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The Venclose System (Venclose digiRF Generator with EVSRF Catheter) is intended for endovascular coagulation of blood vessels in patients with superficial vein reflux.

The Venclose™ digiRF Generator is a multi-voltage energy delivery system with touchscreen control that automatically sets the non-adjustable treatment parameters for the catheter to be used with the generator (time, temperature, etc.), which is connected via a triaxial catheter connector port. The Venclose™ digiRF Generator is intended to be used with Venclose™ RF Catheter(s) (either the Venclose™ EVSRF Catheter or the Venclose™ Maven Catheter) as a system. The Venclose™ RF System uses resistive radiofrequency ablation via energy delivery to heat the wall of an incompetent vein with temperature-controlled RF energy to cause irreversible luminal occlusion, followed by fibrosis and ultimately resorption of the vein.

The scope of this 510(k) is only the Venclose™ digiRF Generator as the generator software has been modified. The Venclose™ Maven Catheter is not in the scope of this submission as the changes discussed within this submission are only applicable to the Venclose™ digiRF Generator as used with the Venclose™ EVSRF Catheter. There is no change to the Venclose™ EVSRF Catheter, as previously cleared via K160754.

Based on the provided 510(k) Clearance Letter, the device in question is the "Venclose digiRF Generator" and the modifications are related to its software when used with the "Venclose EVSRF Catheter." The clearance letter states that the scope of this 510(k) is only the Venclose digiRF Generator as the generator software has been modified, and there is no change to the Venclose EVSRF Catheter.

The document explicitly states that the device is an "Electrosurgical Cutting And Coagulation Device And Accessories" and the testing performed was "Software Verification and Validation." There is no mention of a study involving human subjects, interpretation of medical images by experts, or any kind of diagnostic performance evaluation typically seen with AI/ML-based diagnostic devices.

Therefore, many of the requested criteria regarding acceptance criteria for diagnostic performance, ground truth establishment, expert adjudication, MRMC studies, and training/test set details for AI/ML models are not applicable to this submission. This is a clearance for a software modification to an electrosurgical generator, not a medical imaging AI/ML diagnostic aid.

Here's the breakdown of what can be gathered from the provided text, addressing the points where information is available and indicating where it is not applicable or not provided.

Device: Venclose digiRF Generator (VCRFG1) with Venclose EVSRF Catheter (VC10A256F60, VC10A256F100)
Type of Modification: Software Modification to the Venclose digiRF Generator.

1. Table of Acceptance Criteria and Reported Device Performance

Given that this is a software modification to an electrosurgical generator, the acceptance criteria are not related to diagnostic performance metrics like sensitivity, specificity, or AUC, but rather to software functionality, safety, and effectiveness. The document states that internal risk assessments procedures were used.

Note: Specific numerical acceptance values are not detailed in this public 510(k) summary, as they are typically proprietary and part of detailed engineering and software validation reports submitted to the FDA.

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

This is not applicable in the context of an AI/ML diagnostic performance test set. The "test set" here refers to the software verification and validation activities. The document does not specify exact "sample sizes" (e.g., number of test cases or iterations) for the software testing. Data provenance is also not applicable in the context of clinical data for AI/ML, as the testing relates to engineering and software validation.

• Software Verification and Validation: This typically involves rigorous testing against defined requirements, including unit testing, integration testing, system testing, and perhaps regression testing. The "sample size" would relate to the number of test cases executed, input parameters varied, and error conditions simulated. Specific numbers are not provided in this summary.
• Data Provenance: Not applicable as no clinical data for diagnostic performance was used in this clearance for a software modification to an electrosurgical device.

3. Number of Experts Used to Establish Ground Truth and Qualifications

Not applicable. Ground truth, in the sense of expert annotation of medical data, is not established for an electrosurgical generator's software modification. The "ground truth" for this device's performance would be its adherence to engineering specifications and its ability to safely and effectively deliver RF energy for its intended purpose, as measured by calibrated equipment and functional tests.

4. Adjudication Method for the Test Set

Not applicable. Adjudication methods (e.g., 2+1, 3+1) are for consensus building among human experts for ground truth label generation in diagnostic studies. This process is not part of software verification and validation for an electrosurgical device.

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

No. An MRMC study is relevant for evaluating the impact of an AI/ML diagnostic device on human reader performance. This 510(k) is for a software modification to an electrosurgical generator, not a diagnostic AI/ML device. Therefore, no MRMC study was performed or reported.

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

No. "Standalone performance" refers to the diagnostic accuracy of an AI/ML algorithm by itself. This device is an electrosurgical generator. Its performance is measured by its ability to generate and deliver RF energy according to specifications, not by its diagnostic capabilities. The software's "performance" was evaluated through verification and validation activities.

7. The Type of Ground Truth Used

Not applicable in the AI/ML diagnostic sense. For this device, the "ground truth" for software validation would be derived from:

• Design Specifications: The documented requirements and expected behavior of the software and the device.
• Predicate Device Performance: The existing performance characteristics of the previously cleared predicate devices, to which the modified device is being compared for substantial equivalence.
• Engineering Standards and Measurements: Data from calibrated test equipment, electrical measurements, temperature readings, and time controls.

8. The Sample Size for the Training Set

Not applicable. This device is not an AI/ML algorithm that requires a training set. The software was likely developed using traditional software engineering methodologies.

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

Not applicable. As there is no AI/ML training set, there is no ground truth to establish for such a set.

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The EndoSound Vision System (EVS), when affixed to an endoscope, is intended to provide ultrasonic visualization of, and ultrasound guided therapeutic access to the Upper Gastrointestinal Tract including but not restricted to the organs, tissues, and subsystems: Esophagus, Stomach, Duodenum and underlying areas. The EVS, mounted on an endoscope, is introduced orally when indications consistent with the requirement for a GI procedure are met.

The EVS is a prescription only device to be used by a qualified physician.

The clinical environments where the system can be used include clinics, hospitals, and ambulatory surgery centers.

The EndoSound Vision System® (EVS) is an add-on ultrasound system designed to attach externally to an upper gastrointestinal (GI) endoscope (gastroscope/upper (EGD) endoscope). Once attached, it temporarily converts an EGD endoscope to a fully capable EUS endoscope. The EVS consists of:

• an electronic ultrasound beamformer/image analyzer (EVSScanner),
• a reusable transducer assembly (Ultrasound Transducer Module (UTM)), .
• . a cable/connector to interface the transducer to the beamformer (Transducer Extension Cable (TEC)).
• a sterile, disposable mounting kit with an operator control mechanism (Ultrasound Disposable Kit (UDK-T)) used to externally affix the EVS to a standard gastroscope/upper (EGD) endoscope and to provide needle and transducer angle control.

When attached to an upper GI endoscope, the EVS enables real-time ultrasound imaging, ultrasound quided needle aspiration and other EUS quided procedures within the upper gastrointestinal tract and surrounding organs. Native functions of the gastroscope including video imaging, articulation insufflation and suctioning are available after attachment of the EVS device. The system works in tandem with a PC computer/monitor/tablet connected via a USB 3.0 cable that runs custom software (EVSViewer) and provides the user interface and ultrasound display.

The provided document is a 510(k) summary for the EndoSound Vision System (EVS). It describes the device, its intended use, comparison to predicate devices, and various safety and performance tests. However, it does not contain details about an AI/algorithm performance study with specific acceptance criteria. The "study that proves the device meets the acceptance criteria" refers to a general series of verification and validation tests, and an animal study, primarily focused on the physical and functional aspects of the ultrasound system itself and not explicitly on the performance of a software algorithm that performs diagnostic analysis or image interpretation to address specific clinical outcomes (e.g., detecting pathologies).

The EVSViewer software mentioned primarily handles image processing and display, and integration with the EVS UTM probe, rather than performing AI-driven diagnostic interpretations. The document states: "The EVSViewer is the software run on a PC/monitor which processes the ultrasound data received from the EVSScanner module and displays the ultrasound image. It also functions to produce the user interface on the computer monitor." It further details minor changes to the software related to probe integration, recognition, usage counter, field of view lock, and splash screen/icons, none of which suggest an AI component for image analysis or diagnostic support.

Therefore, for the specific questions regarding acceptance criteria and a study proving an AI/algorithm-based device meets these criteria, the provided text does not offer the necessary information. It details traditional medical device testing for an ultrasound system.

However, I can extract information about the general safety and effectiveness studies conducted for the device as a whole, based on the provided text.

Here's a summary of the device's acceptance criteria and the studies mentioned, as much as can be gleaned from the text:

Device: EndoSound Vision System (EVS) - an add-on ultrasound system for upper GI endoscopes.

Acceptance Criteria and Reported Device Performance (General Device Performance, not AI-specific):

The document broadly refers to "Verification and validation testing" and "Successful results of testing" but does not explicitly list quantitative acceptance criteria in a table format for specific performance metrics of an AI. Instead, it discusses the device's functional equivalence and safety.

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

• Test Sets:
  • Biocompatibility: Not specified beyond "components of the EVS in contact with the patient."
  • Reprocessing/Sterilization: Not specified (e.g., number of units tested).
  • Animal Testing: 3 live animal models. The regimen was implemented four times in each animal. Data provenance is not specified beyond "GLP lab study." It is a prospective study.
  • Phantom and In Vivo Study (for Ultrasound Scanning Comparison): Not explicitly quantified, but mentioned that "Phantom and in vivo study comparison images show similar scanning resolution, image quality and ultrasound image performance."
  • Verification and Validation Testing (general): Not specified.

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

• Animal Testing: "An expert endoscopist as the user under the supervision of the GLP quality director and the study director." The specific number and exact qualifications beyond "expert" are not detailed.
• For other tests (biocompatibility, reprocessing, etc.), expert interpretation is inherent in GLP lab studies, but specific numbers and qualifications of human experts establishing "ground truth" (in the sense of diagnostic image interpretation) are not mentioned.

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

• Not explicitly described for any of the studies mentioned. The animal study mentions "an expert endoscopist as the user under the supervision of the GLP quality director and the study director," implying some form of oversight, but not a specific adjudication protocol for diagnostic ground truth.

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:

• No MRMC study was described. The document does not mention human reader studies, nor does it describe AI assistance for diagnostic tasks. The focus is on the substantial equivalence of the ultrasound imaging system and its physical components.

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

• No standalone algorithm performance study was described. The "EVSViewer software" is described as processing and displaying ultrasound data, not performing automated diagnostic or interpretive functions.

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

• Animal study: "Clinically relevant damage was identified, either radiologically, endoscopically, or macroscopically" suggesting a combination of imaging and direct visual/physical assessment as ground truth for safety. For performance, it was a comparative assessment of image quality and device behavior relative to a predicate device.
• Other tests relying on established and regulated testing methodologies (e.g., ISO 10993 for biocompatibility, IEC 60601 for electrical safety).

7. The sample size for the training set:

• Not applicable. The document does not describe the development or training of an AI algorithm, so there is no training set mentioned. The "firmware modification" and "EVSViewer software" changes are related to device control and display, not AI.

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

• Not applicable. As no AI training set is mentioned, no ground truth establishment process for it is described.

In summary: The provided 510(k) summary focuses on the substantial equivalence of the EndoSound Vision System as an ultrasound imaging device to existing predicate devices, and its safety. It does not contain information about an AI/algorithm-based diagnostic device, nor does it specify AI-related acceptance criteria or studies to prove such criteria. The reported studies are typical for a hardware and basic software medical device submission.

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The Pure-Vu EVS System is intended to connect to standard or slim colonoscopes to help facilitate intra-procedural cleansing of the GI tract by irrigating or cleaning irrigation fluid (water), feces, and other bodily fluids and matter, e.g. blood.

It is for use only by trained medical personnel located in hospitals, clinics and doctors' offices.

The Pure-Vu EVS System enables cleaning of the GI tract during endoscopy using a standard or slim colonoscope with a length of 1630mm - 1710mm and an outer diameter range of 11.7mm - 13.2mm or gastroscope with a length of 950 – 1030mm and an outer diameter of 9.2 - 10mm The EVS Flex Channel , which fits alongside the endoscope and is connected to an external Workstation, generates fluid and gas to break up debris The debris & fluids are removed through the suction channel of the EVS Flex Channel into an external waste container/bag.

The Pure-Vu EVS System consists of the following main components:
Disposable device which includes a Flex Channel section and an Umbilical Section (US) - The EVS Flex Channel fits alongside the endoscope to allow a physician to cleanse the GI tract and is connected to the external Workstation via a disposable US. Workstation (WS) – The Workstation [WS] is reusable and supplies an irrigation mixture of water or saline and gas, and evacuates debris and fluids. The Workstation includes the following components: A monitoring & Control Unit that controls the delivery of irrigation fluids and gas into the GI tract, and suction of fluid and matter from the GI tract. Irrigation Bag/Bottle (saline or water) which is connected to the irrigation line. Waste Containers for collecting the GI content & fluids that are suctioned from the GI tract through the suction lines. Inlet Module that includes pumps and regulators enabling fluid & gas flow into the cleansing device. Outlet Module that includes pumps to evacuate fluid and matter from the GI tract. A foot pedal activates the cleansing, suction and purging functions, and enables switching between cleansing modes used by the physician.

The provided text describes modifications to the Pure-Vu EVS System and its substantial equivalence to predicate devices, but it does not contain the specific acceptance criteria or a study proving the device meets those criteria, nor any of the detailed information requested.

The document is a 510(k) summary for the Pure-Vu EVS System, a device intended for intra-procedural GI tract cleansing during endoscopy. It focuses on demonstrating substantial equivalence to previously cleared devices (Pure-Vu System K220007 and K210981) due to design changes.

Here's an analysis of the provided information against your requested points:

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

• Not found. The document includes tables comparing the technological characteristics of the subject device with its predicate, such as dimensions, flow rates, and materials. However, it does not present specific acceptance criteria (e.g., minimum pressure tolerance, maximum steering angle impact) with corresponding reported performance values against those criteria. It states that "Design verification and validation testing concluded that the design changes have no impact on the Pure-Vu System performance," but does not provide the specifics of that performance or the criteria used to judge it.

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

• Sample size: For "System Validation Testing," it states "Validation testing for the entire Pure Vu EVS system (WS and disposable devices) was performed with 6 physicians." This is the only mention of human-involved testing with a specific number.
• Data provenance: Not specified.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

• Not found. There is no mention of establishing ground truth or the qualifications of the 6 physicians involved in the validation testing. Their role is described as performing "Validation testing," not establishing ground truth.

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

• Not found. No adjudication method is 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

• Not found. This is not an AI-powered device, and no MRMC study or comparative effectiveness study involving human readers or AI assistance is mentioned. The device is a physical system for irrigation and cleaning.

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

• N/A. The device is a mechanical/electro-mechanical system, not an algorithm, so this concept does not apply.

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

• Not found. The document does not describe the establishment or type of ground truth for any evaluation.

8. The sample size for the training set

• N/A. As this is not an AI/machine learning device, there is no concept of a "training set."

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

• N/A. As there is no training set, this is not applicable.

In summary, the provided document outlines the technical specifications, modifications, and verification/validation testing conducted for the Pure-Vu EVS System to demonstrate its substantial equivalence to previously cleared devices. It details various engineering tests (environmental conditioning, dimensions, head pull, steering, pressure, bond strength, software verification) and mentions system validation with 6 physicians. However, it does not provide the kind of detailed clinical study data, acceptance criteria, or ground truth information typically associated with performance evaluation against specific clinical endpoints or for AI/software-as-a-medical-device.

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The Pure-Vu EVS System is intended to connect to standard and slim colonoscopes to help facilitate intra-procedural cleaning of a poorly prepared colon by irrigating or cleaning the irrigation the irrigation fluid (water), feces and other bodily fluids and matter, e.g. blood.

It is for use only by trained medical personnel located in hospitals, clinics and doctor offices.

The Pure-Vu EVS System enables colon cleaning during colonoscopy using a standard or slim colonoscope with a length of 1630mm – 1710mm and an outer diameter range of 11.7mm – 13.7mm. The Oversleeve, which fits over the colonoscope and is connected to an external Workstation, generates fluid and gas to break up feces. The fecal matter & fluids are removed through the suction channel of the Oversleeve into an external waste container.
The Pure-Vu EVS System consists of the following main components:
Oversleeve (OS) and Umbilical Section (US) - The disposable Oversleeve is mounted on Standard or Slim commercially available colonoscopes to allow a physician to cleanse the GI tract and is connected to the external Workstation via a disposable US.
Workstation (WS) – The Workstation [WS] is reusable and supplies an irrigation mixture of water or saline and gas, and evacuates fecal material and fluids. The Workstation includes the following components:
A monitoring & Control Unit that controls the delivery of irrigation fluids and gas into the GI tract, and suction of fluids and feces from the colon.
Irrigation Bag/Bottle (saline or water) which is connected to the irrigation line.
Waste Containers for collecting the GI content & fluids that are suctioned from the GI tract through the suction lines.
Inlet Module that includes pumps and regulators enabling fluid & gas flow into the cleansing device.
Outlet Module that includes pumps to evacuate fluid and matter from the GI tract.
A foot pedal activates the cleansing, suction and purging functions, and enables switching between cleansing modes used by the physician.

The provided text describes the Pure-Vu EVS System, its indications for use, technological characteristics, and a comparison to its predicate device. It also briefly mentions performance data. However, the document does not contain specific acceptance criteria, detailed study results, or information regarding sample sizes for training/test sets, expert qualifications, ground truth establishment, MRMC studies, or standalone algorithm performance.

Therefore, based only on the provided text, I cannot complete the requested table and answer all questions definitively.

Here's what can be extracted and what is missing:

Acceptance Criteria and Device Performance

Detailed Study Information (Not available in the provided text):

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

   • Not specified in the provided text. The document only mentions "Validation testing for the WS and disposable device was performed with 4 physicians." This appears to refer to human factors or usability testing rather than a clinical performance study with a test set of patient data.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

   • Not specified in the provided text.

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

   • Not specified in the provided text.

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:

   • Not applicable/Not specified. This device is for intra-procedural cleaning during colonoscopy, not an AI-powered diagnostic imaging device requiring reader studies in this context. The document describes a physical medical device (Pure-Vu EVS System) for colon cleaning, not an AI algorithm.

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

   • Not applicable/Not specified. As above, this is a physical medical device, not an AI algorithm. Its performance is intrinsically "human-in-the-loop" as it assists a physician during a colonoscopy.

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

   • Not explicitly stated in the provided text. The performance data section focuses on engineering verification and validation testing (e.g., pressure, flow rates, dimensional compliance, biocompatibility, software compliance) rather than a clinical ground truth for a diagnostic claim. The clinical outcome (effective colon cleaning) is assumed to be evaluated during the "Validation testing for the WS and disposable device was performed with 4 physicians," but the specific metrics and how "ground truth" for cleaning effectiveness were established are not detailed here.

7. The sample size for the training set:

   • Not applicable/Not specified. There is no mention of a "training set" as this is a physical medical device, not an AI model requiring machine learning training.

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

   • Not applicable/Not specified. See point 7.

In summary, the provided document is a 510(k) summary for a physical medical device. It covers the technical performance verification and validation to demonstrate substantial equivalence to a predicate device. It does not contain the detailed clinical study information typically found for AI/ML-based devices regarding diagnostic performance, ground truth, reader studies, and sample sizes for algorithmic evaluation. The "performance data" section focuses on engineering tests (e.g., environmental, mechanical, flow, pressure, software validation) and a mention of "validation testing for the WS and disposable device was performed with 4 physicians" which likely refers to human factors or usability rather than an effectiveness study using a "test set" of patient data for an algorithm.

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The EVS 4343W / EVS 4343WG / EVS 3643W / EVS 3643WG / EVS 3643WP 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.

The EVS 4343W / EVS 4343WG / EVS 4343WP / EVS 3643W / EVS 3643WG / EVS 3643WP Detector is an indirect conversion device 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.

Here's a breakdown of the acceptance criteria and the study details for the DRTECH EVS detectors, based on the provided FDA 510(k) summary:

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria for the new devices (EVS 4343WP, EVS 3643WP) were primarily to demonstrate equivalent diagnostic capability to the predicate device (EVS 3643). This was assessed through clinical image evaluation, comparing image performance scores. For other device models and parameters (DQE, MTF), the acceptance typically involved being "basically equal or [better] than the predicate device."

Here's a table summarizing the comparison for key performance metrics:

Note: For DQE and MTF, the acceptance criterion implicitly means that the values should be close to or exceed the predicate's performance, indicating comparable or improved image quality metrics. The document states "basically equal or [better] than the predicate device." In some cases (e.g., EVS 4343W/EVS 3643W DQE vs. EVS 3643), the subject device values are slightly lower than the predicate, but this is presented within the context of "basically equal or [better] than" and ultimately deemed acceptable for substantial equivalence. For the GOS models, the subject devices showed improvement in DQE and MTF.

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

• Sample Size: The document mentions "8 positions of body parts (Chest PA, Cspine AP, C-spine LAT, L-spine LAT, Shoulder AP, Shoulder LAT, Extremities)" were selected for the clinical image evaluation. It does not explicitly state the number of images per body part or the total number of images in the test set. It also doesn't specify if these were real patient cases or phantoms.
• Data Provenance: Not explicitly stated. The manufacturer is based in the Republic of Korea, so the data could originate from there, but this is not confirmed. The study is described as a "clinical image evaluation." It's not specified if it's retrospective or prospective.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Described as a "single blind clinical image evaluation." This implies that the readers were blind to which device produced the image (subject vs. predicate). However, the specific method of consensus or individual scoring (e.g., 2+1, 3+1, none) among multiple readers is not detailed.

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

• MRMC Study: The document describes a "single blind clinical image evaluation" to compare image performance. This sounds like a MRMC study, as it compares the image performance of multiple devices (subject vs. predicate) using human readers.
• Effect Size of Human Reader Improvement: The document states that "it is indicated that there is no significant difference of image performance between EVS 4343WP, EVS 3643WP and EVS 3643 as difference in the score is within one standard deviation." This implies equivalence rather than an improvement with AI vs. without AI assistance, as this is a comparison of X-ray detectors themselves, not an AI software. The study's focus was on the diagnostic capability of the new hardware, not an AI's impact on human performance. Thus, no effect size of human improvement with AI is provided.

6. Standalone Performance (Algorithm Only)

• This section does not involve an algorithm with standalone performance, as the device is a digital X-ray detector (hardware). The software (Econsolel) is mentioned and being the same as the predicate's, but the evaluation focuses on the hardware's image acquisition performance.

7. Type of Ground Truth Used

• Ground Truth: For the clinical image evaluation, the "ground truth" was established by comparing the "image performance" scores between the subject device's images and the predicate device's images. This is an expert consensus or subjective evaluation of image quality and diagnostic capability, rather than an objective pathology or outcomes data.

8. Sample Size for the Training Set

• Training Set Sample Size: Not applicable. This document describes the evaluation of an X-ray detector, which is hardware, not an AI algorithm that would typically require a training set.

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

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

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The EVS 4343A / EVS 4343AG / EVS 3643AG 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.

The EVS 4343A / EVS 4343AG / EVS 3643A / EVS 3643AG is a flat-panel type digital X-ray detector that captures projection radiographic images in digital format within seconds, eliminating the need for an entire x-rav film or an image plate as an image capture medium. EVS 4343A / EVS 4343AG / EVS 3643A / EVS 3643AG 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 EVS 4343A / EVS 4343AG / EVS 3643A / EVS 3643AG Detector is an indirect conversion device 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 through 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 (K192400) for the DRTECH EVS 4343A, EVS 4343AG, EVS 3643A, and EVS 3643AG digital X-ray detectors focuses on demonstrating substantial equivalence to a predicate device (K162555). Therefore, the "acceptance criteria" discussed are primarily related to showing that the new devices perform as well as or better than the predicate, particularly in key physical performance metrics.

Here's an analysis of the acceptance criteria and the study details based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the predicate device's performance and the expectation that the new devices should meet or exceed these values for key metrics like DQE and MTF.

The document states: "it is proved that the DQE and MTF of predicated device and subject device are basically equal or worth than the predicate device." and "As a result, subject devices performance is equal or worth than the predicate device." However, the presented data shows that the subject devices exceed the DQE and MTF values of the predicate device, indicating superior performance in these measured aspects.

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

The provided summary does not specify a sample size for a clinical test set involving patients or images. The "non-clinical data" discussed pertains to bench testing of the detector's physical performance (DQE, MTF, Resolution). Therefore, the concepts of "test set" in the context of clinical images, "country of origin," and "retrospective/prospective" are not applicable to the non-clinical performance evaluation described.

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

This information is not applicable as the evaluation was a non-clinical, bench-top performance assessment of the device's physical imaging characteristics (DQE, MTF, resolution), not a clinical study requiring expert interpretation of medical images.

4. Adjudication Method for the Test Set

This information is not applicable for the same reasons as point 3.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not conducted or described in this 510(k) summary. The submission focuses on demonstrating substantial equivalence based on technical specifications and non-clinical performance, not on a comparison of human reader performance with or without AI assistance.

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

This is not applicable. The device is an X-ray detector, a hardware component that captures images. It does not contain an AI algorithm for image analysis in isolation (standalone) or for human-in-the-loop performance. Its "performance" refers to how well it acquires images, not how well it interprets them.

7. The Type of Ground Truth Used

The ground truth for the non-clinical performance evaluation (DQE, MTF, Resolution) would be based on physical phantom measurements and established international standards (e.g., IEC 62220-1) for characterizing X-ray detector performance. It is not expert consensus, pathology, or outcomes data, as those relate to clinical diagnostic accuracy.

8. The Sample Size for the Training Set

This 510(k) summary does not describe a training set. The device is a digital X-ray detector, which is a hardware component. There is no mention of machine learning or AI algorithms requiring a training set for this particular submission. The "study" here is a technical performance assessment of the detector itself.

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

This information is not applicable as no training set for an AI algorithm is described.

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The EVS 2430W and EVS 2430GW 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.

The EVS 2430(G)W is a wired/wireless 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. EVS 2430(G)W 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 EVS 2430(G)W Detector is an indirect conversion device 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.

This document describes the regulatory submission for the DRTECH Corporation's EVS 2430W and EVS 2430GW Digital X-ray detectors. The submission aims to demonstrate substantial equivalence to a predicate device (EVS 3643, EVS 3643G, K162552).

Here's an analysis of the provided information regarding acceptance criteria and the supporting study:

1. Table of Acceptance Criteria and Reported Device Performance:

The document primarily focuses on demonstrating substantial equivalence to a predicate device rather than defining explicit acceptance criteria with specific thresholds for each parameter. Instead, it compares the performance of the modified device (EVS 2430W, EVS 2430GW) to the predicate device (EVS 3643, EVS 3643G). The acceptance is implicitly based on the new devices showing "basically equal or higher" physical values for key X-ray device comparison metrics.

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

• Sample Size: Not explicitly stated as a number of cases or images. The document mentions "Clinical images were provided" and "study confirmed that the x-ray detectors EVS 2430(G)W provide images of equivalent diagnostic capability." The specific number of images or patients in the clinical image test set is not provided.
• Data Provenance: Not explicitly stated. The study was a "single-blinded concurrence study according to CDRH's Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices." This typically implies a controlled setting, but the origin (e.g., country, type of clinic) of the raw images or data used is not mentioned. It is a clinical test to demonstrate clinical effectiveness. Whether it's retrospective or prospective is also not stated clearly for this "clinical image evaluation."

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

• Number of Experts: Not specified. The document states it was a "single-blinded concurrence study." This implies that expert readers (e.g., radiologists) were involved in evaluating the images, but their number is not provided.
• Qualifications of Experts: Not specified. While the context (radiographic diagnosis) implies radiologists would be the experts, their specific qualifications (e.g., years of experience, subspecialty) are not mentioned.

4. Adjudication Method for the Test Set:

• Adjudication Method: Not explicitly stated. The term "concurrence study" implies that multiple readings were compared for agreement, but the specific adjudication method (e.g., 2+1, 3+1, majority vote, etc.) is not detailed. The "single-blinded" aspect means the readers were unaware which images came from the test device versus the predicate.

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

• Was an MRMC study done? A "single-blinded concurrence study" was conducted to confirm "equivalent diagnostic capability" to the predicate. This is a type of comparative effectiveness study. However, it's not explicitly framed as an "MRMC study" in the sense of a statistical design aimed at measuring observer performance gains. It's more of a qualitative assessment of equivalence.
• Effect size of human readers with AI vs. without AI assistance: Not applicable. This device is a digital X-ray detector, not an AI-assisted diagnostic tool. The comparison is between the new detector and a predicate detector, not about AI assistance for human readers.

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

• Was standalone performance done? Not applicable. The device is a hardware component (X-ray detector). Its "performance" is measured by physical metrics (DQE, MTF, Resolution) and its ability to produce diagnostically equivalent images for human interpretation, not by an autonomous algorithm making diagnoses.

7. The Type of Ground Truth Used:

• Type of Ground Truth: The document states that the "clinical image evaluation proved the clinical effectiveness of the subject device" and that the study confirmed "equivalent diagnostic capability." This strongly suggests that the ground truth was established by expert consensus (i.e., expert readers determining the diagnostic quality and clinical equivalence of images produced by the test device compared to the predicate). It does not mention pathology, outcomes data, or other definitive ground truths.

8. The Sample Size for the Training Set:

• Sample Size for Training Set: Not applicable. This document is for a hardware device (X-ray detector), not a machine learning algorithm that requires a "training set." The tests described are for objective performance metrics and clinical image evaluation for equivalence.

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

• How Ground Truth for Training Set was Established: Not applicable, as this is not an AI/ML device requiring a training set.

In summary, the document details a comparison of a new X-ray detector to a predicate device, focusing on technical specifications and demonstrating "equivalent diagnostic capability" through a clinical image evaluation. While it confirms the device meets acceptance criteria (primarily by demonstrating equivalence to a previously cleared device), many specific details about the clinical study's methodology, particularly regarding reader numbers, qualifications, and exact sample sizes, are not provided in this specific excerpt.

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The EVS 4343 and EVS 4343G 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 radio graphic systems in all general purpose diagnostic procedures. This device is not intended for mammography applications.

The EVS 4343(G) is a wired 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. EVS 4343(G) 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 EVS 4343(G) Detector is an indirect conversion device 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.

1. Table of Acceptance Criteria and Reported Device Performance:

The document indicates that the acceptance criteria are based on equivalence to predicate devices (K142475 and K150766). The study aimed to demonstrate that the new detectors provide "images of equivalent diagnostic capability." While specific numeric acceptance thresholds are not explicitly stated, the performance metrics (DQE and MTF) are compared against the predicate devices.

Note regarding DQE: While the "Remark" column states "similarity," for EVS 4343, the DQE values are higher than both predicates, suggesting better performance. For EVS 4343G, the DQE at 1 lp/mm is lower than both predicates, and at 3 lp/mm, it is the same as K150766 but lower than K142475. The document concludes "similarity," indicating that these differences were deemed acceptable for substantial equivalence.

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

The document states that a "single-blinded concurrence study" was conducted to confirm "equivalent diagnostic capability" to the predicate devices. However, the sample size for the test set is not explicitly provided.

The data provenance is also not explicitly stated in terms of country of origin or whether it was retrospective or prospective.

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

The document does not specify the number of experts used or their qualifications for the single-blinded concurrence study. It only mentions that the study was conducted according to "CDRH's Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices."

4. Adjudication Method for the Test Set:

The document mentions a "single-blinded concurrence study." This implies that readers were blinded to the source of the image (test device vs. predicate) and their readings were compared for agreement or concordance. However, the specific adjudication method (e.g., 2+1, 3+1, none) is not detailed.

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

The document refers to a "single-blinded concurrence study" to assess "equivalent diagnostic capability," which suggests a type of reader study. However, it does not explicitly state that it was an MRMC comparative effectiveness study in the formal sense, nor does it provide an effect size of how much human readers improve with AI vs. without AI assistance. The device described is an X-ray detector, not an AI-powered diagnostic tool, so the concept of "AI assistance" is not applicable in this context.

6. Standalone Performance Study:

Yes, a standalone study of the algorithm's performance (in this case, the detector's physical performance) was done. The document explicitly includes "Summary of Non-Clinical Data" which reports the DQE (Detective Quantum Efficiency) and MTF (Modulation Transfer Function) values for the EVS 4343 and EVS 4343G devices. These are intrinsic performance characteristics of the detector itself, independent of human interpretation.

7. Type of Ground Truth Used:

For the non-clinical performance data (DQE, MTF), the ground truth is established through physical measurements and international standards (IEC 62220-1 for DQE).

For the "single-blinded concurrence study" assessing "equivalent diagnostic capability," the ground truth implicitly seems to be expert consensus on diagnostic capability comparing images from the new device against images from the predicate devices. However, the specific method for establishing this "ground truth" (e.g., if a definitive diagnosis was available for the cases used) is not detailed.

8. Sample Size for the Training Set:

The document does not provide information on a training set sample size. The EVS 4343(G) is an X-ray detector, a hardware device, not an AI algorithm that typically requires a large training dataset.

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

As there is no mention of a "training set" for the device itself (being a hardware detector), this question is not applicable based on the provided document. The performance metrics (DQE, MTF) are physical properties measured according to established standards rather than "trained" by data.

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The EVS 3643 and EVS 3643G 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.

The EVS 3643(G) is a wired/wireless 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. EVS 3643(G) 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 EVS 3643(G) Detector is an indirect conversion device 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 provided text describes the acceptance criteria and a study conducted for the EVS 3643 and EVS 3643G Digital X-ray detectors.

Here's the breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document mentions a "single-blinded concurrence study" and "Clinical images were provided". However, it does not specify the sample size for the test set (number of images or cases).

The data provenance is also not explicitly stated regarding country of origin or whether it was retrospective or prospective. It only notes that the study was conducted "according to CDRH's Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices."

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

The document does not specify the number of experts used or their qualifications for establishing ground truth in the clinical study. It refers to a "concurrence study," implying multiple readers, but provides no details.

4. Adjudication method for the test set

The document mentions a "single-blinded concurrence study," but does not describe the specific adjudication method used (e.g., 2+1, 3+1, none). The term "concurrence" suggests agreement among observers, but the process is not detailed.

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

The study described is a "single-blinded concurrence study" comparing the new x-ray detectors (EVS 3643G) to predicate devices to establish equivalent diagnostic capability. It is not an MRMC comparative effectiveness study evaluating the improvement of human readers with AI assistance. The device itself is a digital X-ray detector, not an AI-powered diagnostic tool. Therefore, there is no mention of effect size related to human reader improvement with AI.

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

The device is a digital X-ray detector, which captures images. It is not an algorithm that performs a diagnostic task independently. Therefore, a standalone algorithm-only performance study is not applicable to this device. The non-clinical data focuses on inherent device performance characteristics like DQE, MTF, and Resolution.

7. The type of ground truth used

The document states that the "concurrence study" confirmed that the new x-ray detectors "provide images of equivalent diagnostic capability." This implies that the ground truth for the clinical images was established through expert consensus on diagnostic capability, rather than pathology or specific disease outcomes.

8. The sample size for the training set

The document does not mention a training set or its sample size. This device is a hardware component (digital X-ray detector) and not a machine learning algorithm that requires a training set for model development.

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

As there is no mention of a training set, the method for establishing its ground truth is also not applicable.

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The Venclose™ Radiofrequency System (Generator and Catheters) is intended for use in endovascular coagulation of blood vessels in patients with superficial vein reflux.

The Venclose RF System is composed of two basic components: an energy console or generator (digiRF) and catheters (EVSRF) designed specifically to be used with the generator. The Venclose RF System uses resistive radiofrequency ablation via energy delivery to heat the wall of an incompetent vein with temperature-controlled RF energy to cause irreversible luminal occlusion, followed by fibrosis and ultimately resorption of the vein. The blood then naturally reroutes to healthy veins.

The technique involves percutaneous access and insertion of the EVSRF Catheter into the great saphenous vein (GSV) or other superficial vein under ultrasound guidance, injection of local anesthesia, and thermal energy from a radiofrequency generator (digiRF) applied into the target vein. As the EVSRF Catheter is withdrawn stepwise down the treated length of the vein, thermal damage is inflicted upon the venous endothelium and through the vein wall, resulting in contraction and ultimately destruction of the vessel. The procedure can be performed in an outpatient setting, without the need for general anesthesia, allowing for a walkin/walk-out procedure with minimal postoperative recovery time.

I am sorry, but the provided text does not contain detailed acceptance criteria or a specific study that proves the device meets those criteria in the format requested. The document is a 510(k) summary for the Venclose™ Radiofrequency System, which focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed performance metrics against specific acceptance criteria.

While it mentions non-clinical bench testing and pre-clinical animal testing, it does not provide:

• A table of acceptance criteria with reported device performance.
• Sample sizes for a test set or data provenance for specific performance evaluation.
• Number of experts or their qualifications for establishing ground truth.
• Adjudication methods.
• Multi-reader multi-case (MRMC) comparative effectiveness study results.
• Standalone (algorithm-only) performance.
• Specific ground truth types used for detailed performance evaluation.
• Sample size for a training set or how its ground truth was established, as this is not an AI/algorithm-based device in the context of typical AI performance studies.

The document primarily focuses on:

• Device Description: What the Venclose RF System is and how it works.
• Materials: Composition of the console and catheters.
• Comparative Summary of Technological Characteristics: A table comparing the subject device with predicate devices in terms of features like energy/frequency, maximum output power, dimensions, etc., to establish similarity.
• Non-Clinical Testing: A general statement that testing was conducted to evaluate conformance to product specifications and applicable standards (e.g., ISO 10555-1, IEC 60601-1). It lists types of bench tests (Catheter Simulation, Temperature, Pressure, Tensile, Fluid Test) but does not provide specific acceptance values or results for these tests.
• Pre-clinical testing (Animal Study): Describes the purpose (demonstrate proper functionality), the model used, and objectives (insertion, navigation, reaching/holding temperature, vein diameter reduction). It concludes that "All of the objectives were met resulting in a satisfactory study conclusion," but again, it doesn't provide quantitative acceptance criteria or specific performance data from this study.
• Rationale for Substantial Equivalence: Concludes that the non-clinical and animal testing demonstrates the Venclose System is as safe, effective, and performs at least as safely and effectively as the predicate devices.

In summary, the provided text establishes substantial equivalence for regulatory purposes but does not offer the detailed performance data against explicit acceptance criteria that your request asks for, especially in the context of an AI-driven device performance study.

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