptor with otherwise identical materials if non-clinical information is sufficient to support the substantial equivalence." This implies that a formal clinical study, as you've outlined, was likely not conducted or required for this 510(k) clearance.

However, I can extract the acceptance criteria and performance related to the device's technical specifications and non-clinical testing as presented in the document, comparing the proposed device (Mars1417X) to its predicate (Mars1417V-TSI).

Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by demonstrating equivalence or improvement over the predicate device's technical specifications. The "reported device performance" refers to the specifications of the proposed device, Mars1417X.

As for the other points, based on the provided text:

2. Sample size used for the test set and the data provenance: Not applicable in the context of clinical reads for performance testing. The "test set" here refers to the physical device's specifications being evaluated against the predicate. Data provenance is not described for any clinical study. The device manufacturer is iRay Technology Taicang Ltd. in China. The submission is for a 510(k) Pre-Market Notification, which relies on demonstrating substantial equivalence, often through non-clinical performance data and comparison to an existing device, rather than new clinical trials.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for clinical diagnostic performance using expert consensus or pathology is not mentioned, as a clinical performance study of this nature was likely not performed.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable. There is no mention of adjudication for a clinical 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: No, an MRMC study was not done, and this device is a digital flat panel detector, not an AI diagnostic algorithm. Its primary function is to convert X-rays to digital images, not to provide AI assistance to human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an algorithm but a hardware device. Its performance is measured by its raw imaging capabilities (e.g., spatial resolution, DQE).

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): For the specified improvements (e.g., spatial resolution, DQE), the ground truth is established through physical measurements and standardized imaging tests as per industry standards (e.g., lp/mm, ISO standards for DQE measurement). For safety aspects, compliance with standards like IEC/ES 60601-1 and ISO 10993-1 establishes "ground truth" for electrical safety, EMC, and biological evaluation.

8. The sample size for the training set: Not applicable. This is a hardware device, not an AI algorithm requiring a training set.

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

Ask a specific question about this device

Mars1717X 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.

Mars1717X Wireless Digital Flat Panel Detectors (Hereinafter referred to as Mars1717X) 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 Mars1717X 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 Mars1717X 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 document is a 510(k) Summary for the Mars1717X Wireless Digital Flat Panel Detector by iRay Technology Taicang Ltd. This document primarily focuses on demonstrating substantial equivalence to a predicate device (Mars1717V-VSI, K201043) rather than detailing a specific study to prove the device meets acceptance criteria regarding clinical performance.

The document states that clinical consideration may not be necessary for changes in image receptor dimensions if non-clinical information is sufficient to support substantial equivalence. This implies that a dedicated clinical study to evaluate the device against specific performance acceptance criteria for diagnostic accuracy (e.g., sensitivity, specificity for detecting conditions) was likely not performed or considered necessary by the FDA for this 510(k) clearance due to the nature of the device as an X-ray detector and the comparison to a predicate.

Therefore, many of the requested details about a study proving device performance against acceptance criteria for diagnostic capability cannot be extracted from this document, as such a study does not appear to be the basis for this 510(k) clearance. The "acceptance criteria and reported device performance" primarily relate to technical specifications and equivalence.

However, I can extract information regarding technical performance metrics and how they compare between the proposed device and the predicate. These comparisons serve as the "evidence" for substantial equivalence.

Here's the breakdown based on the provided text, focusing on the technical and non-clinical aspects:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present "acceptance criteria" in the format of a clinical performance study. Instead, it compares the technical specifications and performance characteristics of the proposed device (Mars1717X) to its predicate (Mars1717V-VSI). The "acceptance criteria" can be inferred as being at least equivalent to or better than the predicate device for relevant technical specifications to demonstrate substantial equivalence.

Regarding a "study that proves the device meets the acceptance criteria":

For this 510(k) submission, the "study" is a comparative technical performance assessment against a legally marketed predicate device (K201043). The conclusion of substantial equivalence means the device meets the regulatory "acceptance criteria" for market clearance based on this comparison. The non-clinical studies performed were used to demonstrate that changes in panel size, structure, IP grade, and surface pressure do not raise new questions of safety or effectiveness and that the performance is substantially equivalent to or better than the predicate.

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

• Sample Size: Not applicable in the context of a clinical test set for diagnostic accuracy, as no such clinical study appears to have been conducted for this 510(k). The "test set" would be the device itself undergoing various engineering and performance tests (e.g., DQE measurements, spatial resolution charts, durability tests). The document does not specify the number of units tested.
• Data Provenance: The technical performance data (DQE, spatial resolution, etc.) would be generated from laboratory tests conducted by the manufacturer, iRay Technology Taicang Ltd., in China.

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

• Not applicable, as no clinical test set with human diagnostic ground truth was used for this 510(k) clearance based on the provided document.

4. Adjudication Method for the Test Set:

• Not applicable, as no clinical test set requiring expert adjudication was used.

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

• No, a MRMC comparative effectiveness study was not performed according to this document. The submission focuses on technical equivalence to a predicate X-ray detector, not on the diagnostic effectiveness of human readers using the device with and without AI assistance.

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

• Yes, the evaluation is inherently a standalone performance assessment of the device itself (the flat panel detector) in terms of its image acquisition capabilities (spatial resolution, DQE, etc.). There is no AI algorithm being evaluated for diagnostic assistance in this context. The device's performance is measured objectively based on physical and technical specifications, independent of human interpretation.

7. Type of Ground Truth Used:

• For the technical performance aspects, "ground truth" refers to objective physical measurements and standards. For example:
  • Spatial resolution is measured using phantoms or line pair gauges with known patterns.
  • DQE is measured according to standardized protocols (e.g., IEC 62220-1) using known X-ray spectra and dose levels.
  • Durability (surface pressure, IP rating) is tested against engineering specifications and industry standards.
  • Safety (electrical, biological) is assessed against international standards (e.g., IEC 60601-1, ISO 10993-1).

8. Sample Size for the Training Set:

• Not applicable, as this is not an AI/machine learning device requiring a training set in the conventional sense. The "training" in manufacturing would refer to quality control and calibration processes during production.

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

• Not applicable, as there is no training set in the context of an AI algorithm.

Ask a specific question about this device

Wireless Digital Flat Panel Detector (Model:Mars1717V-VSI, Mano4343W) are indicated for digital imaging solutions designed to provide general radiographic diagnosis for human anatomy including both adult and pediatic patients. They are intended to replace film/screen systems in all general-purpose diagnostic procedures. These two devices are not intended for mammography, dental applications.

Mars1717V-VSI and Mano4343W Wireless Digital Panel Detectors (Hereinafter referred to as Mars1717V-VSI and Mano4343W) are the kind of wireless digital flat panel detectors. They support the single frame mode, with the key component of TFT/PD image sensor flat panel of active area: 427mm x 427mm. Two models Mars1717V-VSI and Mano4343W are totally same except for label and model name. The sensor plate of Mars1717V-VSI and Mano4343W 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 Mars1717V-VSI and Mano4343W 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.

The provided text is a 510(k) summary for a medical device (Wireless Digital Flat Panel Detector). It describes the device, its intended use, and comparison to a predicate device to demonstrate substantial equivalence. However, it does not contain information about a study proving the device meets specific acceptance criteria based on diagnostic performance (e.g., sensitivity, specificity, accuracy).

The document focuses on demonstrating substantial equivalence primarily through technical characteristics, safety, and non-clinical performance, rather than clinical diagnostic accuracy or reader performance studies.

Therefore, most of the information requested in your prompt regarding acceptance criteria and studies to prove diagnostic performance (points 1-7, and 9 for the test set) cannot be extracted from this document. The document explicitly states: "clinical consideration may not necessary for changes in the pixel size and resolution with the same x-ray detection material and may not necessary for changes in the wireless functionality if non-clinical information is sufficient to support the substantial equivalence." This indicates that a detailed clinical performance study as you've described was not deemed necessary for this 510(k) submission.

Here's what can be extracted based on the provided text:

Preamble: This 510(k) submission for the Wireless Digital Flat Panel Detector (Models: Mars1717V-VSI, Mano4343W) focuses on demonstrating substantial equivalence to a predicate device (Mars1717XF-CSI, K183713) based on technical, electrical, mechanical, and non-clinical performance characteristics, rather than a clinical study evaluating diagnostic accuracy or human reader performance. Therefore, detailed information on diagnostic performance acceptance criteria, sample sizes for such studies, expert involvement for ground truth, or MRMC studies is not present.

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

• Acceptance Criteria (Implicit via comparison to predicate and standards adherence): The device is substantially equivalent to the predicate device and meets applicable electrical safety, EMC, and biological evaluation standards. Specific quantitative diagnostic performance metrics are not provided as acceptance criteria in this document.
• Reported Device Performance (Technical and Safety):

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

• Sample Size: Not applicable/Not specified for diagnostic performance tests. The evaluation was primarily based on technical specifications and non-clinical considerations. The statement "There was no significant difference between the images of the Mars1717V-VSI/Mano4343W and those of the predicate device" implies some form of image comparison, but no formal test set size or study methodology is detailed.
• Data Provenance: Not applicable for diagnostic test set; the document describes a technical and safety comparison.

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

• Not applicable/Not specified. Ground truth in the context of diagnostic performance studies is not mentioned, as such a study was not performed or submitted.

4. Adjudication method for the test set

• Not applicable. No diagnostic test set or ground truth establishment method described.

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 comparative effectiveness study was not done. This device is a flat panel detector, not an AI-assisted diagnostic tool.

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

• No, this product is hardware (a flat panel detector), not a standalone algorithm.

7. The type of ground truth used

• Not applicable. No diagnostic ground truth was established as a clinical diagnostic performance study was not performed. The evaluation relied on technical performance metrics, adherence to safety standards, and perceived equivalence of image quality to a predicate device.

8. The sample size for the training set

• Not applicable. This device is a flat panel detector, not an AI/machine learning algorithm requiring a training set.

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

• Not applicable. See point 8.

Ask a specific question about this device

Mars1417V-TSI wireless digital flat panel detector and Mano4336W wireless digital flat panel detector are indicated for digital imaging solutions designed to provide general radiographic diagnosis for human anatomy including both adult and pediatric patients. They are intended to replace film/screen systems in all general-purpose diagnostic procedures. The device is not intended for mammography or dental applications.

Mars1417V-TSI and Mano4336W Wireless Digital Flat Panel Detectors (Hereinafter referred to as Mars1417V-TSI and Mano4336W) are the kind of wireless digital flat panel detectors. They support the single frame mode, with the key component of TFT/PD image sensor flat panel of active area: 34.56cm×42.00cm. Mars1417V-TSI and Mano4336W are totally same except for label and model name.

The sensor plate of Mars1417V-TSI and Mano4336W 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 Mars1417V-TSI and Mano4336W 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 intend 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 Detectors (Models Mars1417V-TSI, Mano4336W). This document does not contain details about acceptance criteria, a specific study proving the device meets those criteria, or clinical performance data in the context of diagnostic accuracy. These types of studies (like MRMC or standalone performance evaluations with ground truth) are typically required for AI-powered diagnostic devices, which this is not.

This document focuses on establishing substantial equivalence to a predicate device (Mars1417XF-CSI, K182551) based on non-clinical performance characteristics, safety, and technological characteristics, rather than clinical performance for diagnostic tasks.

Therefore, most of the requested information regarding acceptance criteria, study details, sample sizes, expert qualifications, and ground truth establishment for diagnostic accuracy cannot be extracted from this document.

Here's a breakdown of what can and cannot be answered based on the provided text:

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

The document does not explicitly state acceptance criteria for diagnostic performance or present a table comparing such criteria to reported diagnostic performance. It focuses on technical specifications and safety standards.

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

This information is not provided in the document. The substantial equivalence argument relies on non-clinical performance and safety testing, not on a clinical test set for diagnostic accuracy 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)

This information is not provided because there was no clinical diagnostic performance study described. The document is about the technical specifications and safety of the X-ray detector, not an AI or diagnostic algorithm that requires expert ground truth.

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

This information is not provided as there was no clinical diagnostic performance study described.

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 such study is mentioned or implied in this 510(k) summary. This device is a digital flat panel detector, a hardware component for X-ray imaging, not an AI-assisted diagnostic software.

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

No standalone diagnostic algorithm performance study is mentioned. This device is a digital flat panel detector, not a diagnostic algorithm.

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

Not applicable, as no clinical diagnostic performance study with ground truth was described.

8. The sample size for the training set

Not applicable, as this device is a hardware component and not an AI/ML algorithm that requires a training set of imaging data.

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

Not applicable, as this device is a hardware component and not an AI/ML algorithm that requires a training set with established ground truth.

In summary: The provided 510(k) summary is for an X-ray detector, a hardware device. The equivalence argument is based on technical specifications, electrical safety, EMC testing, and biological evaluation, comparing it to a legally marketed predicate device. It does not involve AI/ML components or clinical diagnostic accuracy studies that would require the establishment of ground truth by experts from a sample of patient data. Therefore, most of the questions regarding clinical study design for diagnostic performance cannot be answered from this document.

Ask a specific question about this device

Mars 1717XF-GSI Wireless Digital Flat Panel Detector is indicated for digital imaging solution designed for providing general radiographic diagnosis of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures. This device is not intended for mammography or dental applications, pediatric, pregnant women and fluoroscopy.

Mars1717XF-GSI Wireless Digital Flat Panel Detector is a kind of wireless digital flat panel detector. It supports the single frame mode, with the key component of TFT (Thin Film Transistor)/PD (Photo Diode) image sensor flat panel of active area: 42.48cm×42.54cm. The sensor plate of Mars1717XF-GSI Wireless Digital Flat Panel Detector is coated with Gd2O2S (GOS) 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 Mars1717XF-GSI Wireless Digital Flat Panel Detector is to convert the X-ray to digital image, with the application of high resolution X-ray imaging. This detector is the key component of DR system, enables to complete the digitalization of the medical X-ray imaging with the DR system software.

The provided document is a 510(k) summary for the iRay Technology Taicang Ltd. Wireless Digital Flat Panel Detector (Mars1717XF-GSI). It compares the proposed device to a predicate device (Mars1417XF-GSI, K182550) to demonstrate substantial equivalence.

Based on the content, here's an analysis of the acceptance criteria and the study that proves the device meets them:

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

The document does not explicitly present a table of "acceptance criteria" for clinical performance as would be seen for an AI-powered diagnostic device. Instead, it focuses on demonstrating substantial equivalence by comparing the technical specifications of the proposed device to the predicate device. The implicit "acceptance criteria" here are that the performance metrics of the proposed device are comparable to or better than the predicate device, or within acceptable tolerances for the intended use.

Here's a table focusing on the comparative technical specifications presented, which serve as the "performance" data for establishing equivalence:

Note: The primary difference highlighted and addressed in the document is the "panel dimension" which impacts "Image Matrix Size," "Effective Imaging Area," "MTF," "DQE", "Power Consumption", and "Dimensions". The submission argues that non-clinical information is sufficient to support substantial equivalence despite these changes.

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 describes non-clinical testing rather than a clinical study with a "test set" of patient data in the typical sense of AI/diagnostic device trials (i.e., analyzing images of patients to assess diagnostic accuracy). The "study" here refers to:

• Electrical Safety and EMC testing: Performed according to IEC/ES 60601-1 and IEC/EN 60601-1-2.
• Biological Evaluation: Evaluated with ISO 10993-1 for materials contacting operators' skin.
• Non-clinical Considerations: Performance comparisons, likely quantitative measurements of detector characteristics (MTF, DQE, spatial resolution), and electromagnetic compatibility. This would involve a sample of the manufactured devices, not patient data.
• Clinical Consideration: The submission states that a clinical consideration (study with patient data) was not necessary because the modification from the predicate device to the proposed device is primarily the "panel dimension." It claims "non-clinical information is sufficient to support the substantial equivalence."

Therefore, there is no patient-based "test set" sample size or data provenance (country, retrospective/prospective) relevant to clinical performance assessment mentioned because a clinical study was explicitly deemed unnecessary by the manufacturer, and presumably accepted by the FDA for this direct predicate comparison.

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)

Since no clinical study with a patient imaging test set was conducted (as per point 2), there were no experts used to establish ground truth for a clinical test set. The ground truth in this submission is established through:

• Standardized technical measurements: For parameters like MTF, DQE, spatial resolution, which are objective physical properties of the detector.
• Compliance with international standards: For electrical safety, EMC, and biological compatibility.

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

Not applicable, as no patient-based clinical "test set" requiring adjudication of diagnoses 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. This device is a digital flat panel detector (hardware), not an AI-powered diagnostic software. Its purpose is to capture X-ray images, not to interpret them or assist human readers in diagnosis. Therefore, no MRMC study or AI assistance evaluation was performed.

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

Not applicable, as this is a hardware device (X-ray detector), not a standalone algorithm.

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

As explained in point 3, the "ground truth" for the performance evaluation in this 510(k) submission is based on:

• Objective physical measurements (e.g., MTF, DQE, spatial resolution) using phantoms and calibrated equipment.
• Adherence to international safety and performance standards (e.g., IEC/ES 60601-1, IEC/EN 60601-1-2, ISO 10993-1).
• Demonstration of equivalence to a legally marketed predicate device based on these technical and safety characteristics.

8. The sample size for the training set

Not applicable. This is a hardware device, not an AI model 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 a hardware device.

Ask a specific question about this device

Mars1417XF-CSI Wireless Digital Flat Panel Detector is indicated for digital imaging solution designed for providing general radiographic diagnosis of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures. This device is not intended for mammography or dental applications.

Mars1417XF-CSI Wireless Digital Flat Panel 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: 35.04cm×42.54cm.

The sensor plate of Mars1417XF-CSI Wireless Digital Flat Panel 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 Mars1417XF-CSI Wireless Digital Flat Panel Detector is to convert the X-ray to digital image, with the application of high resolution X-ray imaging. This detector is the key component of DR system, enables to complete the digitalization of the medical X-ray imaging with the DR system software.

The provided text describes a 510(k) submission for a Wireless Digital Flat Panel Detector (Mars1417XF-CSI). It focuses on establishing substantial equivalence to a predicate device rather than providing acceptance criteria and a detailed study proving the device meets them as a standalone AI/diagnostic device.

The document outlines performance characteristics of the detector itself (e.g., DQE, MTF, spatial resolution) and a "concurrence study" using phantom images, but this is not a clinical study to validate a diagnostic AI or to show human performance improvement.

Therefore, for AI/diagnostic device validation, much of the requested information (like specific acceptance criteria for diagnostic performance, details on test set ground truthing by experts, MRMC studies, or training set details) is not present in the provided text, as this device is a hardware component (a digital X-ray detector).

However, I can extract the relevant information regarding the device's technical specifications and the "concurrence study," and highlight what information isn't available for aspects typically associated with AI/diagnostic device validation.

Here's the breakdown based on the provided text:

Device: Wireless Digital Flat Panel Detector (Mars1417XF-CSI)
Device Type (as per 510(k)): Stationary x-ray system component (Product Code: MQB)
Purpose: Digital imaging solution for general radiographic diagnosis of human anatomy, replacing film/screen systems. Not an AI diagnostic device.

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

The document frames "acceptance criteria" through comparison with a predicate device and established technical parameters for X-ray detectors. It doesn't list explicit pass/fail criteria for clinical diagnostic performance but rather demonstrates comparability of technical specifications.

Notes: The "acceptance criteria" here are implicitly meeting the FDA 510(k) standard of substantial equivalence to a predicate device by demonstrating comparable technical performance and safety. The study is not a diagnostic performance study validating an AI algorithm.

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

• Test Set Size: 30 images
• Data Provenance: The images were described as "Phantom images." The origin (country, retrospective/prospective clinical data) is not specified, as this was a technical performance comparison using phantoms, not clinical data.

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 the "concurrence study" used phantom images and focused on the technical image quality comparison, not diagnostic interpretation by human experts to establish ground truth for a diagnostic AI. The statement "No significant difference between the images" implies a qualitative assessment, but details on assessors (number or qualifications) are absent.

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

• Not applicable / Not provided. Given it was a "concurrence study" of phantom images, detailed adjudication methods for human diagnostic interpretation are not relevant or described.

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 performed. This device is a digital X-ray detector, not an AI-powered diagnostic tool, so a study comparing human reader performance with and without AI assistance is beyond the scope of this 510(k) submission for a hardware component.

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

• Not applicable. This submission is for an X-ray detector, not a diagnostic algorithm. Therefore, a standalone algorithm performance study was not conducted.

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

• The ground truth for the "concurrence study" was based on phantom images and comparison to the predicate device's image output, not clinical ground truth like pathology or patient outcomes. The study aimed to demonstrate technical equivalence in image generation.

8. The sample size for the training set

• Not applicable / Not provided. Given this is a hardware device (X-ray detector) and not an AI algorithm, there is no "training set" in the context of machine learning. The device's calibration and manufacturing processes would ensure its performance.

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

• Not applicable / Not provided. As there is no AI training set, there is no ground truth establishment for such a set.

Ask a specific question about this device

Mars1417XF-GSI Wireless Digital Flat Panel Detector is indicated for digital imaging solution designed for providing general radiographic diagnosis of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures. This device is not intended for mammography or dental applications.

Mars1417XF-GSI Wireless Digital Flat Panel 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: 35.04cm×42.54cm. The sensor plate of Mars1417XF-GSI Wireless Digital Flat Panel Detector is direct-deposited with Gd2O2S 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 Mars1417XF-GSI Wireless Digital Flat Panel Detector is to convert the X-ray to digital image, with the application of high resolution X-ray imaging. This detector is the key component of DR system, enables to complete the digitalization of the medical X-ray imaging with the DR system software.

The provided document is a 510(k) summary for a medical device called the "Wireless Digital Flat Panel Detector" (Model: Mars1417XF-GSI). This document primarily focuses on demonstrating substantial equivalence to a predicate device (Mars1417V-PSI, K161730) rather than presenting a detailed study with acceptance criteria for an AI/CAD algorithm.

The device itself is a digital X-ray detector, not an AI or CAD system that provides diagnostic assistance. Therefore, many of the requested criteria such as "effect size of how much human readers improve with AI vs without AI assistance" or "adjudication method" for establishing ground truth for an AI algorithm are not applicable to the information provided.

However, I can extract information related to the technical performance of the device, which can be seen as acceptance criteria for its physical characteristics and image quality, and how it was tested.

Here's a breakdown based on the information available:

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

The document doesn't explicitly state "acceptance criteria" in a typical clinical study sense for an AI device. Instead, it compares the technical characteristics of the proposed device to its predicate device to demonstrate substantial equivalence. These technical characteristics can be considered as performance metrics that, when similar or improved, demonstrate "acceptance" in the context of a 510(k) submission.

Note: The "acceptance criteria" for these are generally that the proposed device performs comparably or better, demonstrating that the changes do not negatively impact safety or effectiveness. For some parameters, like ADC Digitization, the proposed device shows an improvement (16 bit vs 14 bit).

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

The document describes non-clinical studies focused on the physical and performance attributes of the detector itself, not on analyzing patient data with an algorithm. Therefore, there isn't a "test set" of patient images in the context of an AI algorithm evaluation.

The non-clinical studies performed include:

• Electrical Safety and EMC testing (IEC/ES 60601-1, IEC 60601-1-2)
• Biological Evaluation (ISO 10993-1)
• Evaluation of detector characteristics: Detective quantum efficiency (DQE), Quantum limited performance, Modulation transfer function (MTF), Effects of aliasing, Sensitivity linearity, Lag, Change in detection sensitivity, Dose requirement and reciprocity changes, Stability of device characteristics with time, Uniformity of device characteristic, Noise power spectrum (NPS), Spatial resolution, Image Acquisition time, and Black level.

The "sample size" for these technical tests would refer to the number of devices or components tested, which is not specified but is typically a small number for device verification. The data provenance would be laboratory testing data, not patient data from specific countries.

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

Not applicable, as this is not an AI/CAD device being evaluated on clinical images by human experts. The "ground truth" for the technical performance metrics (e.g., DQE, MTF) is established through standardized physical measurements and calculations.

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

Not applicable, as there is no clinical test set requiring expert 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:

Not applicable. The device is a digital X-ray detector, not an AI or CAD system 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 itself is the "standalone" component in the sense that it converts X-rays to digital images without an AI algorithm for diagnostic interpretation. The document explicitly states that the device is a "key component of DR system, enables to complete the digitalization of the medical X-ray imaging with the DR system software."

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

For the evaluation of the detector's image quality and physical performance, the "ground truth" is established through physical measurements and calculations using standardized methods (e.g., for DQE, MTF, spatial resolution). These are objective technical metrics, not clinical ground truth like pathology or expert consensus.

8. The sample size for the training set:

Not applicable. This device is an X-ray detector, not a machine learning model that requires a training set. The "software" mentioned (iRay DR and iRay SDK) are for device control, image acquisition, and processing, not for AI-based interpretation.

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

Not applicable (see point 8).

Ask a specific question about this device

Mars1417V-PSI Wireless Digital Flat Panel Detector is indicated for digital imaging solution designed for providing general radiographic diagnosis of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures. This device is not intended for mammography or dental applications.

Mars1417V-PS1 Wireless Digital Flat Panel 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: 36cm×43cm.

The sensor plate of Mars1417V-PSI Wireless Digital Flat Panel Detector is direct-deposited with Gd2O2S 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 Mars1417V-PSI Wireless Digital Flat Panel Detector is to convert the X-ray to digital image, with the application of high resolution X-ray imaging. This detector is the key component of DR system, enables to complete the digitalization of the medical Xray imaging with the DR system software.

The iRay DR used for getting Digital X -ray radiography images from the flat panel detectors. iRay DR is used to handle the DICOM protocol (DICOM 3.0), iRay DR is responsible for the DR equipment management, acquisition and processing functions, to provide patient registration, scanning, image processing and forwarding, and other functions ..

The provided document is a 510(k) Summary for the iRay Technology (Shanghai) Ltd. Wireless Digital Flat Panel Detector (Mars1417V-PSI), which is a digital X-ray system. The document focuses on demonstrating substantial equivalence to a predicate device, rather than providing detailed acceptance criteria and a standalone study for the device's clinical performance.

However, based on the information provided, here's a breakdown of the requested elements:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of "acceptance criteria" for clinical performance. Instead, it relies on demonstrating substantial equivalence by comparing technological characteristics and an absence of significant difference in clinical images compared to a predicate device (ViZion DR+Wireless, K152279).

Based on the Nonclinical Considerations section, the performance characteristics evaluated are:

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

• Sample Size for Clinical Image Comparison: 30 clinical images.
• Data Provenance: The document does not specify the country of origin for the clinical images, nor does it explicitly state if the study was retrospective or prospective. Given the context of a 510(k) submission for substantial equivalence based on a comparison, it's highly likely these were retrospective images or images specifically acquired for comparison purposes but not necessarily part of a broader prospective clinical trial.

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

The document states: "A concurrence study of 30 clinical images was conducted to compare the performance of the Mars1417V-PSI to that of the predicate device (ViZion DR+Wireless,K152279)."

It does not specify the number of experts used or their qualifications for evaluating these 30 clinical images. It only mentions that "no significant difference" was found.

4. Adjudication Method for the Test Set

The document does not specify any adjudication method (e.g., 2+1, 3+1, none) for the clinical image comparison, nor does it detail how the "no significant difference" conclusion was reached.

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

A multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not done. The study described is a comparison of the device's images to those of a predicate device, not an evaluation of AI assistance for human readers.

6. Standalone Performance Study

A standalone performance study (algorithm only without human-in-the-loop performance) was performed in terms of non-clinical tests evaluating physical characteristics like DQE, MTF, spatial resolution, and others. The software (iRayDR) also underwent standalone testing with 83 test cases. The clinical image comparison, while assessing image quality, doesn't constitute a standalone algorithm performance in the context of an AI device, as this is a digital flat panel detector, not an AI diagnostic algorithm.

7. Type of Ground Truth Used

For the 30 clinical images used in the comparison study, the "ground truth" implicitly referred to the diagnostic quality of the images produced by the predicate device. The study aimed to show "no significant difference" in image quality between the proposed device and the predicate device, implying that the diagnostic utility of the predicate's images served as the reference. There is no mention of pathology or outcomes data as ground truth.

8. Sample Size for the Training Set

The document does not provide any information regarding a training set sample size. This device is a digital flat panel detector, which converts X-rays into digital images, and the software mentioned (iRayDR) handles image processing and DICOM protocols. This is not an AI diagnostic algorithm that requires a specific training set to learn to identify pathology.

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

As no training set is mentioned in the context of an AI diagnostic algorithm, this question is not applicable. The software components described appear to be for image acquisition, processing, and management, not for automated diagnosis based on
learned patterns.

Ask a specific question about this device