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The DICOM Video software package is intended for use by authorized personnel to acquire individual or sequences of images and to allow the user to input patient demographics related to the image. The device transforms imaging studies to DICOM format before they are made available to other locations in the network. The DICOM Video is indicated to receive studies in various digital formats (text, still images and video) or digitized video signals from acquisition host devices. The device operations include capturing the data, recording, storing, editing and transferring it to the clinic PACS as DICOM files.

The DICOM Video is a software package, which is installed in an Off - The - Shelf Host PC. The device is interfaced and configured to a hospital Local Area Network (LAN). The DICOM Video receives patients' list from the Hospital Modality Work List (MWL ) server and digital files input data (in text, images or video formats). The device records the received data, stores it, transfers the studies into standard DICOM files, and transmits the DICOM files via the LAN to the Hospital PACS. The DICOM Video can be interfaced to a Host Acquisition Device in order to receive analog video signals. The data is digitized, stored, optionally edited, transferred into standard DICOM files, and transmitted as DICOM files via the LAN to the Hospital PACS. The device can also retrieve DICOM studies from the PACS and display them on the user screen.

Here's a breakdown of the acceptance criteria and the study information for the DICOM Video device, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document (K132183) for the DICOM Video device does not explicitly list "acceptance criteria" in a quantitative or pass/fail table as one might expect for a diagnostic AI device. Instead, the performance is described in terms of compliance with standards and successful functionality testing.

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

The document does not specify a numerical sample size for any test set or provide details on data provenance (e.g., country of origin, retrospective/prospective). The testing described is functional and compliance-based, rather than clinical efficacy studies involving patient data.

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

Not Applicable. The device is a "Medical Image Digitizer" (a software package that converts various image and video formats into DICOM files and manages them). It does not perform diagnostic interpretations of medical images that would require expert consensus ground truth. Its "ground truth" would be related to the accurate and complete conversion and transfer of data according to DICOM standards and its stated functionalities.

4. Adjudication Method for the Test Set

Not Applicable. As explained above, this device does not involve diagnostic interpretations requiring an adjudication method by experts.

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

No. The document explicitly states: "Clinical Data: Clinical data is not included." An MRMC study assesses the impact of a device on human reader performance, which requires clinical data and human interpretation. This device is a data processing and management tool, not a diagnostic aid for human readers.

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

Yes, in essence. The "Bench data" described indicates standalone performance testing of the software itself: "The device performance has been verified by testing the software with respect to predefined software test plan." The "device validation" in an end-user environment further confirms its performance without explicitly relying on human interpretation of diagnostic findings, but rather on its functional execution.

7. Type of Ground Truth Used

The "ground truth" for this device's performance is based on:

• Compliance with specifications and standards: Adherence to NEMA PS 3.1 - 3.20 (2011) DICOM set.
• Functional correctness: Output meeting "design input" and supporting "indications for use" as demonstrated by predefined software test plans and end-user environment testing.
• Regulatory compliance: Conformance to 21CFR 820.30 regulations.

8. Sample Size for the Training Set

Not Applicable. This device is a pre-AI era software for data digitization and management. It is not an AI/Machine Learning model that would typically have a "training set" in the context of learning patterns from data. Its functionality is rule-based and standard-compliant.

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

Not Applicable. As this is not an AI/Machine Learning device that requires a training set, the concept of establishing ground truth for a training set does not apply.

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The TeleradPRO Edge and TeleradPRO Edge HD-CCD digitizers are used for making digital copies of medical x-ray film, including printed and radiographic film. The target users of these devices are medical professionals or trained staff for use as secondary capture images for consultation, review and final interpretation.

These devices are not intended to be used for primary diagnosis in mammography applications.

The device consists of a film digitizer with single-film or multi-film feeder, external power adapter, and Windows driver software. The film digitizer will convert the X-ray film into digital data representing the X-ray film, and the windows driver software is used to import the digital data into a compatible software application. The digitizer is used with DICOM standard software that meets or exceeds ACR and DICOM standards for use of secondary capture images for consultation, review and final interpretation.

The digitizer is connected to a PC through a USB 2.0 interface. The digitizer utilizes rollers driven by a stepper motor to feed the X-ray film past the scan optics. The scan optics consists of a white or blue LED illuminator, a lens, mirrors, and a CCD linear array detector.

This device has no patient contact and does not supply a diagnostic result. The film digitizer only provides digital data representing the film.

This document describes a 510(k) submission for the TeleradPRO Edge and TeleradPRO Edge HD-CCD film digitizers. The submission asserts substantial equivalence to a predicate device, the VIDAR TeleradPRO film digitizer (K102476).

1. Table of Acceptance Criteria and Reported Device Performance

The device is a medical image digitizer, and its performance is primarily characterized by technical specifications rather than diagnostic performance metrics like sensitivity or specificity. The "acceptance criteria" can be inferred from the stated technological characteristics and the claim of substantial equivalence to the predicate device.

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

The provided document does not specify a separate "test set" in the context of a clinical study with patient data. The performance testing mentioned refers to technical evaluations of the digitizer's hardware and software capabilities. No patient data or clinical images are described as being used for a "test set" in the sense of evaluating diagnostic accuracy. The device "has no patient contact and does not supply a diagnostic result."

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

Not applicable. As described in point 2, there is no mention of a test set requiring expert-established ground truth for diagnostic accuracy, as the device doesn't provide a diagnostic result.

4. Adjudication method for the test set

Not applicable. There is no mention of a test set requiring 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

Not applicable. This device is an X-ray film digitizer, not an AI-assisted diagnostic tool.

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

Not applicable. The device is a film digitizer, not an algorithm providing diagnostic output. Its function is to convert physical film to digital data.

7. The type of ground truth used

For the technical performance characteristics (spatial resolution, OD range, etc.), the ground truth would be established through calibrated measurement instruments and industry standards for film digitizer performance. For instance, DMAX and OD range would be measured using calibrated optical density strips.

8. The sample size for the training set

Not applicable. This device is a film digitizer; it does not utilize a "training set" in the context of machine learning.

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

Not applicable. This device is a film digitizer; it does not utilize a "training set" in the context of machine learning.

Study Proving Device Meets Acceptance Criteria:

The document states: "VIDAR conducts extensive performance testing and the test results demonstrate the device meets the requirements for its intended use."

However, the specific details of this "extensive performance testing" are not provided in this 510(k) summary. The submission relies on claiming substantial equivalence to the predicate device (VIDAR TeleradPRO film digitizer, K102476) rather than providing a detailed report of a new clinical or technical performance study for the TeleradPRO Edge and HD-CCD. The conclusion states: "In terms of intended use, function, safety, operating environmental conditions and effectiveness of the TeleradPRO Edge and TeleradPRO Edge HD-CCD it is determined to be substantially equivalent to the predicate device used for this application." This implies that the 'study' demonstrating acceptance criteria is primarily a comparison against the predicate device's established performance and specifications, along with internal technical verification tests.

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The DiagnosticPRO Edge digitizer is used for making digital copies of medical x-ray film, .including .printed and .radiographic .film. The .target users of these devices are .medical professionals or trained staff for use as secondary capture images for consultation, review and final interpretation.

The devices are indicated for the digitization of mammography images for review and analysis, but not as the sole basis for screening or diagnosis.

The device consists of a film digitizer with multi-film feeder, external power adapter, and Windows driver software. The film digitizer will convert the X-ray film into digital data representing the X-ray film, and the windows driver software is used to import the digital data into a compatible software application. The digitizer is used with DICOM standard software that meets or exceeds ACR and DICOM standards for use of secondary capture images for consultation, review and final interpretation.

The digitizer is connected to a PC through a USB 2.0 interface. The digitizer utilizes rollers driven by a stepper motor to feed the X-ray film past the scan optics. The scan optics consists of a white LED illuminator, a lens, mirrors, and a CCD linear array detector.

This device has no patient contact and does not supply a diagnostic result. The film digitizer only provides digital data representing the film.

The provided document is a 510(k) summary for the VIDAR DiagnosticPRO Edge X-Ray Film Digitizer. It states that "VIDAR conducts extensive performance testing and the test results demonstrate the device meets the requirements for its intended use" and refers to "Section 19 Bench Testing." However, Section 19 Bench Testing and the specific details of the performance testing results and acceptance criteria are not included in the provided text.

Therefore, I cannot provide a table of acceptance criteria or the specific details of the study that proves the device meets the acceptance criteria based only on the provided input. The document mentions "Bench Testing" in Section 19, which implies performance evaluation, but the content of that section is missing.

Based on the available information, I can only provide general information about the device and point out the missing details.

Here's an outline of the information that would be provided if the "Section 19 Bench Testing" were available:

Missing Information from Provided Document:

The provided 510(k) summary states that performance testing was conducted and refers to "Section 19 Bench Testing" for details. However, the content of Section 19 is not included in the provided text. Therefore, I cannot extract specific acceptance criteria, reported device performance data, study design, or ground truth establishment.

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The VIDAR TeleradPRO film digitizer is used for making digital copies of medical x-ray films.
The TeleradPRO film digitizer is intended for making digital copies of medical xray films.

The device consists of a film digitizer with single-film or multi-film feeder, external power adapter, and Windows driver software. The film digitizer will convert the X-ray film into digital data representing the X-ray film, and the windows driver software is used to import the digital data into a compatible software application. The Digitizer is connected to a PC through a USB 2.0 interface. The digitizer utilizes rollers driven by a stepper motor to feed the X-ray film past the scan optics. The scan optics consists of a white LED illuminator, a lens, mirrors, and a CCD linear array detector.

The provided text describes the TeleradPRO X-Ray Film Digitizer and its substantial equivalence to a predicate device (TeleRADPro Film Digitizers, K993597). However, it does not contain detailed information about specific acceptance criteria or an in-depth study that proves the device meets those criteria in a quantitative manner as typically expected for medical device performance studies.

The document primarily focuses on establishing substantial equivalence to a predicate device, which often relies on demonstrating similar technological characteristics and intended use, rather than presenting a novel clinical study with detailed performance metrics against predefined acceptance criteria.

Based on the provided text, here's the information that can be extracted, and where information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

Missing Information: Specific quantitative acceptance thresholds for DMAX, medical OD range linearity, and noise are not provided, nor are detailed test results demonstrating compliance against these thresholds. The document states "VIDAR conducts extensive performance testing and the test results demonstrate the device meets the requirements for its intended use" but does not elaborate on the specific tests or their outcomes beyond the summary characteristics.

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

This information is not provided in the document. The document refers to "extensive performance testing" and "Bench Testing" (Section 22, which is not included in the provided text), but it does not specify any test set sample sizes or the provenance of any data (e.g., country of origin, retrospective/prospective). It suggests that the testing was primarily to demonstrate the device's technical specifications and substantial equivalence, not necessarily a clinical or reader study with a specific test set of images.

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

This information is not provided in the document. As no clinical or reader study with a "test set" and "ground truth" established by experts is described, this information is not applicable. The device is a digitizer, not an AI diagnostic tool, so "ground truth" would relate to the fidelity of the digitization process itself, not an interpretation of medical images.

4. Adjudication Method for the Test Set

This information is not provided in the document. See point 3 above.

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

No MRMC study was done, nor is it relevant given the device is an X-ray film digitizer, not an AI diagnostic tool designed to assist human readers. The document does not mention any AI components.

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

The document describes "Performance Testing" and "Bench Testing" which would likely be standalone evaluations of the digitizer's technical specifications (e.g., resolution, DMAX, linearity). However, details of these studies are not provided beyond the summary characteristics listed in Section 1.

7. The Type of Ground Truth Used

Given that the device is a digitizer, the "ground truth" for its performance would typically involve comparing the digitized image to the original film's characteristics or a known standard. The document mentions "incorporates noise and linearity measurements" for the medical OD range, implying that instrumental measurements were used to verify the fidelity of the digitization process. There is no mention of "expert consensus, pathology, or outcomes data" as ground truth, as those would be relevant for diagnostic interpretation, not for a digitizing device.

8. The Sample Size for the Training Set

This information is not provided and is not applicable. The device is a film digitizer, which typically does not involve machine learning "training sets." Its operation is based on optical and electronic principles, not learned algorithms.

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

This information is not provided and is not applicable. See point 8 above.

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The PCR Eleva System is a digital film processing system for reading and then digitizing X-ray images from reusable imaging plates which have been exposed in conventional radiographic examination devices. The digitized X-ray images can then be viewed, stored, post-processed and printed. The PCR Eleva system can be used in all conventional RAD/RF examination situations, except for mammography. PCR is suitable for routine RAD exams as well as specialist areas, like intensive care units, trauma departments and pediatric departments.

A PCR Eleva consists of one or more workspots with PCR Eleva Software and one or more image plate readers. All components are connected via standard ethernet. The system complies with the ACR/NEMA DICOM Version 3 Digital Image Communication in Medicine Standard. Imaging plates are exposed via conventional X-Ray devices. The imaging plates used in PCR systems are coated with a luminescent material which acts as an x-ray detector. It stores the x-ray image in the form of excited charge carriers. An exposed imaging plate is loaded into the image reader of the PCR Eleva system and the image stored on the imaging plate is scanned with a laser and converted to digital data. The digital X-ray image data is then routed to the Eleva workstation for image processing, viewing, storing and/or printing to film if the workstations are connected to a compatible laser imager. The Eleva Workspot is also used for the scheduling of patients and exams. The Eleva Workstation consists of a PC, a keyboard, a monitor, and an optional bar-code reader.

The provided document is a 510(k) submission for the Philips PCR Eleva 1.2, a digital film processing system. It outlines the device's identification, predicate device, indications for use, and a summary of nonclinical tests. However, it does not contain the detailed information required to answer all aspects of your request regarding acceptance criteria and a specific study proving the device meets those criteria, particularly within the context of AI performance or clinical efficacy studies.

This submission is a Special 510(k), which focuses on modifications to an existing device (Philips PCR 5.2). The core claim is that these modifications do not change the indications for use or alter the fundamental scientific technology, and therefore, the new device is at least as safe and effective as the predicate. The "study" referenced is primarily a verification and validation process against requirement specifications and risk management results, rather than a clinical trial demonstrating performance against specific clinical metrics.

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

1. Table of acceptance criteria and the reported device performance

The document states: "All acceptance criteria for a product release according to our product release policy are met." However, it does not explicitly list the specific acceptance criteria or provide a table of reported device performance against those criteria. It generally refers to "requirement specifications and risk management results" and "software verification, validation and DICOM conformance testing" as areas where tests were performed.

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

This information is not provided in the document. The submission mentions "verification and validation tests were performed on the complete system," but it does not detail the nature of a specific test set (e.g., patient images), its sample size, or its provenance.

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

This information is not provided. As the submission primarily concerns system modifications and verification, and not a clinical performance study with human readers assessing images, the concept of "ground truth established by experts" for a test set in the clinical sense is not discussed.

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

This information is not provided.

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

There is no mention of an MRMC comparative effectiveness study, nor any discussion of AI assistance or its effect size on human readers. This submission predates widespread integration and evaluation of AI in medical imaging devices in this manner (2009). The device is described as a "digital film processing system," which digitizes X-ray images for viewing, storage, post-processing, and printing. It is not presented as an AI-powered diagnostic aid.

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

This information is not applicable/provided in the context of an AI algorithm. The device itself is a system for processing and managing images, not a standalone diagnostic algorithm.

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

The document does not detail how "ground truth" (in a clinical sense related to disease presence/absence) was established for any specific image test set. The "ground truth" in this context refers to the device's functional performance against its technical specifications and regulatory requirements, not clinical diagnostic accuracy.

8. The sample size for the training set

This information is not provided and is not applicable in the context of this device development, as it is not an AI-driven system that would typically undergo a "training" phase with a dataset of labeled clinical images.

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

This information is not provided and is not applicable for the reasons stated above.

In summary:

The Philips PCR Eleva 1.2 Special 510(k) submission describes updates to a Computed Radiography system. The "study" referred to is a series of nonclinical verification and validation tests (software, DICOM conformance, and assessment against requirement specifications and risk management results) ensuring the modified device remains as safe and effective as its predicate. It does not present clinical performance data, AI performance metrics, or details about expert-adjudicated test sets. The nature of this 510(k) (Special 510(k) for device modifications) means a full-scale clinical trial with specific performance metrics against a defined acceptance criterion and a large, expert-adjudicated test set was likely not deemed necessary by the manufacturer or required by the FDA at the time for this type of device modification.

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The Medi-6000 consists of a transparency film digitizer and paper CCD scanner that is used to digitize radiographs or X-ray film as well as paper reports or doctor's orders. When the Medi-6000 is used to digitize radiographic films, the digital image is intended for use in primary, secondary and over reading applications. This device is not to be used for primary image diagnosis in mammography. The target users of the device are medical professionals or trained staffs.

The Medi-6000 is a digitizer that can easily transfer x-ray films into a digital format for patients, hospitals, and office records. It can capture details in bright and dark areas of x-ray films and provide the medical professionals a convenient method to digitize the roentgenogram for the electric data storage.

The provided 510(k) summary for the Microtek International, Inc. Medi-6000 Medical Image Digitizer (K091288) contains limited information regarding detailed acceptance criteria and a specific study proving the device meets them. It primarily focuses on demonstrating substantial equivalence to predicate devices based on technological characteristics and intended use.

Here's an analysis based on the available text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document mentions specific technological characteristics, which act as de facto performance criteria for comparison with predicate devices. However, explicit "acceptance criteria" for a study are not presented in a formal table or detailed manner. The performance criteria are implied by the device's specifications and the comparison to predicate devices.

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

The document does not explicitly state a sample size for a test set. There is no mention of a clinical or image-based study with a test set of images. The performance testing section states "The performance testing results for the Medi-6000 digitizer demonstrated that the device meets its intended use specifications," but it doesn't describe the methodology, sample selection, or data provenance (e.g., country of origin, retrospective/prospective nature of data).

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

The document does not mention using experts to establish ground truth for any test set, as no specific image-based study is described. The assessment appears to be based on physical device specifications and comparison to predicate devices.

4. Adjudication Method for the Test Set:

Since no test set or image-based study is described, there is no information provided on any adjudication method.

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

The document does not mention any MRMC study. The device is a digitizer, and the regulatory filing focuses on its ability to convert physical films to digital images, not on its impact on human reader performance in interpreting those images.

6. Standalone Performance Study:

The document discusses "Performance Testing" which indicates a standalone assessment of the digitizer's capabilities (e.g., resolution, Dmax, grayscale). This implies a standalone evaluation of the algorithm/device's ability to digitize images according to its specifications. However, the details of this testing (e.g., number of films digitized, specific tests performed) are very brief. It states: "The performance testing results for the Medi-6000 digitizer demonstrated that the device meets its intended use specifications and therefore meets the requirements necessary for its intended use as a component of a PAC or Teleradiology system." This suggests internal testing was conducted on the device's technical performance characteristics.

7. Type of Ground Truth Used:

For the performance specifications (resolution, Dmax, grayscale), the "ground truth" would likely be technical measurements against established industry standards or calibrated test patterns. For example, resolution would be measured using line pair patterns, and Dmax would be measured with calibrated densitometers. There is no mention of expert consensus, pathology, or outcomes data as ground truth for this device, which is primarily a hardware digitizer.

8. Sample Size for the Training Set:

The document does not mention a training set as this device is a film digitizer, not an AI/machine learning algorithm that requires training data.

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

This question is not applicable as there is no training set for a film digitizer.

Summary of Findings:

The 510(k) summary for the Microtek Medi-6000 Medical Image Digitizer is primarily a submission for a hardware device seeking substantial equivalence to existing predicate film digitizers. The "performance testing" described is focused on the device's technical specifications (resolution, dynamic range, grayscale) rather than a clinical study evaluating diagnostic accuracy or reader performance using digitized images. Therefore, many of the typical elements expected for an AI/CADe device study (like test set size, expert ground truth, MRMC studies, or training sets) are not present or not relevant in this document. The acceptance criteria are essentially the device's specifications aligning with or improving upon those of the predicate devices.

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Model 2908 Mammo pro is intended for covering analog Medical x-ray films to digital images.

The device is indicated for the digitization of mammography Imaged for review and analysis, but not as the sole basis for screening or diagnosis.

The device is indicated for the digitization of mammography images for storage and display not for primary image diagnosis.

Film Digitizer, Model 2908 Mammo Pro

The provided document is a 510(k) clearance letter from the FDA for a medical image digitizer (Film Digitizer, Model 2908 Mammo Pro). It does not contain details about specific acceptance criteria or a study proving the device meets those criteria. The letter primarily states that the device is substantially equivalent to legally marketed predicate devices and outlines general regulatory requirements.

Therefore, I cannot provide the requested information as the source document does not contain it. The information requested (acceptance criteria table, sample sizes, expert qualifications, adjudication methods, MRMC study details, standalone performance, ground truth types, and training set details) are typically found in the 510(k) submission document itself, or in associated test reports, which are not part of this clearance letter.

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The CDCR5020s,UCCR7001 and TCCR7001 are intended to be used in radiological practices as laser digitizers for phosphor storage plate. The images are sent for processing to a PC.

1. Model CDCR 5020s This device is used on cassette based x-ray systems using the standard Buckey exist on every system. After exposing the cassette which includes the phosphor plate ,the plate is digitized and the digital image is transferred to the PC for processing
2. Models UCCR 7001 ,TCCR 7001

With these two devices the organ or object for x-ray exposure are positioned In the front of the device (near the phosphor plate which is positioned into the device) and after exposure the digitizing process is taking place,then the Digital image is transferred to the PC for processing.

The only difference in the models are the way of use eather Vertical for Upright or horizontal under the Radiographic Table.

ALL ABOVE MODELS ARE NOT TO BE USED FOR MAMMOGRAPHY

Not Found

The provided text is a 510(k) summary and associated FDA correspondence for "CDCR 5020s, UCCR 7001, and TCCR 7001 CR Readers - COMPUTED RADIOGRAPHY IMAGING SYSTEMS" manufactured by CR TECH LTD.

It outlines that the devices are substantially equivalent to predicate devices, and describes their intended use in radiological practices as laser digitizers for phosphor storage plates. The document, however, focuses on regulatory submission and does NOT contain information regarding acceptance criteria, device performance studies, sample sizes, expert qualifications, adjudication methods, multi-reader multi-case studies, standalone performance, or ground truth details.

Therefore, I cannot fulfill your request for a table of acceptance criteria and reported device performance or details about a study proving the device meets those criteria, as this information is not present in the provided text.

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The NAI Tech Products Medical Digital Recorder is intended for use in Radiology applications to capture and digitize images and record them to single or multiple in the form of CD (Compact Disc) or DVD (Digital Video Disc).

Medical Digital Video Recorder

The provided text is a 510(k) clearance letter from the FDA for a "Medical Digital Video Recorder." It does not contain the specific technical details about acceptance criteria or a study proving those criteria are met for the device itself.

The document states that the device is "intended for use in Radiology applications of capture and digitize video images and record them to single or multiple media in the form of CD (Compact Disc) or DVD (Digital Video Disc)." This describes the function, but not performance metrics or how performance was validated.

Therefore, I cannot provide the requested information based solely on the text provided. The acceptance criteria and the study results would typically be contained within the 510(k) submission document itself, which is not fully included here.

However, I can deduce what types of information would typically be present in such a study for this kind of device, if it were available:

• Acceptance Criteria: For a medical digital video recorder, common acceptance criteria would likely revolve around image quality, recording accuracy, data integrity, and compliance with relevant standards. Examples might include:

  • Resolution preservation (e.g., recorded image resolution matches input resolution).
  • Frame rate integrity (e.g., no dropped frames during recording).
  • Color accuracy (e.g., recorded colors are perceptually similar to input).
  • Absence of artifacts (e.g., compression artifacts, noise introduction).
  • Successful recording and playback rate (e.g., 99.9% successful recordings).
  • Data permanence on recorded media (e.g., ability to reliably retrieve data after X years).
  • Compliance with DICOM or other relevant medical imaging standards for interoperability.

• Study Design (Hypothetical, based on device type):

  • Device Performance: Assessment would involve recording various medical video sources (e.g., fluoroscopy, endoscopy, ultrasound, etc.) and evaluating the output against the input.
  • Ground Truth: For a device that digitizes and records, the "ground truth" would likely be the original analog or digital video signal itself, as observed on a calibrated monitor or measured electronically.
  • "Experts": For image quality assessment, radiologists, clinicians, or imaging scientists might subjectively evaluate recorded media, or objective metrics could be used. For data integrity, technical experts would verify bit-for-bit accuracy.
  • Sample Size: This would depend on the statistical power needed to demonstrate compliance with the acceptance criteria. For a variety of medical video types and recording durations.
  • Data Provenance: Could be a mix of synthetic data, clinical data from various institutions (potentially retrospective), and test patterns.
  • Adjudication Method: If subjective image quality assessment was used, a consensus method (e.g., 2+1 or 3+1) among experts would be common.
  • MRMC Comparative Effectiveness: Highly unlikely for this type of device. An MRMC study assesses the impact of a diagnostic tool on human reader performance. A video recorder's primary function is data capture and storage, not diagnostic interpretation. It doesn't "improve" human readers in the same way an AI diagnostic algorithm might.
  • Standalone Performance: Yes, the performance would be standalone – the algorithm / device would be judged on its ability to accurately capture and store the video, without direct human-in-the-loop interpretation being part of its primary function.
  • Training Set: For a digital video recorder, extensive "training" in the AI sense is unlikely. The device likely relies on established digital signal processing, compression algorithms, and hardware. If there were any adaptive components, the "training data" would be technical specifications and various video inputs used for calibration and testing during development.
  • How Ground Truth for Training Set was Established: Again, not applicable in the AI sense. Ground truth for calibration and testing would be established by comparing processed video to known input video signals, using objective metrics.

In summary, the provided document is a regulatory clearance letter, not a technical report detailing the performance study. To get the specific information requested, one would need access to the actual 510(k) submission and its supporting technical documentation.

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The Ultrasound Capture System™ (UCS) system captures analog video and audio from any ultrasound with a composite or S-video output and converts the captured video to DICOM image and audio files. UCS software runs on a standard "off-the-shelf" personal computer running the Windows XP operating system and a high-resolution video acquisition interface.

The Ultrasound Capture System™ (UCS) system captures analog video and audio from any ultrasound with a composite or S-video output and converts the captured video to DICOM image and audio files. UCS software runs on a standard "off-the-shelf" personal computer running the Windows XP operating system and a high-resolution video acquisition interface.

1. Allows demographic (Patient Name, ID etc.) input consistent with the DICOM standard for image storage.
2. Performs automatic image acquisition triggered by the cine-pedal.
3. Performs network transmission of images in DICOM format to a DICOM compatible storage system.

The provided text describes the Heartlab Inc. Ultrasound Capture System (UCS), a device for capturing analog ultrasound video and audio and converting it to DICOM files.

Here's an analysis of the acceptance criteria and the study (or lack thereof) based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance:

The document describes the design control activities that were performed to ensure the device met its specifications. However, it does not explicitly list quantitative acceptance criteria for specific performance metrics (e.g., image quality, conversion speed, accuracy of DICOM conversion) and their corresponding reported device performance values in a direct comparison.

Instead, the document states:
"All verification and validation activities were performed by the designated individual(s), and the results demonstrated that the predetermined acceptance criteria were met."

This implies that acceptance criteria were established internally as part of the design control process, but the specific details and the performance against them are not disclosed in this 510(k) summary. The summary focuses on demonstrating substantial equivalence to predicate devices based on functional characteristics and adherence to industry standards.

The closest to "acceptance criteria" are the mentioned design control measures and adherence to standards:

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

The document does not specify a sample size for a test set in the context of clinical performance or diagnostic accuracy. The testing described (unit, integration, acceptance, performance) likely refers to software and system functionality testing, not a clinical study involving a dataset of patient images. Therefore, details like country of origin or retrospective/prospective nature are not applicable for this submission.

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

Since there's no mention of a clinical or image-based test set requiring ground truth established by experts, this information is not provided. The device converts analog signals to DICOM; it does not perform diagnostic interpretation.

4. Adjudication method for the test set:

Not applicable, as no clinical or image-based test set requiring expert adjudication 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:

No MRMC study was conducted or mentioned. This device is an image capture and conversion system, not an AI-powered diagnostic or assistive tool for human readers.

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

The device itself is a standalone system in terms of its function (capturing and converting). The performance testing mentioned ("Performance testing" under Design Control) would assess its ability to perform these functions independently. However, this is not an "algorithm only" in the sense of an AI algorithm performing a diagnostic task. The document implies that the system's performance (e.g., successful capture, correct DICOM conversion, maintaining specified compression ratios) was verified, but specific quantitative results are not provided in this public summary.

7. The type of ground truth used:

Given the nature of the device (capture and conversion), the "ground truth" for its verification and validation would likely involve:

• Known input signals: Using standardized analog video/audio signals (e.g., test patterns, known cine loops from an ultrasound machine) to verify correct capture.
• DICOM standard conformance: Verifying that the output files adhere strictly to the DICOM 3.0 standard.
• Data integrity: Comparing captured digital images/audio with the original analog source to ensure no loss or degradation beyond expected compression.
• Metadata accuracy: Verifying that demographic and study information is correctly embedded in the DICOM headers.

No clinical "ground truth" (e.g., pathology, outcomes data, expert consensus on disease presence) is relevant for this type of device.

8. The sample size for the training set:

Not applicable. This device is an image capture and conversion system, not a machine learning or AI algorithm 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 this device.

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