Search Results

AutoMIStar is a medical image post-processing software package to be used by trained professionals, including but not limited to physicians and medical technicians. The software runs on a standard "off-the-shel" computer or a virtual platform with Windows operating system, and can be used to perform image viewing, processing and analysis of brain images acquired by DICOM compliant imaging devices.

AutoMIStar provides general viewing of DICOM images, with analysis capabilities for functional imaging data including a CT Perfusion Module and a DWI (diffusion-weighted MRI) Module.

The CT Perfusion Module is used for visualization and analysis of contrast enhanced CT Perfusion (CTP) dataset, showing properties of changes in contrast over time. This function includes calculation of various parameters related to tissue flow (perfusion) and tissue blood volume.

The DWI Module is used to visualize local water diffusion properties from the analysis of diffusion-weighted MRI data.

AutoMIStar is a software package that provides visualization and processing of medical imagesacquired by CT (Computed Tomography) and MRI (Magnetic Resonance Imaging) scanners, as an aid to physician diagnosis. It can be installed and run on a PC, laptop or virtual machine running a Microsoft Windows operating system. It provides viewing, quantification, analysis and reporting capabilities.

AutoMIStar also provides processing and analysis of functional and dynamic imaging datasets including CT Perfusion (CTP) and diffusion-weighted MRI (DWI). It provides tools to perform the following type of advanced analysis:

• time intensity plots for dynamic time courses;
• volumetry of threshold maps;
• calculation of mismatch between volumes of different threshold maps;

AutoMIStar can be configured to connect to various DICOM devices (modality scanners, workstations, PACS) via the hospital medical imaging network to receive CT or MRI images as they become available. It supports transfer of DICOM images using the DICOM standard network protocol, and allows import of DICOM images from storage media. It provides automated workflow to processes received data and sends post-processed results to designated DICOM devices. It can also be configured to send post-processed results to designated recipients via email through the hospital email system.

AutoMIStar is a DICOM-compliant PACS softwarethat provides functionality to transfer, process, and display of CT and MR imaging data including dynamically acquired CT perfusion imaging data and diffusion-weighted MRI (DWI).

AutoMIStar runs on a standard "off-the-shelf" computer with Windows operating system, and can seamlessly integrate into an existing radiological data network. AutoMIStar is entirely independent from CT, MRI, or PACS platforms.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state quantitative acceptance criteria in a structured table. Instead, it describes general performance goals and compares performance to a predicate device and other software packages.

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

• Sample Size: The document does not explicitly state the specific number of cases or images used for the test set. It mentions "published perfusion phantom and diffusion phantom data."
• Data Provenance: The data used for testing was derived from "published perfusion phantom and diffusion phantom data." This suggests the data is likely from research studies or publicly available datasets specifically designed for validating perfusion and diffusion analysis software. The country of origin is not specified, but the nature of phantom data implies it's synthetic or laboratory-generated rather than patient-specific. The data would be considered retrospective in the sense that it was pre-existing for testing purposes, not prospectively collected for this device's validation.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The ground truth was established by "published perfusion phantom and diffusion phantom data," implying that the ground truth was inherent to the design of these phantoms, which are validated by established methodologies in the field.

4. Adjudication Method for the Test Set:

• Adjudication Method: Not applicable/not specified. Since the ground truth was established by phantom data, there was no need for human expert adjudication of individual cases in the traditional sense. The phantom data itself provided the ground truth values.

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

• Was an MRMC study done? No. The document does not mention an MRMC study or any comparison of human readers with vs. without AI assistance. The performance testing focused on the standalone algorithm's accuracy against phantom data and comparison to other software.
• Effect size of human readers improve with AI vs without AI assistance: Not applicable, as no MRMC study was conducted.

6. Standalone (Algorithm Only) Performance:

• Was standalone performance done? Yes. The entire performance data section describes the evaluation of the AutoMIStar device itself against ground truth from phantom data. This is a standalone algorithm performance assessment. The "regression analysis between the output of the AutoMIStar device and the ground truth values" is a direct measure of its standalone accuracy.

7. Type of Ground Truth Used:

• Type of Ground Truth: The ground truth was established using phantom data (specifically, "published perfusion phantom and diffusion phantom data"). This type of ground truth provides precise, known values for the parameters being measured (e.g., CBF, CBV, MTT, ADC), which is ideal for quantitative validation.

8. Sample Size for the Training Set:

• Sample Size: The document does not provide any information regarding the sample size of a training set. This suggests that the device, or at least the part being validated, does not rely on a machine learning model that requires a distinct training phase with labeled data in the way a deep learning algorithm would. It appears to be based on established algorithms for perfusion and diffusion analysis.

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

• How Ground Truth for Training Set Established: Not applicable. As no training set information is provided, the method for establishing its ground truth is also not mentioned.

Ask a specific question about this device

MIStar is a software package that provides manipulation, visualization and processing of medical images in a diagnostic imaging setting. It allows transfer of DICOM 3.0 images over a medical imaging network, and can receive digital images and data from various sources (including but not limited to CT, MR, NM, and PT).

MIStar supports the analysis of dynamic CT and MR images acquired during and after the injection of a compact bolus of contrast media. It allows visual inspection of time intensity curves and calculation of parametric parameters (i.e., area under curve, time to peak, maximum slope of enhancement, etc). It also calculates various blood perfusion related parameters such as blood flow (BF), blood volume (BV), mean transit time (MTT) and capillary permeability from dynamic CT images. This software provides supplemental information to those images extracted from CT and MR temporal datasets and will aid in the assessment of the extent and type of perfusion, blood volume and capillary permeability changes related but not limited to stroke or tumor angiogenesis and be helpful in therapy monitoring.

MIStar is intended to be used by trained medical professionals, including but not limited to licensed radiologists, technologists and clinicians, and for the rendering clinical diagnosis.

MIStar is a software package, which runs on an Intel-based PC platform. It allows manipulation, visualization and processing of medical images acquired with various clinical scanners and stored in DICOM and for other proprietary formats. MIStar allows transfer of DICOM 3.0 images over a medical imaging network.

MIStar also provides post-processing of dynamic CT and MR images acquired during and after the injection of a compact bolus of contrast media, where the contrast media acts as a pure intravascular tracer. It allows visual inspection of time intensity curves and calculation of parameters (i.e., area under curve, time to peak, maximum slope of enhancement, etc). It also allows calculation of various perfusion related parameters (i.e. regional blood flow, regional blood volume, mean transit time and capillary permeability) from dynamic CT data. The results are displayed in a user-friendly graphic format as parametric images that provide supplementary information for diagnosis purposes.

The provided document does not contain specific acceptance criteria or a study demonstrating the device meets such criteria.

The document K043350 is a 510(k) premarket notification for the MIStar device, which is a software package for manipulating, visualizing, and processing medical images, particularly for analyzing dynamic CT and MR images to calculate perfusion-related parameters.

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

1. Table of Acceptance Criteria and Reported Device Performance:

• Acceptance Criteria: Not specified in the document. The submission focuses on demonstrating substantial equivalence to predicate devices rather than meeting predefined performance acceptance criteria.
• Reported Device Performance: Not explicitly stated as a table of performance metrics. The document mentions that the algorithms used are "similar to" predicate devices, but no quantitative performance data (e.g., accuracy, sensitivity, specificity for perfusion parameter calculation) is provided.

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

• Test Set Sample Size: Not mentioned. No specific test set for evaluating the device's performance is described.
• Data Provenance: Not mentioned.

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

• Number of Experts: Not mentioned.
• Qualifications of Experts: Not mentioned.
• Ground Truth Establishment: No information on how ground truth would be established for any performance evaluation.

4. Adjudication Method:

• Adjudication Method: Not mentioned, as no study involving expert interpretation or adjudication is described.

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

• MRMC Study: No MRMC comparative effectiveness study is mentioned. The document primarily focuses on establishing substantial equivalence based on functional similarity to predicate devices. There is no information about human readers improving with or without AI assistance, as the device itself is a post-processing tool.

6. Standalone (Algorithm Only) Performance Study:

• Standalone Study: No standalone performance study is mentioned. The document states that the software "requires the intervention of a physician in setting necessary parameters and in assessing resultant images," implying it's not a fully standalone diagnostic algorithm. While it performs calculations, there's no study to evaluate the accuracy of these calculations against a gold standard.

7. Type of Ground Truth Used:

• Type of Ground Truth: Not mentioned. No ground truth is described for any performance evaluation.

8. Sample Size for the Training Set:

• Training Set Sample Size: Not applicable/mentioned. The document describes the software development process as including "verification and validation according to the procedures described in the Software Information section: Software Development Process," but details about training data for any machine learning components (if present, which is not explicitly stated in detail for the core algorithms) are absent. This device predates widespread AI/ML regulatory frameworks.

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

• Ground Truth for Training Set: Not applicable/mentioned.

Summary of what the document does provide regarding "proof" of meeting criteria:

The "study" presented is a 510(k) Premarket Notification whose primary goal is to demonstrate substantial equivalence to legally marketed predicate devices, not to meet specific quantitative performance acceptance criteria with detailed clinical study data as might be expected today for an AI/ML device.

• Comparison with Predicate Devices: The document argues that MIStar is substantially equivalent to:
  • Efilm Workstation with Modules (K020995)
  • CT Perfusion 2 (K010042)
  • Advantage Windows with Functool Option (K960265)
• Basis for Equivalence:
  • Functional Similarity: MIStar provides digital image processing, measurement capabilities, and DICOM communication, similar to the predicate devices.
  • Algorithm Similarity: It states that "The algorithms used to calculate parametric parameters are similar to CT perfusion2 and Functool."
  • Intended Use: The indications for use are aligned with the capabilities of the predicate devices for image manipulation, visualization, processing, and calculation of perfusion-related parameters.
  • Safety: It asserts that the use of MIStar "does not result in any additional potential hazards when compared to predicate devices."
• Software Development and Testing: The document generally states that the "software package was designed, developed, verified and validated according to the procedures described in the Software Information section: Software Development Process." However, these are general assertions of good practice rather than a description of a specific performance study.

In essence, the "proof" described is the regulatory argument for substantial equivalence based on functional and algorithmic similarity to existing cleared devices, rather than a detailed performance study against predefined acceptance criteria for the output of its advanced features (like perfusion parameter calculations).

Ask a specific question about this device