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EXTRON Series are a mobile fluoroscopic X-ray system with high output capacity, high thermal capacity and high resolution image processing system, which provides X-ray images of the patient's anatomy during surgery or treatment. This device plays an important role in emergency injury treatment, orthopedic surgery, neurosurgery surgery, bone surgery, etc. This device has a function to save important a specific images as records, so you can easily search for the images and transmit it to the PACS system in the hospital to help the medical staff in diagnosis.

Examples of a clinical application may include: Neurosurgery, Orthopedics, Anesthesiology, Urology, Gynecology, Internal Medicine

(※ This device is not intended for mammography applications.)

EXTRON Series are mobile fluoroscopic X-ray systems with high output capacity, high thermal capacity, and high-resolution image processing systems that provide X-ray images of the patient's anatomical structures during surgery or treatment. This device plays an important role in emergency injury treatment, orthopedic surgery, neurosurgery surgery, bone surgery, etc. This device has a function to save important a specific images as records, so you can easily search for the images and transmit it to the PACS system in the hospital to help the medical staff in diagnosis.

The EXTRON Series are composed of a C-arm main body and a monitor cart. The C-arm main body is composed of an X-ray tube, a flat panel detector, a collimator, a generator, a touch panel, foot switch, hand switch and an XConsoleOP program, while the monitor cart is composed of a monitor, a thermal transfer printer, a mouse, a keyboard and an XConsole program.

The operating principle of the device is designed to expose the patient to X-ray beams. The range of X-ray irradiation are adjusted by the collimator.

X-rays can penetrate into the human body through a two-step conversion process.

X-ray photons are converted into light. The light is then converted into electrical signals through the sensor. The electrical charges are transmitted as the sensor output and converted into signals. These signals are digitized and captured by memory. The captured images are processed and displayed on the monitor. The displayed images can be saved or transmitted to an external storage device, such as a network printer.

Here's a breakdown of the acceptance criteria and the study information for the DRTECH Corporation EXTRON Series, based on the provided FDA 510(k) clearance letter.

It's important to note that this document is a 510(k) summary, which often emphasizes equivalence to a predicate device rather than presenting a novel clinical study with explicit acceptance criteria for a new device's performance. The "performance" here refers to demonstrating equivalence to the predicate, primarily through non-clinical testing and image quality assessment.

Acceptance Criteria and Device Performance for DRTECH Corporation EXTRON Series

Based on the provided 510(k) summary, the device's "acceptance criteria" are implied by its demonstration of substantial equivalence to predicate devices through compliance with established international and FDA-recognized consensus standards and a comparison of technological characteristics. The study primarily relies on non-clinical performance and a qualitative assessment of image quality.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: The document does not specify a numerical sample size for a "test set" in the traditional sense of a clinical trial. Instead, it states: "Sample clinical images using anthropomorphic phantoms representative of the indicated anatomies and populations have been taken for both the proposed devices (EXTRON 3/5/7) and the predicate devices (Veradius Unity and OEC 9900 ELITE)."
• Data Provenance: The data primarily comes from non-clinical testing using anthropomorphic phantoms. There is no mention of human subject data (clinical images from patients). The provenance of the phantoms themselves (e.g., manufacturer) or the exact location where these phantom images were acquired is not stated, but the manufacturer is based in South Korea. The study is retrospective in the sense that the comparison is made against existing predicate devices, but the image acquisition for the subject device is new. It is explicitly stated that "Clinical studies were not performed."

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

• Number of Experts: The document states: "These images have been reviewed and compared by qualified clinical experts." The exact number of experts is not specified.
• Qualifications of Experts: The experts are described as "qualified clinical experts." No specific qualifications (e.g., radiologist with X years of experience, board certification) are provided in this summary.

4. Adjudication Method for the Test Set

• Adjudication Method: The document states that the phantom images "have been reviewed and compared by qualified clinical experts." It does not specify a formal adjudication method (e.g., 2+1, 3+1 consensus). It appears to be a comparative review rather than a ground truth establishment process requiring formal adjudication for diagnostic accuracy.

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

• No MRMC study was done. This device is an X-ray imaging system, not an AI software intended to assist human readers in diagnosis. The study focused on demonstrating the image quality equivalence of the X-ray system itself. Therefore, the question about human reader improvement with/without AI assistance is not applicable.

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

• Not applicable. This device is an X-ray system, not an algorithm. The "standalone performance" implicitly refers to the performance of the X-ray system in producing images, which was assessed through non-clinical tests and qualitative image comparisons with predicate devices.

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

• For the image quality comparison, the "ground truth" for the phantom images is the inherent physical properties of the anthropomorphic phantoms themselves, as imaged by both the subject and predicate devices. The "qualified clinical experts" then assessed if the images produced by the subject device were "clinically comparable" to those from the predicate devices. There is no mention of pathology, outcomes data, or a formal expert consensus to establish a diagnostic ground truth in the traditional sense, as these were phantom images, not patient images.

8. The Sample Size for the Training Set

• Not applicable. This device is an X-ray imaging hardware system, not an AI/machine learning model that requires a training set.

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

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

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EXPD 114, EXPD 114P, EXPD 114G, EXPD 114PG Digital X-ray detector is indicated for digital imaging solution designed for providing general radiographic diagnosis of human anatomy. This device is intended to replace film or screen based radiographic systems in all general purpose diagnostic procedures. This device is not intended for mammography applications. It is intended for both adult and pediatric populations.

EXPD 114, EXPD 114G, EXPD 114P, EXPD 114PG are flat-panel type digital X-ray detector that captures projection radiographic images in digital format within seconds, eliminating the need for an entire x-ray film or an image plate as an image capture medium. EXPD 114, EXPD 114G, EXPD 114P, EXPD 114PG differs from traditional X-ray systems in that, instead of exposing a film and chemically processing it to create a hard copy image, a device called a Detector is used to capture the image in electronic form.

EXPD 114, EXPD 114G, EXPD 114P, EXPD 114PG are indirect conversion devices in the form of a square plate in which converts the incoming X-rays into visible light. This visible light is then collected by an optical sensor, which generates an electric charges representation of the spatial distribution of the incoming X-ray quanta.

The charges are converted to a modulated electrical signal thin film transistors. The amplified signal is converted to a voltage signal and is then converted from an analog to digital signal which can be transmitted to a viewed image print out, transmitted to remote viewing or stored as an electronic data file for later viewing.

The DRTECH Corporation's EXPD 114, EXPD 114G, EXPD 114P, EXPD 114PG Digital X-ray detectors were assessed for substantial equivalence to a predicate device (K223124). The company conducted non-clinical performance testing (bench tests) and a "Concurrence Study" for image quality to demonstrate this.

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

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" in a numerical or pass/fail format for the Concurrence Study beyond broad equivalence. Instead, it compares the performance of the subject device to the predicate device. The performance data mentioned primarily relates to technical specifications and general image quality assessment.

Note on DQE and MTF: The subject device's DQE for EXPD 114/P is slightly lower than the predicate EXPD 129P/86P (45% vs 50%). Similarly, the MTF for all subject devices is lower than the predicate (40% vs 45%). Despite these numerical differences, the overall conclusion states "basically equal or worth the predicate device" and that the device meets acceptance criteria. This suggests that the measured differences were considered clinically acceptable within the context of substantial equivalence.

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

The document states: "Our Concurrence Study for Image Quality was based on body parts (Chest, C-spine AP, L-spine AP, Shoulder AP, Pelvis AP, Extremity) to compare subject device and predicate device(K223124)."

• Sample Size: The exact number of images or cases analyzed in the Concurrence Study is not specified in the provided text. It only lists the anatomical sites included.
• Data Provenance: The document does not specify the country of origin of the data or whether the study was retrospective or prospective. It is a "Concurrence Study" which implies a direct comparison, likely of newly acquired images, but this is not explicitly stated.

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

• Number of Experts: "a qualified clinical expert" (singular) is mentioned.
• Qualifications of Experts: The expert is described as "qualified clinical expert." No further details on their specific qualifications (e.g., radiologist, years of experience, board certification) are provided.

4. Adjudication Method for the Test Set

• Adjudication Method: The document only mentions "a qualified clinical expert confirmed" the image quality. This strongly suggests a single-reader assessment without any explicit adjudication method (e.g., 2+1, 3+1 consensus).

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

• MRMC Study: Based on the description of "a qualified clinical expert confirmed," it appears that a formal MRMC comparative effectiveness study was not conducted. The assessment seems to be a qualitative comparison of image quality by a single expert.
• Effect Size of Human Reader Improvement: As an MRMC study was not indicated, there is no information on the effect size of how much human readers improve with AI vs. without AI assistance. The device is a digital X-ray detector, not an AI software intended for interpretation assistance.

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

• The device is a digital X-ray detector, not an AI algorithm. Therefore, the concept of "standalone performance" of an AI algorithm is not applicable in this context. The "performance" described relates to the physical characteristics of the detector (DQE, MTF, Resolution) and its ability to produce images of diagnostic quality.

7. Type of Ground Truth Used

• Type of Ground Truth: The ground truth for the Concurrence Study was based on the expert's subjective assessment of image quality compared to the predicate device, stating that "the image quality of the subject device is equivalent to that of the predicate device." This is a form of expert consensus, albeit with only one expert explicitly mentioned. It's not based on pathology or outcomes data.

8. Sample Size for the Training Set

• The document describes a device (digital X-ray detector), not a machine learning model. Therefore, the concept of a "training set" in the context of an AI/ML algorithm is not applicable.

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

• As the device is not an AI/ML algorithm, the concept of "training set ground truth" is not applicable.

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The Digital X-ray detector, EXPD-N Series, is designed for use in digital imaging solutions for general radiographic diagnosis of human anatomy. This device is intended for use in all general diagnostic procedures, replacing film or screen-based radiographic systems for both adult and paediatric patients. It is not intended for use in mammography.

In comparison to existing devices, the new detectors incorporate a Flexible a-Si in the TFT material within the panel. The primary difference from the conventional glass a-Si panel is that the electronic circuits, such as silicon, are deposited on a plastic substrate instead of a glass substrate during the manufacturing of the TFT panel. Since only the material of the substrate on which the silicon is deposited changes, the overall image performance remains unaffected. Another difference is the pixel pitch. While existing products feature only a pixel pitch of 140μm, the new models include an option with a pixel pitch of 100um. The resolution of an X-ray detector has a significant impact on MTF (Modulation Transfer Function) and sensitivity.

This medical device submission is for an X-ray detector, not an AI/ML device. Therefore, the typical acceptance criteria and study requirements for AI/ML devices, such as those related to multi-reader multi-case studies, standalone performance, and ground truth establishment with expert consensus or pathology, are not applicable here.

The submission focuses on establishing substantial equivalence to a predicate device based on technical characteristics and physical performance, confirming it is suitable for general radiographic diagnosis.

Here's a breakdown of the provided information, tailored to the context of a non-AI X-ray detector:

1. Table of Acceptance Criteria and the Reported Device Performance

The acceptance criteria are implicitly defined by demonstrating substantial equivalence to the predicate device in terms of technical characteristics and performance metrics relevant to X-ray image quality. The table below compares the subject device's performance to the predicate device, highlighting where performance is similar or improved.

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XERO a is an Hand-held intraoral dental x-ray equipment to produce X-ray images using intraoral image receptors. It is indicated for use by a dental technician for both adult and pediatric patients. This equipment is used in a professional healthcare facility environment.

XERO a is an Hand-held intraoral dental x-ray equipment to produce X-ray images using intraoral image receptors. It is indicated for use by a dental technician for both adult and pediatric patients. This equipment is used in the professional healthcare facility environment. But the image detectors (an integral part of a complete dental system) are not part of the device. The operation principle of the device involves emitting x-ray source when a high voltage is supplied to the X-ray tube assembly, which frees electrons from the cathode. They hit anode to produce X-rays. The device acquires images by emitting X-rays continuously on the human tooth. And the functions of the dental generator XERO a are supported by firmware. This is of basic firmware documentation level, and there is no external data exchange port in the device. Moreover, the XERO a is not wireless. The subject dental system is not a cyber device.

The provided text describes the 510(k) premarket notification for the XERO - C and XERO α intraoral dental X-ray equipment. However, the document does not contain information about a study proving the device meets acceptance criteria related to AI/algorithm performance, multi-reader multi-case (MRMC) studies, or standalone algorithm performance.

The document primarily focuses on demonstrating substantial equivalence to a predicate device (Vatech Co., Ltd.'s EzRay Air Portable, K200182) through:

• Technological characteristics comparison: Showing similar design, mechanical features, electrical specifications, and adherence to relevant safety standards.
• Non-clinical data summary: Listing compliance with international and FDA-recognized consensus standards for medical electrical equipment safety, electromagnetic compatibility, usability, and specific requirements for dental X-ray equipment.
• Statement on clinical data: Explicitly stating that clinical studies are unnecessary to validate the safety and effectiveness of the device.

Therefore, I cannot provide the requested information regarding acceptance criteria, study details, sample sizes, expert involvement, adjudication methods, MRMC studies, standalone performance, or ground truth establishment for an AI/algorithm. This information is not present in the provided 510(k) summary.

The device described is a medical imaging device (X-ray equipment), not an algorithm or AI software for image analysis that would typically have the kind of performance metrics and study designs you've asked about.

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The EXSYS DEXi is a diagnostic X-ray system intended for use in generating radiographic images of human anatomy for general purpose. The system obtains necessary information of patient's anatomical structure by an image processing (workstation) after process of examination using radiation exposure with DR. This system is not intended for mammography applications.

The EXSYS DEXi is a diagnostic X-ray system intended for use in generating radiographic images of human anatomy for general purpose. The system obtains necessary information of patient's anatomical structure by an image processing (workstation) after process of examination using radiation exposure with DR. This system is not intended for mammography applications.

The EXSYS DEXi composed of a x-ray generator, tube, collimator, tube stand, bucky stand, patient table, flat panel detector and console.

This FDA 510(k) summary does not contain information about acceptance criteria and device performance as it pertains to AI/ML or image analysis aspects. The document focuses on the substantial equivalence of the modified EXSYS DEXi diagnostic X-ray system to a previously cleared predicate device (K233530) based on hardware and software updates, and compliance with general safety and performance standards for X-ray systems.

Specifically, the document refers to non-clinical data and verification and validation testing demonstrating compliance with various international and FDA-recognized consensus standards (e.g., IEC 60601 series for medical electrical equipment, ISO 14971 for risk management, IEC 62304 for medical device software, UL ANSI 2900-1 and IEC 81001-5-1 for cybersecurity). It states, "The test results support that all the specifications have met the acceptance criteria. Verification and validation testing were found acceptable to support the claim of substantial equivalence." However, it does not provide a table of acceptance criteria with reported device performance metrics in an AI/ML context, nor does it describe specific studies that would typically prove such performance (e.g., standalone performance studies, MRMC studies, details on ground truth establishment for a diagnostic algorithm, sample sizes for test/training sets relevant to AI performance).

The "technological characteristics" table (Table 1) compares design parameters of the subject device (new models of EXSYS DEXi) with the predicate device, highlighting additions like new collimators, mechanical parts, detectors, and software (EConsole2). The discussion column for these additions generally states, "The system has been tested and there is 'No negative impact on safety or efficacy' and there are no new potential or increased safety risks concerning this difference." This refers to overall system safety and performance in line with a general X-ray system, not specific AI/ML diagnostic performance.

Therefore, based on the provided document, the following information cannot be extracted:

1. A table of acceptance criteria and the reported device performance (for AI-specific functions): Not provided. The document states general compliance with standards and "test results support that all the specifications have met the acceptance criteria," but does not detail these criteria or performance metrics specific to an AI component's diagnostic accuracy, sensitivity, specificity, etc.
2. Sample size used for the test set and the data provenance: Not provided for AI/ML performance evaluation.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not provided for AI/ML performance evaluation.
4. Adjudication method for the test set: Not provided for AI/ML performance evaluation.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size: Not mentioned.
6. If a standalone (algorithm only without human-in-the-loop performance) was done: Not mentioned.
7. The type of ground truth used: Not specified for AI/ML performance evaluation.
8. The sample size for the training set: Not provided for AI/ML performance evaluation.
9. How the ground truth for the training set was established: Not provided for AI/ML performance evaluation.

The document states, "Clinical studies are unnecessary to validate the safety and effectiveness of the Stationary x-ray system, EXSYS DEXi, the subject of this 510(k) notification," further indicating that specific performance data from clinical trials or detailed AI algorithm validation studies (which typically involve such criteria) are not included in this submission summary. The software updates mentioned (EConsole2) were previously cleared via K240243, suggesting that any specific performance data for that software might be found in its own 510(k) submission, but not in this document.

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EConsole2 is indicated for use in general radiographic images of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures (excluding fluoroscopic, and mammographic applications). It is intended for both adult and pediatric populations.

EConsole2 is digital radiography operating console software. EConsole2 provides an integrated solution for X-ray projection. It integrates with the digital detector. Furthermore, EConsole2 acquires and processes images. In addition, it complies with DICOM standards and is able to transmit and receive data with the PACS system, and print images through the DICOM printer.

This document, a 510(k) summary, lacks the detailed information necessary to fully address all aspects of your request regarding acceptance criteria and the study proving the device meets them. Specifically, it does not contain the specifics of a study conducted to prove the device's performance against acceptance criteria for an AI/CAD/software-as-a-medical-device (SaMD) product.

The EConsole2 is described as a Radiological Image Processing System (software), which "acquires and processes images" and "can utilize and transfer the digitalizing x-ray images for radiography diagnostic." However, the summary focuses on its equivalence to a predicate device (EConsole1) primarily based on shared software functions, DICOM compliance, and safety standards adherence.

The key statement regarding performance is: "The test results support that all the specifications have met the acceptance criteria. Verification and validation testing were found acceptable to support the claim of substantial equivalence." However, it does not provide the details of these "test results" or "acceptance criteria" in the context of diagnostic performance as would be expected for an AI-powered image analysis device.

The document explicitly states: "Clinical studies are unnecessary to validate the safety and effectiveness of the Radiological Image Processing System, EConsole2, the subject of this 510(k) notification." This strongly suggests that an MRMC or standalone performance study measuring diagnostic accuracy (e.g., sensitivity, specificity, AUC) was not performed or submitted as part of this 510(k) given their determination that clinical data was "unnecessary."

The "ECE Function" is mentioned as a new "optional" image processing feature that "normalizes the image brightness and contrast regardless of the exposed x-ray dose." While this is a form of image processing, the summary doesn't describe it as an AI/CAD function with specific performance metrics against an established ground truth for diagnostic purposes.

Therefore, I cannot provide the requested information about acceptance criteria, detailed study results, sample sizes, expert qualifications, or ground truth establishment relevant to an AI/CAD device's diagnostic performance. The 510(k) appears to be based on substantial equivalence for a medical image management and processing system rather than demonstrating the performance of a novel AI-driven diagnostic aid.

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The EXPD 4343S Digital X-ray detector is indicated for use in generating radiographic images of human anatomy. This device is intended to replace film or screen based radiographic systems in all general purpose diagnostic procedures on general populations. This device is not intended for mammography applications.

The EXPD 4343S Detector is a square plate-shaped indirect conversion device that converts incoming X-rays into visible light. This visible light is subsequently captured by an optical sensor, which produces an electric charge representation of the spatial distribution of the incoming X-ray quanta. Through thin film transistors, the charges are transformed into a modulated electrical signal is amplified, then changed from an analog to digital form (from voltage to signal) so that it can be printed out, sent for remote viewing, or saved as an electronic data file for later viewing. The subject device features two matrix arrays strategically positioned in an overlapping configuration within the housing, facilitating the generation of multiple images with a single exposure. Array #1 works similarly to the predicate device by detecting incident X-ray photons and converting them into electrical signals. Simultaneously, Array #2 captures unabsorbed X-rays from Array #1 after passing through it. As a consequence of this configuration, the subject device has the capacity to produce three distinct images: a Standard image produced by Array #1, a second image (a soft tissue image) obtained by processing the Standard image using Console software, and a third image (a bone image) generated by processing the image obtained from Array #2.

The provided text describes a 510(k) summary for the EXPD 4343S Digital X-ray detector, claiming substantial equivalence to a predicate device (EXPD 4343P). However, the document does not contain specific acceptance criteria or a detailed study proving the device meets those criteria with quantitative values.

Instead, it presents a comparison of technological characteristics and states that:

• "an overall assessment of the subject device's essential performance revealed that it is basically on the equivalent level with the predicate device."
• "the clinical image evaluation was performed to assess the device's clinical performance and average score of evaluation results by two experienced physicians demonstrated that the device is prove to be effective in clinical practice."
• "The result showed that images acquired by the subject device were generally in diagnostic quality, and evaluators stated that the device proved to be effective use in clinical practice."

Without specific numerical acceptance criteria and a structured study result, it's impossible to fill out the requested table and answer many of the specific questions.

Based on the information available, here's what can be extracted and what remains unknown:

Acceptance Criteria and Device Performance (Based on available comparative data)

While no explicit acceptance criteria are provided in the sense of pass/fail thresholds, the document implies that the device's performance is deemed acceptable if it is "basically on the equivalent level" to the predicate device and found to be "effective in clinical practice" and "of diagnostic quality" by physicians.

Study Details (Inferred and Missing Information)

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

   • Sample Size: Not specified. The document states "Chest PA imaging sets used for evaluation".
   • Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). It just mentions "Chest PA imaging sets".

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

   • Number of Experts: Two experienced physicians.
   • Qualifications: "experienced physicians." No further detail regarding their specialty (e.g., radiologist) or years of experience is provided.

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

   • Adjudication Method: Not specified. It mentions an "average score of evaluation results" and that "visual system was adapted with each physician's overall assessment," which suggests independent assessment followed by some form of averaging or consensus, but the specific method is not detailed.

4. 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 as described for AI assistance. The study described is a clinical image evaluation of the device's output by physicians, not an evaluation of AI assistance to human readers. The device itself is an X-ray detector, not an AI diagnostic tool.

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

   • Standalone Performance: Not applicable in the context of an X-ray detector's primary performance. The "soft tissue image" and "bone image" are generated by processing, which implies an algorithm, but the performance evaluation described is of the final image quality and diagnostic effectiveness by human readers. There is no mention of a quantitative standalone performance evaluation of these derived images in terms of specific diagnostic tasks.

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

   • Ground Truth: Expert assessment/consensus from the two experienced physicians on the "diagnostic quality" and "effectiveness in clinical practice" of the images generated by the device. It is not based on pathology or outcomes data.

7. The sample size for the training set:

   • Training Set Sample Size: Not applicable. This document is for an X-ray detector, not an AI algorithm that requires a training set. The "soft tissue" and "bone" image processing is fundamental to the detector's output, not a separate AI application.

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

   • Training Set Ground Truth: Not applicable, as no training set for an AI algorithm is mentioned.

Summary of what's provided vs. what's missing:

The document provides basic comparative technical specifications and a high-level qualitative summary of a clinical image evaluation. It lacks detailed quantitative results, specific acceptance criteria for image quality, detailed expert qualifications, sample sizes, and adjudication methods that would be expected in a rigorous study for an AI-enabled diagnostic device. Given that the device is a digital X-ray detector, the focus of the provided information is on demonstrating the equivalence of its imaging capabilities to a predicate device, rather than the performance of a diagnostic AI algorithm.

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The EXPD 4343D and EXPD 3643D Digital X-ray detector are indicated for digital imaging solution designed for providing general radiographic diagnosis of human anatomy. This devices are intended to replace film or screen based radiographic systems in all general purpose diagnostic procedures. This devices are not intended for mammography applications.

The EXPD 4343D, EXPD 3643D are flat-panel type digital X-ray detector that captures projection radiographic images in digital format within seconds, eliminating the need for an entire x-ray film or an image plate as an image capture medium. EXPD 4343D, EXPD 3643D differs from traditional X-ray systems in that, instead of exposing a film and chemically processing it to create a hard copy image, a device called a Detector is used to capture the image in electronic form.

The EXPD 4343D, EXPD 3643D Detector are indirect conversion devices in the form of a square plate in which converts the incoming X-rays into visible light. This visible light is then collected by an optical sensor, which generates an electric charges representation of the incoming X-ray quanta.

The charges are converted to a modulated electrical signal thin film transistors. The amplified signal is converted to a voltage signal and is then converted from an analog to digital signal which can be transmitted to a viewed image print out, transmitted to remote viewing or stored as an electronic data file for later viewing.

The provided FDA 510(k) summary for the DRTECH EXPD 4343D and EXPD 3643D digital X-ray detectors focuses on demonstrating substantial equivalence to a predicate device (K192400), rather than a clinical study establishing new acceptance criteria for diagnostic performance.

Therefore, many of the requested items related to clinical studies, expert-established ground truth, and human reader performance are not applicable in this context.

Here's the information that can be extracted or deduced from the document:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria here are based on equivalence to the predicate device's measured physical performance parameters (DQE and MTF) and compliance with recognized standards. The "study" proving this is a bench test report.

The document states: "According to bench test report, it is proved that the DQE and MTF of predicated device and subject device are basically equal or worth than the predicate device. As a result, subject devices performance is equal or worth than the predicate device."
This indicates that the acceptance criteria for DQE was for the subject device to be at least equal to or better than the predicate, and for MTF, it was to be at least equal to or better than the predicate.

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

• Sample Size: Not applicable. This was a bench test on the physical performance of the X-ray detectors, not a study involving patient data or a test set of images.
• Data Provenance: Not applicable, as it's a bench test. The manufacturer (DRTECH Corporation) is located in South Korea.

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

• Not applicable. The "ground truth" here is objective physical measurements (DQE, MTF, Resolution) taken from the devices themselves, not expert interpretation of diagnostic images.

4. Adjudication method for the test set

• Not applicable. No diagnostic images or expert reviews were involved in establishing the performance parameters for this submission.

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

• Not applicable. This submission does not describe a comparative effectiveness study involving human readers or AI. It's for a digital X-ray detector (hardware).

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

• Not applicable. This is a hardware device (digital X-ray detector), not an algorithm or AI software for image analysis.

7. The type of ground truth used

• Physical measurements/Instrumental data: DQE, MTF, and Resolution measurements are determined through standardized test procedures defined by documents like IEC 62220-1-1.

8. The sample size for the training set

• Not applicable. This submission does not involve a training set as it is not an AI/ML device.

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

• Not applicable. This submission does not involve a training set.

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EConsole1 is indicated for use in general radiographic images of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures (excluding fluoroscopic, and mammographic applications).

The main features of this software are controlling and interfacing the detector, controlling the x-ray generator acquisition settings, storing acquired images, data management and image processing.

Radiological Image Processing Software, EConsolel is complete digital image processing console software specialized for the digital X-ray detector series developed by DRTECH Corporation.

EConsole1 not only processes the acquired images but also complies with DICOM standards which allow the user to transmit and receive data with the PACS system and print images through the DICOM printer.

The provided text does not contain the detailed information necessary to fully answer the request regarding specific acceptance criteria, study methodologies, and performance results for the EConsole1 device.

The document is a 510(k) summary for a medical image management and processing system (EConsole1). Its primary purpose is to demonstrate substantial equivalence to a predicate device, not to present a comprehensive study proving specific performance metrics against pre-defined acceptance criteria for, for example, diagnostic accuracy of an AI component.

Here's a breakdown of what can be extracted and what is missing, based on the provided text:

What is Present:

• Device Name: EConsole1
• Device Type: Radiological Image Processing Software (Medical Image Management and Processing System)
• Intended Use: General radiographic images of human anatomy, to replace film/screen systems in general-purpose diagnostic procedures (excluding fluoroscopic, angiographic, and mammographic applications). Its main features involve controlling and interfacing the detector, controlling X-ray generator acquisition settings, storing acquired images, data management, and image processing.
• Regulatory Classification: Class II, Product Code LLZ.
• Predicate Device: FEEL-DRCS (K110033) and a reference device EConsole1 (K152172).
• Non-Clinical Data: Mentions compliance with standards (IEC 62304, NEMA PS 3.1-3.20 DICOM, ISO 20417, IEC 62366-1, ISO 14971) and software verification/validation activities (code, module, integration, dynamic tests, risk analysis).
• Cybersecurity: States conformity to FDA guidance for managing cybersecurity.
• Conclusion: The device is substantially equivalent to the predicate, passed V&V testing, and performs as intended without new safety risks.

What is NOT Present (and therefore cannot be answered from this document):

1. A table of acceptance criteria and the reported device performance: The document states, "The test results suggest that all software specifications meet the acceptance criteria." However, it does not list what those specific acceptance criteria were (e.g., minimum accuracy, sensitivity, specificity, processing speed, image quality metrics) nor does it provide the reported numerical values for the device's performance against these criteria.
2. Sample size used for the test set and the data provenance: No information on the number of images/cases used specifically for a "test set" to evaluate performance. No details on the country of origin or whether the data was retrospective or prospective.
3. Number of experts used to establish the ground truth for the test set and their qualifications: No mention of expert review or ground truth establishment for performance data, as specific clinical performance evaluation data is not provided.
4. Adjudication method for the test set: Not applicable, as detailed performance test set data is not provided.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done: The document explicitly states "Clinical studies are unnecessary to validate the safety and effectiveness of the software in EConsole1". This indicates no MRMC study was conducted or deemed necessary for this 510(k) submission.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: While general "verification and validation" and "non-clinical tests" are mentioned, no specific standalone performance metrics for an AI algorithm (e.g., for disease detection) are provided. The device described appears to be an image processing and management system, not an AI for diagnostic interpretation.
7. The type of ground truth used: Not applicable, as no specific performance study against a diagnostic ground truth is described. The V&V described are for software functionality, not diagnostic accuracy.
8. The sample size for the training set: Not applicable, as no AI model requiring a training set for diagnostic classification/segmentation is described. The software's function is image management and processing.
9. How the ground truth for the training set was established: Not applicable for the same reason as above.

In summary, the provided FDA 510(k) summary is for an image management and processing system, not an AI diagnostic algorithm. Therefore, it focuses on software V&V, functional equivalence, and compliance with standards rather than clinical performance metrics, ground truth establishment, or human reader studies typically associated with AI-powered diagnostic devices.

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