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MR-CT VVA is indicated for use in clinical settings where more reproducible than manually derived quantified results are needed to support the visualization and analysis of MR and CT images of the heart and blood vessels for use on individual patients with cardiovascular disease. Further, MR-CT VVA allows the quantification of T2* in MR images of the heart and the liver. Finally, MR-CT VVA can be used for the quantification of cerebral spinal fluid in MR velocity-encoded flow images.

When the quantified results provided by MR-CT VVA are used in a clinical setting on MR and CT images of an individual patient, they can be used to support the clinical decision making for the diagnosis of the patient. In this case, the results are explicitly not to be regarded as the sole, irrefutable basis for clinical diagnosis, and they are only intended for use by the responsible clinicians.

MR-CT VVA (MR-CT Vessel and Ventricular Analysis) is image post-processing software for the viewing and quantification of MR and CT images of blood vessels, of the heart and MR images of the liver and cerebral spinal fluid. Semi-automatic contour detection forms the basis for the analyses. Its functionality is independent of the type of vendor acquisition equipment. The analysis results are available on screen and can be exported in various electronic formats.

MR-CT VVA has been developed as a standalone application to run on a Windows based operating system. The import of images and the export of analysis results are via CD / DVD, a PACS or network environment.

MR-CT VVA has a modular structure that consists of its previously cleared predicate devices: MRI-MASS, CT-MASS, MRI-FLOW, CMS-VIEW and MRA-CMS. MR-CT VVA comprises their respective functionalities for analyzing the blood vessels and the heart. In addition, MR-CT VVA includes new functionality for the 3D review of MR volumetric data.

The provided text does not contain detailed information about specific acceptance criteria and the results of a study proving the device meets those criteria. It primarily focuses on the regulatory submission process and the substantial equivalence to predicate devices.

However, I can extract information related to the device description, intended use, and general statement about testing and validation.

Here's an attempt to answer your request based on the available text, with caveats for missing information:

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

The document does not explicitly list specific acceptance criteria in a table format nor does it provide a table of reported device performance metrics against those criteria. It broadly states that "Testing and validation have produced results consistent with design input requirements."

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

The document does not provide details on the sample size used for the test set, the country of origin of the data, or whether it was retrospective or prospective.

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 in the document.

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

This information is not provided in the document.

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 document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study or any effect size related to human reader improvement with or without AI assistance. The device is described as providing "more reproducible than manually derived quantified results," implying an improvement over manual methods, but no study details are given.

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

The document states: "MR-CT VVA has been developed as a standalone application to run on a Windows based operating system." This confirms that a standalone algorithm is at the core of the device. However, regarding performance, it also mentions that analysis results are based on "contours that are either manually drawn by the clinician or trained medical technician who is operating the software, or automatically detected by the software and subsequently presented for review and manual editing." This indicates that while the software has standalone capabilities for automatic detection, it's designed to be used with human review and potential editing, implying a human-in-the-loop component in its typical clinical use. Standalone performance data (algorithm only) is not provided.

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

The document does not specify the type of ground truth used for testing or validation.

8. The sample size for the training set

The document does not provide information about the sample size used for the training set.

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

The document does not provide information on how the ground truth for the training set was established.

Summary of available information related to performance and validation:

The document states:

• Device Description: "Semi-automatic contour detection forms the basis for the analyses."
• Intended Use: "These analyses are based on contours that are either manually drawn by the clinician or trained medical technician who is operating the software, or automatically detected by the software and subsequently presented for review and manual editing."
• Indications for Use: "MR-CT VVA is indicated for use in clinical settings where more reproducible than manually derived quantified results are needed to support the visualization and analysis of MR and CT images of the heart and blood vessels..."
• Conclusions: "Testing and validation have produced results consistent with design input requirements. MR-CT VVA is a software-only device for which there no applicable mandatory performance standards." And "Medis concludes that MR-CT VVA is a safe and effective medical device, and is at least as safe and effective as its predicate devices."

In essence, the submission asserts that the device is safe, effective, and meets its design requirements, partly evidenced by its "more reproducible than manually derived quantified results." However, the specific study details (methodology, sample sizes, ground truth establishment, expert qualifications, and quantitative performance metrics against acceptance criteria) that would prove these claims are not present in the provided text. The submission relies heavily on demonstrating substantial equivalence to predicate devices rather than providing detailed de novo performance study results.

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X-RAY VVA is software intended to be used for performing calculations in X-ray angiographic images of the chambers of the heart and of blood vessels. These calculations are based on contours that are either manually drawn by the clinician or trained medical technician who is operating the software, or automatically detected by the software and subsequently presented for review and manual editing. X-RAY VVA is also intended to be used for performing caliper measurements. The results obtained are displayed on top of the images and provided in reports. The analysis results obtained with X-RAY VVA are intended for use by cardiologists and radiologists: to support clinical decisions concerning the heart and vessels to support the evaluation of interventions or drug therapy applied for conditions of the heart . and vessels. X-RAY VVA is indicated for use in clinical settings where validated and reproducible quantified results are needed to support the calculations in X-ray angiographic images of the heart and of blood vessels, for use on individual patients with cardiovascular disease. When the quantified results provided by X-RA Y VVA are used in a clinical setting on X-ray images of an individual patient, they can be used to support the clinical decisions making for the diagnoiss of the patient or the evaluation of the treatment applied. In this case, the results are explicitly not to be regarded as the sole, intefutable basis for clinical diagnosis, and they are only intended for use by the responsible clinicians.

X-RAY VVA (Vessel and Ventricular Analysis) is image post-processing software for the viewing and quantification of digital x-ray angiographic images of blood vessels and of the chambers of the heart. Semi-automatic contour detection forms the basis for the analyses. Its functionality is independent of the type of vendor acquisition equipment. The analysis results are available on screen, and can be exported in various electronic formats. X-RAY VVA has been developed as a standalone application to run on a Windows based operating system. The import of images and the export of analysis results are via CD / DVD, a PACS or network environment. X-RAY VVA has a modular structure that consists of its previously cleared predicate devices: OCA-CMS, QVA-CMS, QLV-CMS, and CMS-VIEW. X-RAY VVA comprises their respective functionalities for analyzing the blood vessels and the left ventricle. In addition, X-RAY VVA includes new functionality for the analysis of: the right ventricle, stent and sub-segments, coronary anewysms, and bifurcations.

The provided text for K112807 does not contain the detailed information necessary to complete most of the requested fields regarding acceptance criteria and study design. The document is a 510(k) summary focusing on substantial equivalence to predicate devices. It states that "Testing and validation have produced results consistent with design input requirements" but does not elaborate on what those requirements or results were.

Therefore, many of the requested fields cannot be accurately filled based on the provided text.

Here's a breakdown of what can and cannot be answered:

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

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 test set: Not specified in the document.
• Data provenance: Not specified in the document.

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 specified in the document.

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

• Not specified in the document.

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

• A MRMC study is not mentioned in the document. The device is described as "image post-processing software" that assists with quantification, suggesting it's an aid, but no comparative effectiveness study with human readers is described regarding improvement with or without AI assistance.

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

• The document implies that the software's automatic contour detection is presented for review and manual editing, indicating a human-in-the-loop workflow. A standalone performance study of the algorithm alone is not described.

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

• Not specified in the document.

8. The sample size for the training set

• Not specified in the document.

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

• Not specified in the document.

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QPlaque MR is a post-processing software application that is intended to assist trained cardiologists and radiologists in the assessment of atherosclerosis. The software is intended in particular to aid in assessing vessel wall thickness and remodeling in the carotid arteries I OPlaque MR post-processes multi-spectral MR images to semi-automatically determine the boundaries of the lumen and outer vessel wall, and provides editing tools for manual drawing of plaque components. The software enables area and volume measurements of the vessel wall as well as quantification of user-indicated areas.

QPlaque MR results can be used to support the decision-making process in clinical practice and to support conclusions in clinical trials.

QPlaque MR is able to read DICOM MR images from all major MR vendors. Vessel analysis data, generated by semi-automatic segmentation, detected stenosis and quantitative results can be saved in separate files enabling the comparison of results from different users.

Radiologists, cardiologists and technicians use the QPlaque MR analytical software package to obtain objective and reproducible results. The obtained results may be used to support the interpretation of MR data, or they are used in the evaluation of follow-up studies and the effectiveness of treatment.

In clinical practice QPlaque MR is used on workstations in review rooms or integrated in a PACS environment.

This document does not contain the detailed information necessary to answer all parts of your request. It is a 510(k) summary and FDA clearance letter, which primarily focuses on demonstrating substantial equivalence to predicate devices rather than providing a detailed study report of a device's performance against specific acceptance criteria.

Here's what can be extracted from the provided text, and where information is missing:

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

This information is not present in the provided document. The 510(k) summary focuses on the device's indications for use and substantial equivalence to existing devices, not on a performance study with defined acceptance criteria.

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 present in the provided document.

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 present in the provided document.

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

This information is not present in the provided document.

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

This information is not present in the provided document. The device is described as an "auxiliary tool" to "support the interpretation" and "support the decision-making process," which implies human-in-the-loop, but no MRMC study or effectiveness data is provided.

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

This information is not explicitly stated as a standalone performance study. The device is intended to "assist trained cardiologists and radiologists" and its results "may be used to support the decision-making process." This indicates it's designed to be used with a human in the loop, rather than operating in a fully standalone capacity.

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

This information is not present in the provided document.

8. The sample size for the training set

This information is not present in the provided document.

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

This information is not present in the provided document.

In summary, the provided document is a 510(k) substantial equivalence submission, which typically does not include the detailed study design, acceptance criteria, and performance results that would be found in a full clinical or technical validation report. The focus here is on demonstrating that the new device is as safe and effective as previously cleared devices, largely by having similar technology and intended use.

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QAngio CT software solution has been developed for the objective and reproducible analysis of vessels in CTA images. It enables the quantitative analysis of CT angiograms based on automated segmentation. More specifically, QAngio CT can be used to quantify a number of lesion characteristics. QAngio CT is intended for use as an auxiliary tool in assessing CTA studies in clinical practice and in clinical trials. The analysis results obtained with QAngio CT are to be interpreted by cardiologists and radiologists.

QAngio CT is able to read DICOM CT images from all major CT scanner vendors. Vessel analysis data, generated by automated (and/or manual) segmentation, detected stenosis, and quantitative results, can be saved in separate files enabling the comparison of results from different users. Radiologists, cardiologists and technicians use the QAngio CT analytical software package to obtain objective and reproducible results. The obtained results may be used to support the interpretation of CTA data, or they are used in the evaluation of follow-up studies and the effectiveness of treatment. In clinical practice the QAngio CT software is used on workstations in review rooms or integrated in a PACS environment.

The provided text does not contain information regarding the acceptance criteria, specific study details (sample sizes for test and training sets, data provenance, number and qualifications of experts, adjudication methods, MRMC studies, or standalone performance), or the type of ground truth used.

The document is a 510(k) summary for a medical device called QAngio CT, which is a software for the quantitative analysis of CT angiograms. It describes the device's intended use and claims substantial equivalence to a predicate device. It also includes the FDA's clearance letter.

Therefore, I cannot provide the requested table and study details based on the given input.

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The QBrain software has been developed for the objective and reproducible analysis of MR images of the brain. It performs quantitative analysis of MR brain images based on automatic segmentation. More specifically, it quantifies the volumes of intracranial cavities, areas that contain cerebrospinal fluid (CSF), and white matter hyperintensities (lesions). These parameters should only be used by trained medical professionals in clinical practice and to reach conclusions in clinical trials.

The QBrain software has been developped for the onalyses on MR brain images of based on automatic segmentation. More specifically, it quantifies the volumes of based on automatic segmentation: more openiture, more openiture (CSF), and white matter hyperintensities (lesions).

The provided text (K050703) describes the QBrain software, which performs automatic quantitative analysis of MR brain images, specifically quantifying the volumes of intracranial cavities, cerebrospinal fluid (CSF), and white matter hyperintensities (lesions).

However, the provided document does not contain information regarding traditional acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds) or a formal study designed to demonstrate performance against such criteria.

Instead, the submission emphasizes substantial equivalence to an existing predicate device (IQuantify workstation software, K011196). The "Summary of Safety and Effectiveness" primarily focuses on the device description, intended use, and a declaration of safety based on internal development, risk analysis, and validation tests. There is no detailed study methodology, test set characteristics, or a table of acceptance criteria with reported performance.

Therefore, I cannot fulfill the request for a table of acceptance criteria and reported performance, sample sizes used, number of experts, adjudication methods, MRMC study details, or standalone performance. The document only implicitly "proves" acceptance by asserting substantial equivalence and stating that "validation and validation tests" were performed, but without detailing their scope or results.

Here's a breakdown of what can be extracted from the provided text based on your questions, with the understanding that robust study details are absent:

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

• Acceptance Criteria: Not explicitly stated in terms of performance metrics (e.g., accuracy thresholds, sensitivity, specificity). The primary "acceptance" mechanism implied is demonstrating substantial equivalence to a predicate device.
• Reported Device Performance: No specific performance metrics (e.g., percentages, F1 scores, absolute error) are reported in the document.

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

• Sample Size (Test Set): Not specified.
• Data Provenance: Not specified.

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

• Number of Experts: Not specified, as no formal ground truth establishment for a test set is detailed.
• Qualifications of Experts: Not specified.

4. Adjudication method for the test set:

• Adjudication Method: Not specified.

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

• MRMC Study: Not mentioned or described. The document states a primary use is "quantitative values support the diagnostic and/or therapy response" and "should only be used by trained medical professionals in clinical trials," but does not present a study comparing human performance with and without AI assistance.
• Effect Size: Not applicable, as no MRMC study is described.

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

• Standalone Study: No specific standalone performance study is described or reported with metrics. The device's function is "automatic segmentation," implying standalone capability, but its performance is not quantified.

7. The type of ground truth used:

• Type of Ground Truth: Not specified in detail for any study. The document mentions "mask data, generated by automatic segmentation and/or manual editing" as input for results, but does not clarify how a "ground truth" for evaluating the system's accuracy was established.

8. The sample size for the training set:

• Sample Size (Training Set): Not specified.

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

• Ground Truth Establishment (Training Set): Not specified.

Summary based on available information:

The K050703 submission for QBrain primarily relies on demonstrating substantial equivalence to a predicate device (IQuantify workstation software, K011196) rather than providing detailed performance studies with quantitative acceptance criteria, test sets, and ground truth methodologies. The document emphasizes the device's functionality (automatic quantification of brain structures) and its intended use by trained medical professionals in clinical trials, but it lacks the specific data points requested in your prompt regarding detailed study designs and outcomes.

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RSA-CMS has been developed for the objective and reproducible analysis on digital roentgen images (DICOM CR or DX) or digitised images in a PACS environment.

Orthopedic specialist and core labs use the RSA-CMS standalone analytical software package in image post-processing for the evaluation of new implant designs, coatings and new cementation techniques in clinical trials.

When interpreted by trained physicians these parameters may be useful to derive conclusions from these clinical trials.

RSA-CMS is a software package that "automatically" performs Roentgen Stereophotogrammetric Analysis (RSA) in digital images. This software package runs on a PC with the Windows 2000 or XP operating system.

Here's an analysis of the provided text to extract the requested information about the acceptance criteria and the study that proves the device meets those criteria:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state defined acceptance criteria in a quantitative manner (e.g., minimum accuracy, sensitivity, or specificity). Instead, it relies on demonstrating substantial equivalence to a predicate device and highlighting the inherent accuracy of the underlying methodology (RSA).

However, the document does report on the general "accuracy" of RSA, which the device implements:

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

The document does not specify a distinct "test set" sample size or its provenance for the RSA-CMS device itself. The reported accuracy figures are attributed to the general "RSA technique" developed by Selvik in 1974. It describes RSA-CMS as a software package that "automatically" performs this technique.

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

Since a dedicated test set and its ground truth establishment are not described for RSA-CMS, this information is not available in the provided text. The accuracy figures for the RSA technique are described as "reported accuracy," implying they come from established literature or prior research on the technique, not a new study specific to this submission.

4. Adjudication Method for the Test Set

As no specific test set or ground truth establishment is detailed for RSA-CMS, an adjudication method is not mentioned.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not mentioned in the provided document. The device is described as a standalone analytical software package.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study Was Done

Yes, the device is described as a "standalone analytical software package" that "automatically performs" the RSA analysis. Its reported accuracy is based on the inherent accuracy of the RSA technique itself. Therefore, the performance described is standalone (algorithm only).

7. The Type of Ground Truth Used

The ground truth for the reported accuracy figures of the RSA technique (which RSA-CMS implements) is not explicitly stated but implicitly refers to the inherent accuracy of the stereophotogrammetric analysis method itself, likely established through rigorous measurement and calibration standards in the field. It is a highly accurate method for dimensional measurement.

8. The Sample Size for the Training Set

The document does not mention a training set or its sample size for RSA-CMS. This suggests that the software's functionality is based on implementing a well-established and validated algorithm (RSA) rather than a machine learning model that requires a dedicated training phase with labeled data.

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

As no training set is mentioned, information on how its ground truth was established is not available.

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Ortho-CMS is an orthopaedic analysis software tool. It has been developed to optimize preoperative planning through digital prosthesis templating and to enable preoperative and postoperative measurements in digital or digitized X-Ray images. Ortho-CMS software is meant solely for use by trained medical personnel.
The intended purposes of Ortho-CMS are:

• Displaying of X-Ray images
• Supporting planning of joint replacement operations
• Supporting clinical diagnoses on implant loosening
• Enabling preoperative and postoperative measurements for clinical and research purposes

In orthopaedics, radiographs are used to diagnose and analyse various kinds of orthopaedic disorders, generative joint conditions, and bone fractures, and to evaluate orthopaedic treatments such as osteotomies and total joint arthroplasty. These arthroplasties need to be evaluated in order to assess the quality of the procedure. Measurements on radiographs of endoprostheses include the assessment of radiolucent lines around the prosthesis, bone growth or bone resorption, position of the prosthesis, motion of the prosthesis relative to the surrounding bone, and the determination of wear of the polyethylene components.
Since the measurements on radiographs are commonly performed manually, considerable intra-observer and inter-observer variation exists. Automation of the measurements might increase the objectivity and speed of the analysis, and decrease the variation of the results. In radiology, digital roentgen imaging techniques are increasingly being used over plain film radiographs. The digital roentgen images (DICOM CR or DX) are easily accessible from a medical picture archive (PACS) through a network connection.
Ortho-CMS supports the radiologist by facilitating the diagnosis of orthopaedic digital images and allows the orthopaedic specialist to perform a pre-surgical planning and a post-surgical evaluation on these images. Further, Ortho-CMS can be deployed as a measurement tool for core-labs that focus on quality assessment of orthopaedic implants or it can be used by bone centres that need to make measurements in donor bone images for joints replacement purposes.

Here's a breakdown of the acceptance criteria and study information for the Ortho-CMS device based on the provided text:

It's important to note that the provided text is a 510(k) summary for a medical device submitted in 2004. As such, it focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed, statistically robust study design with explicit acceptance criteria and detailed performance metrics as might be seen for novel technologies today. The document heavily emphasizes the safety and effectiveness through risk management, verification, and validation, rather than a specific comparative effectiveness study with human readers or standalone AI performance.

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly state specific quantitative acceptance criteria or detailed device performance metrics in the way one might expect for a modern AI-driven device. Instead, the "acceptance criteria" are implicitly tied to demonstrating substantial equivalence and ensuring safety and effectiveness through general software development lifecycle processes.

The document discusses the limitations of manual measurements (intra-observer and inter-observer variation) and suggests that automation might increase objectivity, speed, and decrease variation. However, it doesn't provide threshold values for these improvements.

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

The provided 510(k) summary does not specify a numerical sample size for a test set. It mentions "verification and validation tests (See Appendix E)" and "Evaluations by hospitals and literature (See Appendix F)." This suggests that testing was conducted, but the details of the dataset used (number of cases, data provenance, etc.) are not included in this public summary. Given the year (2004) and the nature of medical device submissions focusing on substantial equivalence for software tools, a large, independent test set with explicit details might not have been a mandatory requirement to the same extent as for a novel diagnostic algorithm today.

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

The document does not specify the number or qualifications of experts used to establish ground truth for any test set. It states the software "supports the radiologist" and "allows the orthopaedic specialist" to perform tasks, and that "The analyses results will be interpreted by the operator, who can choose to accept or reject the tools results." This implies that medical professionals would be involved in evaluating the output, but details on how ground truth was established for a formal validation study are absent from this summary.

4. Adjudication Method for the Test Set

The provided text does not describe any specific adjudication method (e.g., 2+1, 3+1, none) for a test set.

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

The 510(k) summary does not mention a Multi-Reader Multi-Case (MRMC) comparative effectiveness study demonstrating how much human readers improve with AI vs. without AI assistance. The device is presented as a tool to facilitate measurements and planning, rather than a diagnostic AI that provides independent interpretations or directly augments human reader performance in a controlled study comparing assisted vs. unassisted reading. The focus is on increasing objectivity and speed, and decreasing variation, but not a quantified improvement in diagnostic accuracy via an MRMC study.

6. Standalone (Algorithm Only) Performance Study

The document describes Ortho-CMS as an "orthopaedic analysis software tool" that "supports the radiologist" and "allows the orthopaedic specialist" to perform measurements and evaluations. It explicitly states, "The analyses results will be interpreted by the operator, who can choose to accept or reject the tools results." This clearly indicates that the device is not intended to operate in a standalone (algorithm only) manner for making clinical decisions without human oversight. Its role is as a measurement and planning aid for trained medical personnel. Therefore, a standalone performance study as understood for fully automated diagnostic AI would not be applicable or expected for this device.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for any validation studies. Given the device's function involves making measurements and supporting clinical decisions on images, it's plausible that ground truth would involve:

• Expert Consensus/Manual Measurements: Manual measurements performed by multiple experienced orthopaedic specialists or radiologists, potentially with reconciliation, could have served as a reference standard.
• Pathology/Outcomes Data: For supporting diagnoses on implant loosening, potentially clinical outcomes or follow-up pathology could have been considered, but this is speculative given the limited information.

The mention of "Evaluations by hospitals and literature (See Appendix F)" suggests that real-world clinical data and established medical knowledge were used for validation, but the specifics are not detailed.

8. Sample Size for the Training Set

The provided text does not specify a sample size for the training set. The Ortho-CMS, described in 2004, is more likely a rule-based software tool or one that employs traditional image processing algorithms rather than a machine learning or AI model in the modern sense that requires a "training set" for model parameters. If any "training" occurred, it would likely refer to internal development and calibration using a limited set of representative cases, rather than a large, diverse dataset for supervised learning.

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

As the document does not specify a training set or explicitly describe a machine learning model, it also does not detail how ground truth for such a set was established. If traditional image processing techniques were "trained" or calibrated, it would have involved internal testing and parameter tuning, likely based on expert-annotated or known-measurement cases developed by the manufacturer.

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MRA-CMS has been developed for the objective and reproducible analysis of vessels from MRA data sets. The MRA software parameters may be used to semi-automatically determine lumen length, cross sectional parameters and percent stenosis. When interpreted by a trained physician these parameters may be useful in supporting the determination of clinical diagnoses and subsequent clinical decision making processes.

MRA-CMS can be utilized to determine lumen lengths; minimum and maximum cross sectional diameters and percent stenosis. MRA-CMS improves productivity of the clinician by semi-automating the measurement function for routine vascular measurements.

The provided text is a 510(k) summary for the MRA-CMS device. It does not contain the detailed study results, acceptance criteria, or performance data required to complete the requested table and answer many of the questions.

The submission focuses on establishing substantial equivalence to a predicate device (Vital Images, K002519 "Vitrae 2, version 2.1") and discusses general safety and effectiveness without presenting specific quantitative performance metrics beyond the device's capability to measure lumen lengths, cross-sectional diameters, and percent stenosis.

Therefore, I can only provide information based on what is available in the text. Many sections will indicate "Not provided in the text."

Here's the breakdown of the information that can be extracted:

Acceptance Criteria and Device Performance

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

• Sample size for the test set: Not provided in the text.
• Data provenance (e.g., country of origin of the data, retrospective or prospective): Not provided in the text. The document mentions "Evaluations by hospitals and literature (See Appendix F)" but does not detail the nature or source of these evaluations.

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

• Number of experts: Not provided in the text.
• Qualifications of experts: Not provided in the text.

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

• Adjudication method: Not provided in the text.

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

• MRMC study: Not provided in the text. The document refers to the device as "semi-automating the measurement function" and that the "regions-of-interest will be interpreted by the operator, who can choose to accept or reject the outlines, and then decide to use the derived data." This implies a human-in-the-loop workflow, but no comparative effectiveness study results are presented.
• Effect size: Not provided in the text.

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

• Standalone study: Not explicitly stated. The description of MRA-CMS as "semi-automating the measurement function" and requiring operator interpretation and acceptance/rejection of outlines suggests it's not purely a standalone device in its intended clinical use. No standalone performance study metrics are provided.

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

• Type of ground truth: Not provided in the text.

8. The sample size for the training set

• Sample size for the training set: Not provided in the text.

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

• Ground truth establishment for training set: Not provided in the text.

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CT-MASS has been developed for the objective and reproducible analysis of multi-slice, multi-phase left and right ventricular function from cardiac CT data sets. The CT-MASS software package can be used to semi-automatically calculate and display various parameters such as: EDV, BSV, stroke volume, ejection factor, peak ejection and filling rates, myocardial mass, regional wall thickness, as well as well as wall thickening/thinning, and regional wall motion. This is including the axial to short axis reformat.

When interpreted by a trained physician these parameters may be useful in supporting the determination of a diagnosis.

CT-MASS is a professional state-of-the-art analytical software tool designed for UNIX, Linux as well as Windows platforms. CT-MASS facilitates the import and visualization of multi-slice, multi-phase CT data sets encompassing the cardiac chambers via CD-Rom and digital network. This CT-MASS functionality is independent of the CT equipment vendor. CT-MASS provides objective and reproducible global and regional two-, three- and four-dimensional clinically relevant parameters describing left and right ventricular heart function, such as ventricular volumes, regional wall thickness and wall thickening/thinning CT-MASS is intended to support all clinicians, i.e. cardiologists, radiologists, and referring physicians involved in the noninvasive assessment of heart function.

This 510(k) submission for CT-MASS does not contain a specific section detailing the acceptance criteria and a study proving the device meets those criteria with a table of performance metrics. The submission focuses on substantial equivalence to predicate devices and provides general information about verification and validation tests and evaluations by hospitals and literature, but it lacks the granular detail requested.

However, based on the provided text, we can infer some information due to the device's nature as an analytical software tool for cardiac function. The core of its safety and effectiveness relies on its ability to objectively and reproducibly analyze cardiac CT data.

Here’s an attempt to construct the response based on the available information and typical expectations for such a device, while clearly identifying what is stated and what is inferred or missing:

Acceptance Criteria and Device Performance Study for CT-MASS

This 510(k) submission (K033774) for CT-MASS does not explicitly define a table of acceptance criteria or present a detailed study with specific performance metrics against those criteria. The submission primarily focuses on demonstrating substantial equivalence to predicate devices (K013422 "CardIQ Function" and K020796 "CardIQ Analysis III") and asserts that "potential hazards are controlled by a risk management plan for the software development process... including hazard analysis, verification and validation tests." It also mentions "Evaluations by hospitals and literature" support the safety and effectiveness.

Given the intended use of "objective and reproducible analysis of multi-slice, multi-phase left and right ventricular function," the acceptance criteria would implicitly relate to the accuracy and reproducibility of the calculated cardiac parameters.

1. Table of Acceptance Criteria and Reported Device Performance

As specific acceptance criteria and detailed performance metrics are not provided in the submitted document, the table below represents inferred or expected criteria for this type of device, with no reported performance values available in this submission.

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QVA-CMS is developed for the quantitative analysis of vascular morphology in peripheral arteries and is applicable in both research studies and during interventions in the vascular lab. The automated contour detection can be used to standard digital, subtracted and inverted images. The package reduces significantly the intra- and inter-observer variability associated with conventional visual assessment. It also avoids the very time-consuming conventional manual tracing of boundaries. QVA-CMS analytical software is intended to support clinicians. i.e. cardiologists. radiologists, and referring physicians involved in the assessment of X-ray images. When interpreted by trained physicians these parameters may be useful in supporting a clinical decision process.

QVA-CMS is a state-of-the-art analytical software tool designed for Windows operating systems. QVA-CMS analytical software facilitates the import and visualization of X-ray images via CD-ROM and digital network. The QVA-CMS functionality is independent of the X-ray acquisition equipment vendor. QVA-CMS, performing automated contour detection, provides quantitative analysis with objective and reproducible assessment of vascular lesions in selected regions of interest. The analysis results of user's selection can be reported in user-defined configuration, exported in general formats and transported for storage via communication with standard Microsoft office packages.

The provided document, a 510(k) notification for the QVA-CMS analytical software package, is a regulatory submission to the FDA. It declares the device's substantial equivalence to a predicate device (QCA-CMS, K993763) and outlines its intended use and safety. However, this document does not contain the detailed information necessary to fully answer the request regarding acceptance criteria and a specific study proving the device meets those criteria.

Here's an analysis of what can and cannot be extracted from the provided text based on your request:

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

• The document states: "It is the opinion of MEDIS medical imaging systems B.V. that QVA-CMS is safe and potential hazards are controlled by the risk management plan for the software development process (see Appendix C), including hazard analysis (see Appendix D), verification and validation tests (see Appendix E)."
• It also mentions: "Evaluation by hospitals and literature (see Appendix F) supports this statement."

Missing Information: The actual acceptance criteria (e.g., specific quantitative thresholds for accuracy, precision, sensitivity, specificity) and the reported performance values that demonstrate these criteria were met are not detailed within this document. Appendices C, D, E, and F are referenced as containing this information, but they are not included in the provided text.

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

Missing Information: The document explicitly mentions "verification and validation tests" and "Evaluation by hospitals and literature," but it does not specify the sample size of any test set, the provenance of the data (e.g., country of origin), or whether the study was retrospective or prospective.

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)

Missing Information: The document describes the device's function as providing "objective and reproducible assessment of vascular lesions" and improving "interpretation of vascular images for decisions by the clinicians." It also mentions "reducing the risks, due to user variability associated with conventional visual assessment." However, it does not detail the number or qualifications of experts used to establish ground truth for any validation study.

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

Missing Information: The document does not describe any adjudication method used for establishing ground truth or evaluating test set results.

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 document states: "The package reduces significantly the intra- and inter-observer variability associated with conventional visual assessment." and "QVA-CMS analytical software is intended to support clinicians, i.e. cardiologists, radiologists, and referring physicians involved in the assessment of X-ray images. When interpreted by trained physicians these parameters may be useful in supporting a clinical decision process."

Missing Information: While the text hints at a reduction in inter-observer variability, it does not explicitly describe an MRMC comparative effectiveness study where human readers' performance with and without AI assistance was measured. Therefore, an effect size for human improvement with AI cannot be determined from this text. The statement about reduced variability suggests that such a study might have been performed (or is conceptually implied), but the details are not here.

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

• The document describes QVA-CMS as "performing automated contour detection, provides quantitative analysis with objective and reproducible assessment of vascular lesions." This implies the algorithm's ability to operate in a standalone capacity to perform its core function.
• "It is the opinion of MEDIS medical imaging systems B.V. that QVA-CMS is safe and potential hazards are controlled by the risk management plan for the software development process (see Appendix C), including hazard analysis (see Appendix D), verification and validation tests (see Appendix E)." This "verification and validation tests" section would likely include evaluation of the algorithm's standalone performance.

Partial Information: The core function of "automated contour detection" and "quantitative analysis" inherently refers to the algorithm's standalone capability. The document alludes to "verification and validation tests" which would assess this, but does not provide direct results or details of a specific standalone study.

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

Missing Information: The document mentions "objective and reproducible assessment" and "quantify medical images," but does not specify how the ground truth for comparison was established (e.g., whether it was expert consensus, invasive measurement, pathology, or clinical outcomes).

8. The sample size for the training set

Missing Information: The document is primarily focused on regulatory submission and equivalency. It does not provide any details regarding the training set's sample size used for the development of the automated contour detection algorithms.

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

Missing Information: Similar to point 8, the document does not detail how ground truth was established for any training data used in the development of the QVA-CMS software.

In summary:

This 510(k) submission establishes the device's intended use and its substantial equivalence to a predicate device. It references internal validation and testing documents (Appendices C, D, E, F) that would likely contain the specifics requested. However, the provided text itself lacks the detailed technical and clinical study information required to populate a comprehensive table of acceptance criteria, study methodologies, and results. You would need to access those referenced appendices or a more detailed technical report to get this information.

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