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Segment 3DPrint is a software for review and segmentation of images from a medical scanner as well as of medical 3D models. Segment 3DPrint is intended to generate 3D models for diagnostic purposes in both paediativ and adult populations in the field of orthopaedic, maxillofacial, and cardiovascular applications. The models can be used for visualisation, measuring, and treatment planning. Output from Segment 3DPrint can be used to fabrical replical replical replical replical replical replical replical replical by use of additive manufacturing methods. Segment 3DPrint is intended to be used by trained professionals in conjunction with expert clinical judgement.

Seqment 3DPrint is a software for review and segmentation of images from a medical scanner as well as of medical 3D models. Segment 3DPrint is intended to generate 3D models for diagnostic purposes. The models can be used for visualisation, measuring, and treatment planning. Output from Segment 3DPrint can be used to fabricate physical replicas, by use of additive manufacturing methods.

Here's an analysis of the acceptance criteria and study details for the Segment 3DPrint device based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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Segment CMR is a software that display and analyzes medical images in DICOM-format using multi-slice, multi-frame and velocity encoded MR images. Segment CMR provides features for analysis of cardiac function, such as cardiac pumping and blood flow. The ventricular analysis is provided for usage in both pediatric (from newborn) and adult population. Images and associated data analysis can be stored, communicated, rendered, and displayed within the system and across PACS system. The data produced by Segment CMR is intended to support qualified cardiologist, radiologist or other licensed professional healthcare for clinical decision making. It is a support tool that provides relevant clinical data as a resource to the clinician and is not intended to be a source of medical advice or to determine or recommend a course of action or treatment for a patient.

Segment CMR is a software that displays and analyzes multi-slice, multiphase and velocity encoded DICOM compatible medical MR images. Segment CMR provides quantitative measures for analysis of function of the cardiovascular system. The data produced by Segment CMR is intended to be used to support qualified cardiologist, radiologist or other professional healthcare practitioners for clinical decision making. Functional, flow, valve, vessel, and tissue analysis is performed using standardized algorithms and user input. The quantification methods are validated and reproducible. The ventricular analysis is provided for usage in both pediatric (from newborn) and adult population. MR images may be imported from various sources including images stored on portable media, network storage devices, and other vendor systems and supports cardiovascular MR images from all of the major MRI scanner vendors.

The provided text describes the acceptance criteria and a study proving the device meets them for "Segment CMR", a cardiovascular MRI analysis software.

Here's a breakdown of the requested information:

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

The document does not explicitly present a discrete table of acceptance criteria with numerical targets and corresponding device performance metrics. Instead, it makes a general statement about the device's performance relative to reference methods and its predicate device. This is typical for a 510(k) submission where substantial equivalence is demonstrated rather than meeting specific performance thresholds for a novel device.

Acceptance Criteria (Implied from the text):

• Safety and Effectiveness: Segment CMR should be as safe and effective as the predicate devices.
• Agreement with Reference Methods: Values from the evaluated features in Segment CMR should be in good agreement with values from established reference methods.
• No Adverse Events/Complications: No adverse events or complications associated with the device should be observed in studies.
• Acceptable Residual Risk: All identified hazards should be mitigated to accepted levels of residual risk, and the overall risk evaluation should conclude that the risk is acceptable.
• Benefit Outweighs Risk: Specifically for pediatric use, the benefits should outweigh the risks.
• Performance in Accordance with Intended Use: The device performs in accordance with its intended use and similarly to existing cardiovascular MRI image analysis products.
• No Alteration of MRI Data: The device should not alter MRI imaging data in the analytical process.

Reported Device Performance:

• "The results by the studies show that the values from the evaluated features in Segment CMR were in good agreement with values from the reference method." (Page 6)
• "No adverse advents or complications associated with the subject device were observed in the studies." (Page 6)
• "Based on the clinical performance as documented in the performance studies, Segment CMR was found to have a safety and effectiveness profile that is similar to the predicate device." (Page 6)
• "We conclude that the subject device Segment CMR is as safe and effective as the predicate devices." (Page 7)
• "All identified hazards for Segment CMR have been mitigated to accepted levels of the residual risk and the overall risk residual evaluation concluded that the risk of Segment CMR is acceptable." (Page 7)
• "The extension of pediatric use for the left ventricular analysis did not, according to the hazard analysis, increase the overall residual risk and by evaluating safety and effectiveness, the benefits with pediatric use can be considered to outweigh the risks." (Page 7)
• "Segment CMR performs in accordance with its intended use as well as the cardiovascular MRI image analysis products currently on the market." (Page 7)

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 explicitly stated in terms of a specific number of cases or patients. The document refers to "studies" in the plural, implying multiple tests and evaluations.
• Data Provenance: "The studies were performed in US or in Europe." The text does not specify whether the data 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)

• Number of Experts: Not explicitly stated for establishing ground truth.
• Qualifications of Experts: Not explicitly stated. The device is intended to support "qualified cardiologist, radiologist or other licensed professional healthcare practitioners for clinical decision making." This implies that the reference methods or ground truth would be established by such qualified professionals.

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

• Adjudication Method: Not explicitly stated.

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: The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study designed to measure the improvement of human readers with AI assistance. The focus is on the standalone performance of the software in agreement with established methods. The device is presented as a "support tool" that provides data "as a resource to the clinician," implying it does not replace human interpretation but assists it.
• Effect Size of Human Reader Improvement: Not applicable, as no such MRMC study is described.

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

• Standalone Performance: Yes, the performance data presented (e.g., "values from the evaluated features in Segment CMR were in good agreement with values from the reference method") primarily focuses on the standalone performance of the algorithms within the software compared to established reference methods or predicate devices. The software "provides quantitative measures for analysis," suggesting its output is directly compared.

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

• Type of Ground Truth: "establish methods as reference standard." This implies ground truth was based on validated and recognized clinical methods, likely expert interpretations, other validated software, or established measurement techniques. The specific nature (e.g., expert consensus vs. pathology) is not detailed.

8. The sample size for the training set

• Sample Size for Training Set: Not explicitly stated. The document mentions that the predicate device Segment, "on which Segment CMR is built upon, is used by more than 200 research groups," which suggests a large body of data might have been implicitly involved in the development and refinement of the underlying algorithms, but a specific "training set" for this device (Segment CMR) is not quantified.

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

• Ground Truth for Training Set: Not explicitly described. The general statement about "establish methods as reference standard" likely applies to both training and testing, but the process of establishing ground truth specifically for a training set (if one was formally used) is not detailed.

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Segment is a software that analyzes DICOM-compliant cardiovascular images acquired from magnetic resonance (MR) scanners. Segment specifically analyzes the function of the heart and its major vessels using multi-slice, multi-frame and velocity encoded MR images. It provides clinically relevant and reproducible data for supporting the evaluation of the function of the chambers of the heart such as left and right ventricular volumes, ejection fractions, stroke volumes, peak ejection and filling rates, myocardial mass, regional wall thickness, fractional thickening and wall motion. It also provides quantitative data on blood flow and velocity in the arterial vessels and at the heart valves. Segment is tested on MR images acquired from both 1.5 T and 3 T MR scanners. The data produced by Segment is intended to be used to support qualified cardiologist, radiologist or other licensed professional healthcare practitioners for clinical decision making. It is a support tool that provides relevant clinical data as a resource to the clinician and is not intended to be a source of medical advice or to determine or recommend a course of action or treatment for a patient.

Segment is a software for analysis of cardiovascular MR images. Segment provides clinical quantitative data by analyzing multi-slice, multi-phase DICOM compatible cardiovascular MR images. Functional and blood flow analysis is performed using 2D, 3D and 4D data sets using standard algorithms and user input. MR images may be imported from various sources including images stored on portable media, network storage devices, PACS, and other vendor systems and supports cardiovascular MR images from all of the major MRI scanner vendors. Segment can be used for quantitative and qualitative analysis of cardiovascular MR images.

The provided text is a 510(k) Summary for the Medviso AB Segment software, dating from 2009. It focuses on demonstrating substantial equivalence to predicate devices (MRI-MASS and MRI-FLOW) rather than providing detailed acceptance criteria and a specific study proving the device meets those criteria with statistical significance as typically found in more recent submissions for AI/ML devices.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state quantitative acceptance criteria or a table of reported device performance in the way a modern AI/ML device submission would. Instead, the "performance" is implicitly tied to demonstrating substantial equivalence to the predicate devices and general claims of clinical utility and reproducibility.

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

The document does not specify a sample size for any formal "test set" in the context of validating performance against ground truth. The submission predates the common expectations for rigorous AI/ML performance validation studies. It states that "Extensive testing of the software package is performed by programmers, by non-programmers, quality assurance staff and by potential customers prior to commercial release." This refers to internal software quality assurance and user acceptance testing, not a clinical performance validation study with a defined test set and ground truth.

There is no mention of data provenance (e.g., country of origin or retrospective/prospective nature) for any data used in testing.

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

The document does not mention using experts to establish ground truth for a test set. This type of submission relies on the established clinical utility of MRI images and the predicate devices' method of analysis, with the software acting as a tool to aid qualified professionals. The output is "intended to be used to support qualified cardiologist, radiologist or other licensed professional healthcare practitioners."

4. Adjudication Method:

Given that no formal expert-derived ground truth test set is described, there is no mention of an adjudication method (e.g., 2+1, 3+1).

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

No, a MRMC comparative effectiveness study was not done (or at least, not reported in this 510(k) summary). The submission focuses on substantial equivalence based on features and functionality, not on comparing reader performance with and without AI assistance. Therefore, no effect size calculation for human reader improvement with AI assistance is provided.

6. Standalone (Algorithm Only) Performance Study:

No, a standalone performance study was not explicitly described in the context of quantitative performance metrics against a defined ground truth. The document states, "Segment is used for image analysis and quantification... and all of the information is subject to his/her oversight and control." This indicates that the device is intended as a support tool, not a fully autonomous diagnostic algorithm. The "extensive testing" mentioned is internal software testing and potential customer feedback, not a formal standalone clinical performance study.

7. Type of Ground Truth Used:

No specific "ground truth" (e.g., pathology, outcomes data, or expert consensus specifically for a test set) is described as being used to validate the device's performance. The basis for safety and effectiveness is largely on clinical acceptance of MRI imaging itself and substantial equivalence to existing, legally marketed devices. The software "does not in any way alter the images" and provides "clinically relevant numeric computations."

8. Sample Size for the Training Set:

The document does not mention a training set or its sample size. This is consistent with the era and type of device; Segment is an image analysis software based on "standard algorithms and user input," rather than a machine learning model that requires a "training set" in the modern sense.

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

As no training set is discussed, no information is provided on how its ground truth would have been established.

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