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The IODR1717 / IODR1417 / IODR1417-GF (Digital Flat Panel X-Ray Detector) is indicated as a digital imaging solution designed for providing the general radiographic diagnosis of human anatomy targeting both adults and children. It is intended to replace film-based radiographic diagnostic systems. Not to be used for mammography.

The IODR1717 / IODR1417 / IODR1417-GF detectors are wired or wireless digital flat panel detectors that have been designed for faster, more streamlined approach to digital radiography systems. The IODR1717 / IODR1417 / IODR1417-GF detectors utilize a combination of propriety TFT and scintillator (Csl), and those and electronics are housed in one package. The detectors support an auto-trigger signal sensing technology that allows the detectors to be used without generator integration.

The flat panel sensors of The IODR1717 / IODR1417 / IODR1417-GF are fabricated using thin film technology based on amorphous silicon technology. Electronically, the sensors are much like conventional photodiode arrays. Each pixel in the array consists of a light-sensing photodiode and a switching Thin Film Transistor (TFT) in the same electronic circuit. Amorphous silicon photodiodes are sensitive to visible light, with a response curve roughly comparable to human vision. The sensitivity of amorphous silicon photodiodes peaks in green wavelengths, well matched to scintillators such as Csl. The response has the excellent linearity of a charge-integrating-biased photodiode.

Aspen\View software includes the basic functionality: generator control, detector control, firmware, image acquisition, image calibration and correction, image storage.

The Aspen Imaging Healthcare IODR1717 / IODR1417 / IODR1417-GF Digital Flat Panel X-ray Detectors are evaluated against a predicate device (K223930). This submission focuses on demonstrating substantial equivalence based on technological characteristics and performance, rather than a clinical study with specific acceptance criteria on diagnostic accuracy for an AI-powered device.

Here's an analysis of the provided text in relation to your request, with the caveat that this is a hardware device (digital X-ray detector) and not an AI algorithm for diagnosis. Therefore, many of your requested points, especially those related to AI effectiveness, human reader improvement, and expert-established ground truth for a diagnostic test set, are not directly applicable.

1. Table of Acceptance Criteria and Reported Device Performance

The submission establishes substantial equivalence by comparing the proposed device's performance characteristics to a legally marketed predicate device (K223930). The "acceptance criteria" here are implicitly that the proposed device's reported performance metrics are equivalent to or better than the predicate device.

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

The document states that "Imaging performance test has been conducted according to: IEC 62220-1, Medical Electrical Equipment Characteristics of Digital X-ray Imaging Devices Part . 1-1: Determination of the Detective Quantum Efficiency Detectors Used in Radiographic Imaging." This standard describes methods for laboratory testing of detector performance, using phantoms and controlled X-ray beams. It is not equivalent to a clinical test set with patient data.

Therefore, there is no information about a "test set" in the context of clinical images or patient data. The provenance of such clinical data (e.g., country of origin, retrospective/prospective) is not provided because such a test set was not used for this type of device submission.

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 a hardware device (digital X-ray detector) and not an AI-powered diagnostic algorithm, there was no clinical test set requiring expert interpretation or ground truth establishment in the diagnostic sense. The performance tests (DQE, MTF, resolution) are objective physical measurements.

4. Adjudication method for the test set

Not applicable, for the same reasons as point 3.

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 is not an AI-powered diagnostic device, and no MRMC study was conducted or mentioned. The submission is for an X-ray detector, which is a component rather than a diagnostic interpretation system.

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

This question is geared towards AI/software. The IODR devices are digital flat panel X-ray detectors. They are hardware components for X-ray imaging. While they include "Aspen\View software" for "generator control, detector control, firmware, image acquisition, image calibration and correction, image storage", this software pertains to the operation and image processing of the detector itself, not diagnostic analysis or algorithms to be used standalone for diagnosis. Therefore, a standalone performance study in the sense of an algorithm for diagnostic interpretation was not done.

7. The type of ground truth used

The "ground truth" for the performance tests (DQE, MTF, resolution) is based on physical measurements using phantoms and standardized protocols as defined by IEC 62220-1-1. This is a technical standard for evaluating the intrinsic image quality characteristics of the detector, not pathological or clinical outcomes data.

8. The sample size for the training set

Not applicable. This is not an AI algorithm that requires a training set. The device itself is hardware.

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

Not applicable, as there is no training set for an AI algorithm.

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Digital Flat Panel X-Ray Detector is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic systems in all general purpose diagnostic procedures. Not to be used for mammography.

1717WCE, 1717WCE-HR, 1717WCE-HS, 1717WCE-GF X-ray detectors, are wired/wireless digital solid state X-ray detectors that are based on flat panel technology. The wireless LAN (IEEE 802.11 n/ac) communication signals images captured to the system and improves the user operability through high speed processing. These radiographic image detectors are processing unit consist of a scintillator coupled to an TFT sensor. The flat-panel detectors need to be integrated with an x-ray generator (not part of the submission), so it can be utilized to capture and digitize x-ray images for radiographic diagnosis.

1717WCE. 1717WCE-HR. 1717WCE-HS. 1717WCE-GF includes the software (firmware) of basic level of concern. It's the same Image Acquisition and Operating Software used for the predicate device is used but modified to include additional detector models in comparison with the predicate device. Full software documentation has been submitted, as well as the necessary sections to demonstrate device cybersecurity.

The RAW files can be further processed as DICOM compatible image files by separate consol SW (K190866, XmaruView V1 / Rayence Co.,Ltd) for a radiographic diagnosis and analysis.

1717WCE is the basic model. 1717WCE-HR is identical with the basic model except for pixel pitch not related to safety. 1717WCE-HS is identical with the basic model except for marking of sheet. 1717WCE-GF is identical with the basic model except for case color and pixel pitch.

The given text describes a 510(k) submission for a Digital Flat Panel X-ray Detector. The submission aims to demonstrate substantial equivalence to a predicate device. However, the document does not describe a study that uses an AI algorithm as a device or an AI assistance to human readers, so most of the requested information regarding AI-specific criteria (like MRMC studies, standalone AI performance, training set details, or ground truth establishment for AI) is not present.

The document focuses on the technical and clinical performance comparison of the subject device (new X-ray detector models) against a predicate device (older X-ray detector models) through non-clinical and image quality assessments by human reviewers.

Here's the available information based on the provided text, addressing the points where information is available and noting where it's not:

1. Table of acceptance criteria and reported device performance:

The document doesn't present a formal table of "acceptance criteria" for the entire device as one might see for an AI algorithm's specific performance metrics (e.g., AUC, sensitivity, specificity). Instead, it demonstrates substantial equivalence by comparing its technological characteristics and performance to a predicate device. The performance is described qualitatively through comparisons of image quality.

Qualitative Image Quality Assessment (summarized from section 6):

• 1717WCE 99.9um vs. 1417WCE 100um: Subject device (1717WCE 99.9um) showed overall better image quality, clearer anatomical structures (bony and soft tissues of upper and lower extremities). Predicate device (1417WCE 100um) had decreased sharpness, more overexposed appearance, and higher noise.
• 1717WCE 140um vs. 1417WCE 140um: Subject device (1717WCE 140um) showed overall better image quality, clearer anatomical structures. Predicate device (1417WCE 140um) had less sharpness, more overexposed appearance, and higher noise.
• Conclusion: Both 1717WCE 140um and 1717WCE 99.9um demonstrated sufficient image quality for diagnostic purposes, with better image quality than the predicate device.

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

• Sample Size: The document states, "After comparing a broad review of plain radiographic images taken with 1717WCE... and 1417WCE images obtained equivalent quality for the same view obtained from a similar patient." It further mentions reviewing "plain radiographic images taken with 1717WCE 99.9um and the 1417WCE 100um" and "plain radiographic images taken with 1717WCE 140um and the 1417WCE 140um." No specific numerical sample size (e.g., number of images, number of patients) is provided for this qualitative review.
• Data Provenance: Not explicitly stated (e.g., country of origin). The studies appear to be internal performance assessments rather than large-scale clinical trials. The data is retrospective in the sense that existing images were compared.

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

• The document mentions "Upon review of the plain radiographic images..." suggesting a human review. However, it does not specify the number of experts, their qualifications (e.g., radiologist with X years of experience), or how ground truth was established by them. The "ground truth" here seems to be subjective human judgment of image quality for diagnostic purposes rather than a definitive disease presence/absence.

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

• No adjudication method is described. The qualitative image quality assessment is presented as a singular conclusion derived from "review."

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 submission is for an X-ray detector, not an AI algorithm assisting human readers. The qualitative image review is a comparison of image detector performance, not human reader performance with or without AI.

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

• Not applicable. The device is a digital X-ray detector, which produces images. It's not a standalone AI algorithm designed to interpret those images without human involvement.

7. The type of ground truth used:

• Qualitative Human Assessment of Image Quality. The "ground truth" for the performance study is based on visual assessment by unspecified reviewers that the image quality of the subject device is "better" or "sufficient" for diagnostic purposes compared to the predicate device. It is not tied to a confirmed diagnosis (e.g., pathology, surgical findings, or long-term outcomes data). The document also mentions "non-clinical test report for the subject device were prepared and submitted to FDA... by using the identical test equipment and same analysis method described by IEC 62220-1" for metrics like MTF, DQE, and NPS, which are objective image quality measurements.

8. The sample size for the training set:

• Not applicable. This device is an X-ray detector, not an AI or machine learning model that requires a training set.

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

• Not applicable. As the device is not an AI/ML model, there is no "training set" or "ground truth for the training set."

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Edge Air(1417) Digital Flat Panel X-Ray Detector is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic system in all general purpose diagnostic procedures. Not to be used for mammography.

Edge 40(1417) is a wired/wireless digital solid state X-ray detector that is based on flat-panel technology. The wireless LAN (IEEE 802.11a/g/w/ac) communication signals images captured to the system and improves the user operability through high-speed processing. This radiographic image detector and processing unit consists of a scintillator coupled to an a-Si TFT sensor. This device needs to be integrated with a radiographic imaging system. It can be utilized to capture and digitalize X-ray images for radiographic diagnosis The RAW files can be further processed as DICOM compatible image files by a separate console SW program (K190866 / Xmaruview V1 (Xmaru Chiroview, Xmaru Podview) / Rayence Co., Ltd.) for a diagnostic analysis.

The provided text describes the Edge Air (1417) Digital Flat Panel X-ray Detector, a device intended to replace film or screen-based radiographic systems for general diagnostic procedures, excluding mammography. The submission argues for substantial equivalence to a predicate device, Edge Air (K172681).

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The submission primarily focuses on demonstrating substantial equivalence to a predicate device, rather than defining explicit acceptance criteria against a fixed standard. However, the comparisons provided between the proposed device and the predicate device can be interpreted as the performance metrics and their desired "acceptance" (i.e., being similar or equivalent to the predicate).

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

The document mentions "clinical images have been reviewed by a licensed radiologist" for the "clinical consideration test." However, it does not specify the exact sample size (number of images or patients) used for this clinical review or the demographics of the "similar age group and anatomical structures."

The data provenance is implicitly prospective for the clinical images used for comparison, as they involved "taking sample radiographs." The country of origin of the data is not specified.

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

Only one expert is mentioned: "a licensed radiologist." No specific details about the radiologist's experience (e.g., "10 years of experience") are provided beyond being "licensed" and an "expert opinion."

4. Adjudication Method for the Test Set

The adjudication method appears to be none beyond a single expert's opinion. The text states, "clinical images have been reviewed by a licensed radiologist to render an expert opinion." There is no mention of multiple readers, consensus, or a specific adjudication process for discrepancies.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

No MRMC comparative effectiveness study was done as described for an AI device. This submission is for a digital flat panel X-ray detector, which is a hardware component for image acquisition, not an AI-powered diagnostic tool. The device itself does not involve AI assistance for human readers in the sense of improving their diagnostic capability. The comparison is for image quality of two different hardware detectors.

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

Again, this question is not directly applicable to the device under review. The Edge Air (1417) is an X-ray detector, not a standalone algorithm. Its "performance" is measured by physical characteristics (MTF, DQE, NPS) and its ability to produce diagnostic images comparable to a predicate device. The imaging software (Xmaruview V1) is mentioned as a separate 510(k) (K190866), but the submission for the detector itself does not detail standalone performance of an algorithm.

7. The Type of Ground Truth Used

For the non-clinical tests (MTF, DQE, NPS), the ground truth is derived from physical measurements against established standards (IEC 62220-1).

For the "clinical consideration test," the ground truth is expert opinion/comparison by a single licensed radiologist, asserting similarity in image quality ("spatial and soft tissue contrast resolutions for both devices are equivalent"). It's a comparative visual assessment against images from the predicate device by an expert.

8. The Sample Size for the Training Set

The concept of a "training set" is not directly applicable here. This is a hardware device (X-ray detector). The imaging software (Xmaruview V1) might have had a training set if it involved AI, but the submission for the detector does not provide this information. The device's manufacturing and performance are based on engineering design and testing, not machine learning training.

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

Since there is no "training set" for the X-ray detector itself, this question is not applicable.

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The PEDRA-17F detector is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic system in all general-purpose diagnostic procedures. It is not to be used for mammography.

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I am sorry, but the provided text from the FDA 510(k) letter for the PEDRA-17F Digital Flat Panel X-ray Detector does not contain any information regarding acceptance criteria, performance studies, sample sizes, expert qualifications, or ground truth establishment related to an AI/ML-driven device.

This document is a standard 510(k) clearance letter for a digital flat panel X-ray detector, which is a hardware device for capturing X-ray images, not an AI/ML software device for image analysis. The letter focuses on the substantial equivalence of the hardware device to existing predicate devices based on its indications for use and general controls.

Therefore, I cannot fulfill your request to describe the acceptance criteria and the study that proves the device meets them using the provided text. The information required for your request (e.g., acceptance criteria for diagnostic accuracy, sample sizes for AI model validation, expert qualifications for ground truth, etc.) is not present in this document.

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Edge Air Digital Flat Panel X-Ray Detector is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic system in all general purpose diagnostic procedures. Not to be used for mammography.

Edge Air is a wired/wireless digital solid state X-ray detector that is based on flat-panel technology. The wireless LAN (IEEE 802.11a/g/n/ac) communication signals images captured to the system and improves the user operability through high-speed processing. This radiographic image detector and processing unit consists of a scintillator coupled to an a-Si TFT sensor. This device needs to be integrated with a radiographic imaging system. It can be utilized to capture and digitalize Xray images for radiographic diagnosis The RAW files can be further processed as DICOM compatible image files by a separate console SW program (K160579 / Xmaru View V1 and Xmaru PACS/ Rayence Co., Ltd.) for a diagnostic analysis.

This document describes the equivalence of the OSKO, INC. Edge Air Digital Flat Panel X-ray Detector to a predicate device, the Rayence Co., Ltd. 1717WCC. The focus is on demonstrating that the Edge Air performs similarly to the predicate device, rather than establishing novel acceptance criteria for improved diagnostic accuracy.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a substantial equivalence submission, the "acceptance criteria" are primarily based on demonstrating similar performance to the predicate device. The performance metrics are reported in terms of comparison.

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Edge is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic system in all general purpose diagnostic procedures. Not to be used for mammography.

Edge digital flat panel X-ray detector is based on flat-panel technology. This radiographic image detector and processing unit consists of a scintillator coupled to an a-Si TFT sensor. This device needs to be integrated with a radiographic imaging system. It can be utilized to capture and digitalize X-ray images for radiographic diagnosis.

The RAW files can be further processed as DICOM compatible image files by separate console SW (K160579 / XmaruView V1 and XmaruPACS/ Rayence Co.,Ltd.) for a radiographic diagnosis and analysis.

The provided text describes the 510(k) submission for the "Edge Digital Flat Panel X-ray Detector" and its substantial equivalence to a predicate device, the "1717SCC_140µm". The performance testing described primarily focuses on demonstrating this equivalence through non-clinical (bench) testing and a clinical consideration report.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of "acceptance criteria" for the device's performance in the typical sense of a target value that must be met for approval. Instead, the acceptance criteria for the study proving substantial equivalence appears to be based on the similarity of performance characteristics to the predicate device.

The performance characteristics used for comparison are:

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

The document states: "To further demonstrate the substantial equivalency of two devices, clinical images are taken from both subject devices and reviewed by a licensed US radiologist to render an expert opinion."

• Sample Size: Not explicitly stated as a number of images or patients. It mentions "sample radiographs of similar age group and anatomical structures". This suggests a limited sample size rather than a large clinical trial.
• Data Provenance:
  • Country of Origin: US (implied by "licensed US radiologist").
  • Retrospective or Prospective: Not explicitly stated, but the description "clinical images are taken from both subject devices and reviewed" and "sample radiographs... taken" suggests these were specifically acquired for this comparison, implying a prospective data collection for the purpose of the study.

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

• Number of Experts: "a licensed US radiologist" - One (1) expert.
• Qualifications: "licensed US radiologist". No specific years of experience or subspecialty are provided.

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

• Adjudication Method: None mentioned. The single radiologist's opinion appears to be the sole basis for the clinical image quality assessment ("reviewed by a licensed US radiologist to render an expert opinion").

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 conducted. This study's purpose was to demonstrate substantial equivalence between two X-ray detectors, not to evaluate AI assistance for human readers. The device itself is an X-ray detector, not an AI algorithm for improving reader performance.
• Effect Size: Not applicable as no such study was performed or needed for this device.

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

• Standalone Performance: The device is an X-ray detector. Its performance is standalone in terms of image acquisition (MTF, DQE, etc.). The "clinical consideration" involved human review of the images produced by the device, but this was to compare the image quality of the device to its predicate, not to assess an AI's diagnostic performance without a human. The non-clinical tests (MTF, DQE) are indeed "standalone" measures of the detector's physical performance.

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

• The term "ground truth" isn't explicitly used in the context of diagnostic findings. For the non-clinical tests (MTF, DQE), the "ground truth" is established by adherence to IEC 62220-1 standards for objective physical performance measurements.
• For the "clinical consideration" part, the "ground truth" for image quality comparison was the expert opinion of a single licensed US radiologist. This is a subjective assessment of image characteristics ("spatial and soft tissue contrast resolution are superior"). There's no mention of a diagnostic "ground truth" (e.g., presence/absence of disease confirmed by pathology or other means) being established for the clinical images used for review. The focus was on comparing the quality of the images produced by the devices, not on the diagnostic accuracy of those images for specific conditions.

8. The sample size for the training set

• This information is not applicable and not provided. The "Edge Digital Flat Panel X-ray Detector" is an imaging hardware device, not an AI algorithm that requires a training set. The clinical consideration involved comparing images produced by two hardware devices.

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

• This information is not applicable and not provided as the device is not an AI algorithm requiring a training set.

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The BSD4343R Digital Flat Panel X-ray Detector (Model: BT-DA24-IA) is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic system in all general-purpose diagnostic procedures. It is not to be used for mammography.

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This document is a 510(k) clearance letter from the FDA for a digital flat panel X-ray detector. It is not a study proving the device meets acceptance criteria for an AI/ML medical device. Therefore, I cannot extract the information required to populate the fields you specified.

The provided text discusses:

• Device: BSD4343R Digital Flat Panel X-ray Detector
• Manufacturer: Bontech Inc.
• Regulatory Classification: Class II, Product Code MOB, Regulation Number 21 CFR 892.1680 (Stationary x-ray system)
• Approval Date: June 21, 2017
• Indications for Use: Digital imaging solution for general radiographic systems for human anatomy, intended to replace film or screen-based systems in general-purpose diagnostic procedures (not for mammography).

The letter confirms substantial equivalence to a predicate device, meaning it's cleared because it's as safe and effective as a device already on the market, not because it's an AI/ML device that has met specific performance criteria through a clinical study.

Therefore, I cannot provide the requested information for acceptance criteria and study details because this document does not contain that information, nor does it describe an AI/ML device or its performance study.

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The BSD3543(BT-DA22-IA) detector is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic system in all general-purpose diagnostic procedures. It is not to be used for mammography.

BSD3543(BT-DA22-IA/BT-DB22-IA) is a digital X-ray flat panel detector which intercepts x-ray photons and the scintillator (BT-DB22-IA(Gdos) / BT-DA22-IA(CsI)) emits visible spectrum photons that illuminate an array of photo (a-SI)-detector that creates electrical signals. After the electrical signals are generated, it is converted to digital values, and the images will be displayed on the monitor. This device should be integrated with an operating PC and an X-Ray generator. It can digitalize x-ray images and transfer them for radiography diagnostics. Advanced digital image processing allows considerably efficient diagnosis, all kinds of information management, and sharing of image information on network.

Based on the provided text, the device in question is the BSD3543 Digital Flat Panel X-ray Detector. The information describes non-clinical performance testing to demonstrate substantial equivalence to a predicate device (BONTECH BSD4343, K160204), rather than a study proving the device meets specific clinical acceptance criteria for diagnostic accuracy.

Here's a breakdown of the requested information based on the provided text. Please note that several items (like clinical study details, expert qualifications, adjudication methods, and training set information) are explicitly not available because the submission relies on non-clinical testing for substantial equivalence, not clinical studies for efficacy.

Acceptance Criteria and Device Performance for BSD3543 Digital Flat Panel X-ray Detector

The acceptance criteria for the BSD3543 Digital Flat Panel X-ray Detector are based on demonstrating substantial equivalence to its predicate device, BONTECH BSD4343 (K160204), through non-clinical performance testing. The key performance metrics compared are related to image quality and technical specifications.

1. Table of Acceptance Criteria and Reported Device Performance

The document states that the non-clinical performance testing concluded that "BSD3543(BT-DA22-IA/BT-DB22-IA) offer similar or better resolution performance than BSD4343 at 0 ~ 3.5lp/mm spatial frequencies. Moreover, the ability of BSD3543(BT-DA22-IA/BT-DB22-IA) to utilize the input image signal are more efficient than BSD4343 at same patient exposure as shown in the detective quantum efficiency graph." While the tables provide numerical differences, the overall conclusion is that performance is similar or better. Note that direct comparison of GDOS to CsI is done, and both are offered for the proposed device, suggesting the specific scintillator type impacts the exact values. The table for predicate only lists CsI for MTF/DQE, while proposed lists GDOS.

2. Sample Size Used for the Test Set and the Data Provenance
The submission relies on non-clinical bench testing rather than a clinical test set. The data provenance is described as "bench testing performed to compare the subject devices to the predicate." No patient data or country of origin is mentioned for this testing, as it's not a clinical study.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts
Not applicable. No experts were used to establish ground truth for a clinical test set, as this was a non-clinical performance evaluation focused on physical properties (MTF, DQE, etc.) of the imaging device.

4. Adjudication Method for the Test Set
Not applicable. No clinical test set or adjudication method 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 an X-ray detector, not an AI-powered diagnostic tool, and no MRMC study was performed.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. This device is an X-ray detector, not an algorithm, so standalone algorithm performance is not relevant. The performance tested is the inherent image quality of the detector.

7. The Type of Ground Truth Used
The "ground truth" for this non-clinical study were the established scientific and engineering performance metrics (e.g., MTF, DQE values, pixel pitch, resolution) obtained through standardized laboratory measurements. These are objective measurements of the device's physical and technical capabilities.

8. The Sample Size for the Training Set
Not applicable. This is not an AI/machine learning device, so there is no training set in the context of algorithm development.

9. How the Ground Truth for the Training Set Was Established
Not applicable. (See #8).

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The BSD4343 detector is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic system in all general-purpose diagnostic procedures. It is not to be used for mammography.

BSD4343 is a digital X-ray flat panel detector which intercepts x-ray photons and the scintillator (Gadox:Tb type) emits visible spectrum photons that illuminate an array of photo (a-SI)-detector that creates electrical signals. After the electrical signals are generated, it is converted to digital values, and the images will be displayed on the monitor. This device should be integrated with an operating PC and an X-Ray generator. It can digitalize x-ray images and transfer them for radiography diagnostics. Advanced digital image processing allows considerably efficient diagnosis, all kinds of information management, and sharing of image information on network.

The acceptance criteria and study proving the device meets them are described below for the BSD4343 Digital Flat Panel X-ray Detector.

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state quantitative "acceptance criteria" for performance metrics in a pass/fail format. Instead, it demonstrates substantial equivalence to a predicate device (LLX240AB01) by showing similar or better performance in key imaging characteristics and adherence to safety standards. The "Reported Device Performance" below is derived from the comparison table and the "Non-clinical study" section, relative to the predicate.

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

The document does not explicitly mention a "test set" in the context of a clinical study with patient data. The "non-clinical study" section refers to physical values for comparison (DQE, MTF, NPS) with the predicate device. These are typically measured on phantoms or test objects, not human subjects, and therefore do not involve patient-specific sample sizes or data provenance in the clinical sense.

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

Since no clinical "test set" with patient data requiring expert ground truth is described, this information is not applicable. The basis for comparison is the technical specifications and measured performance of the device against a predicate, typically using engineering metrics and standards.

4. Adjudication Method for the Test Set

Not applicable, as no clinical "test set" requiring adjudication or expert consensus for ground truth is described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The submission relies on non-clinical performance data and substantial equivalence to a predicate device.

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

Yes, the performance testing described is standalone, focusing on the inherent technical capabilities of the device (such as DQE, MTF, NPS) and its compliance with safety and electrical standards. These are measures of the device's technical performance, independent of human interpretation in a clinical setting.

7. The Type of Ground Truth Used

For the non-clinical performance metrics (DQE, MTF, NPS), the "ground truth" would be established through highly controlled measurements using calibrated equipment and phantoms according to industry standards. These are objective physical measurements, not subject to expert consensus, pathology, or outcomes data in the traditional sense of medical image evaluation. For regulatory compliance, the "ground truth" is defined by the requirements of the standards (e.g., IEC 60601-1).

8. The Sample Size for the Training Set

This information is not applicable. This device is a digital X-ray detector, which is hardware, not an AI algorithm that requires a training set of data.

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

This information is not applicable, as the device is not an AI algorithm and therefore does not have a "training set."

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1717SGN and 1717SCN Digital Flat Panel X-Ray Detector are indicated for digital imaging solution designed for general radiographic system for human anatomy. They are intended to replace film or screen based radiographic systems in all general purpose diagnostic procedures. Not to be used for mammography.

1717SGN and 1717SCN digital solid state X-ray detectors are based on flat-panel technology. Both radiographic image detector and processing units consist of a scintillator coupled to an a-Si TFTsensor. A digital flat panel X-ray detector needs to be integrated with a radiographic imaging system. It can be utilized to capture and digitalize X-ray images for radiographic diagnosis. The RAW files can be further processed as DICOM compatible image files by separate console SW (not part of this 510k submission) for a radiographic diagnosis and analysis.

The provided text describes a 510(k) premarket notification for the Rayence Co., Ltd.'s 1717SGN / 1717SCN Digital Flat Panel X-ray Detectors. The submission aims to demonstrate substantial equivalence to predicate devices (LLX240AB01 and LTX240AA01-A manufactured by Samsung Mobile Display Co., Ltd.).

This document outlines the testing conducted to support the claim of substantial equivalency but does not provide specific, quantifiable acceptance criteria in a table format with corresponding pass/fail results or detailed device performance metrics relevant to an AI/Machine Learning context. The focus is on demonstrating that the new devices perform comparably to (or better than) existing predicate devices for general radiographic use, primarily through non-clinical bench testing and a limited clinical consideration.

Therefore, many of the requested elements for an AI/ML device's acceptance criteria and study proving performance cannot be fully extracted or are not applicable from this document, as it pertains to a hardware medical device submission.

Here's an attempt to address the points based on the provided text, highlighting where information is missing or not applicable:

Acceptance Criteria and Study Proving Device Meets Acceptance Criteria

This document describes the 510(k) submission for a digital flat panel X-ray detector, not an AI/Machine Learning diagnostic algorithm. Therefore, the typical acceptance criteria and study designs for AI/ML devices (e.g., sensitivity, specificity, AUC, MRMC studies, ground truth establishment for AI training/testing sets) are not directly applicable or detailed in this submission.

The general acceptance criteria for this hardware device revolve around demonstrating substantial equivalence to predicate devices in terms of:

• Safe and effective operation.
• Having the same intended use.
• Similar technological characteristics.
• Performance that is comparable or superior to the predicate.

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

The document does not present acceptance criteria in a quantitative table format suitable for an AI/ML context (e.g., minimum sensitivity, specificity). Instead, performance is reported comparatively against the predicate devices.

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

• Test Set (Clinical Consideration): "sample radiographs of similar age groups and anatomical structures" were taken. A specific number of cases or images is not provided.
• Data Provenance: Not explicitly stated, but given the manufacturer is Rayence Co., Ltd. (Korea) and the consulting firm is in Houston, TX, the data could be from various geographies. The clinical consideration involved a "licensed US radiologist", suggesting the review was done in the U.S. There is no indication of retrospective or prospective study design, but the description "After comparing a broad review of plain radiographic images taken with..." suggests an observational review rather than a formal prospective clinical trial.

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

• Number of Experts: "a licensed US radiologist" (implies one expert).
• Qualifications: "licensed US radiologist." No specific experience level (e.g., 10 years) is provided.

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

• Adjudication Method: "reviewed by a licensed US radiologist to render an expert opinion." This implies no formal adjudication among multiple readers, as only one expert is mentioned. The comparison was a qualitative expert review.

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 or AI assistance: This submission is for a digital X-ray detector, not an AI-powered diagnostic tool. Therefore, an MRMC study related to AI assistance was not applicable and not performed.

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

• Not applicable: This is a hardware device. No diagnostic algorithm is being evaluated in a standalone capacity.

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

• Type of Ground Truth (for clinical consideration): The "ground truth" was the expert opinion of a single licensed US radiologist comparing images from the new devices against images from the predicate devices. This is a qualitative comparison, not a formal diagnostic ground truth like pathology.

8. The sample size for the training set

• Not applicable: This is a hardware device; there is no mention of a "training set" in the context of an AI/ML algorithm.

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

• Not applicable: No training set for an AI/ML algorithm was used.

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