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MIM 4.1 (SEASTAR) software is intended for trained medical professionals including, but not limited to, radiologists, oncologists, physicians, medical technologists, dosimetrists and physicists.

MIM 4.1 (SEASTAR) is a medical image and information management system that is intended to receive, transmit, store, retrieve, display, print and process digital medical images, as well as create, display and print reports from those images. The medical modalities of these medical imaging systems include, but are not limited to, CT, MRI, CR, DX, MG, US, SPECT, PET and XA as supported by ACR/NEMA DICOM 3.0.

MIM 4.1 (SEASTAR) software is used by trained medical professionals as a tool to aid in evaluation and information management of digital medical images. The medical image modalities include, but are not limited to, CT. MRI. CR. DX, MG, US, SPECT, PET and XA as supported by ACR/NEMA DICOM 3.0. MIM 4.1 (SEASTAR) assists in the following indications:

• . Receive, transmit, store, retrieve, display, print, and process medical images and DICOM objects.
• . Create, display and print reports from medical images.
• Registration, fusion display, and review of medical images for diagnosis, . treatment evaluation, and treatment planning.
• . Evaluation of cardiac left ventricular function and perfusion, including left ventricular end-diastolic volume, end-systolic volume, and ejection fraction.
• Localization and definition of objects such as tumors and normal tissues in . medical images.
• . Creation, transformation, and modification of contours for applications including, but not limited to, quantitative analysis, aiding adaptive therapy, transferring contours to radiation therapy treatment planning systems, and archiving contours for patient follow-up and management.
• Quantitative and statistical analysis of PET/SPECT brain scans by comparing to other registered PET/SPECT brain scans

MIM 4.1 (SEASTAR) is a software package designed for use in diagnostic imaging. It is a stand-alone software package which operates on Windows 2000/XP. Its intended function and use is to provide medical professionals with the means to display, register and fuse medical images from multiple modalities including DICOM PET, ECAT PET, SPECT, CT and MRI. Additionally, it evaluates cardiac left ventricular function and perfusion including left ventricular end-diastolic volume, end-systolic volume, ejection fraction, volumetric curve, Region of Interest (ROI) contouring, and quantitative/statistical analysis of PET/SPECT brain scans through nonlinear registration to template space.

MIM 4.1 (SEASTAR) aids the efficiency of medical professionals in the creation of contours defining, but not limited to, normal and tumor tissues. The software automatically generates contours using a deformable registration technique which registers pre-contoured patients to target patients. Registrations are either between a serial pair of intra-patient volumes or between a pre-existing atlas of contoured patients and a patient volume. This process facilitates contour creation or re-contouring for adaptive therapy.

MIM 4.1 (SEASTAR) additionally functions as a medical image and information management system intended to receive, transmit, store, retrieve, display, print and process digital medical images, as well as create, display and print reports from those images. The system has the ability to send data to DICOM-ready devices for image storage, retrieval and transmission.

The provided document, K071964, describes the MIM 4.1 (SEASTAR) medical imaging software. However, it does not contain specific details regarding acceptance criteria for its performance or a detailed study proving it meets such criteria. The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to predicate devices rather than providing extensive performance study results.

The document states: "MIMvista has conducted performance and functional testing on the MIM 4.1 (SEASTAR) software. In all cases, the software passed its performance requirements and met specifications." This is a high-level statement without specific metrics or details of the study.

Therefore, many of the requested details cannot be extracted from this document.

Here's an attempt to answer the questions based only on the provided text, indicating where information is not available:

Acceptance Criteria and Device Performance Study

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

Note: The document broadly states that the software "passed its performance requirements and met specifications," but it does not define what those requirements or specifications were, nor does it provide any quantitative performance data (e.g., accuracy, precision, speed, etc.) for any specific feature.

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

• Sample Size: Not specified.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

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

Not specified. The document does not mention the use of experts for establishing ground truth in any performance study.

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

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 as performed.
• Effect Size: Not applicable, as no MRMC study or AI assistance comparison is described.

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

The device is software intended for use by "trained medical professionals." While it performs automated tasks (e.g., image registration, contour generation, quantitative analysis), the document describes it as a "tool to aid in evaluation and information management," implying human oversight and interaction. A standalone performance study for "algorithm only" is not explicitly described with specific results or metrics.

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

Not specified.

8. The sample size for the training set

Not specified. The document does not explicitly mention a "training set" in the context of machine learning. It describes a "deformable registration technique which registers pre-contoured patients to target patients" and "between a pre-existing atlas of contoured patients and a patient volume," implying the use of pre-existing data, but details about the size or nature of this data are not provided.

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

Not specified.

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The MIMviewer software program is used for the registration, fusion and display of medical images from multi-modalities, such as SPECT, PET, CT, and MRI. MIMviewer provides tools for image review, manipulation, and analysis that assist physicians both inside and outside the medical environment.

MIMviewer is a software package designed for use in diagnostic imaging which operates on Windows 2000/XP, MacOS X 10.4+ and Linux. MIMviewer is designed to be used as a standalone software package, as a remote viewing client for the MIM (K060816) software package, or as CD-ROM DICOM viewer for referring physicians. MIMviewer provides the physician with the means to display, register and fuse medical images from multiple modalities including PET, SPECT, CT and MRI.

The provided text describes a 510(k) summary for the MIMviewer 1.0 software, a medical imaging software for displaying, registering, and fusing medical images. However, it does not contain specific details about acceptance criteria or a dedicated study proving these criteria were met.

The relevant section on "Performance Data" states: "MIMvista has conducted performance and functional testing on the MIMviewer software. In all cases, the software passed its' performance requirements and met specifications." This is a very general statement and lacks the specificity needed to answer your questions in detail.

Therefore, I cannot populate the table or provide specific answers to most of your questions based solely on the provided text.

Here's a breakdown of what can be inferred or what is explicitly missing:

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

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

• 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 (e.g. radiologist with 10 years of experience)

• Not specified. The document only mentions the software "aids the physician" but doesn't detail how testing was performed involving experts or ground truth establishment.

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

• 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

• Not specified. The document describes MIMviewer as a tool for displaying, registering, and fusing images, not explicitly as an AI-assisted diagnostic tool in the sense of an MRMC study.

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

• Not specified. The document states MIMviewer can be used as a standalone software package, but this refers to its operational mode, not a standalone performance study in the context of an algorithm's diagnostic accuracy.

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

• Not specified.

8. The sample size for the training set

• Not applicable/Not specified. The document describes MIMviewer as a software package for display, registration, and fusion of medical images. It does not indicate that it is an AI/ML device that requires a training set in the typical sense of a diagnostic algorithm.

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

• Not applicable/Not specified. (See point 8).

Summary of what's provided:

• Device Name: MIMviewer 1.0
• Intended Use: Aids physicians in diagnosing patients by displaying, registering, and fusing medical images from multiple modalities (PET, SPECT, CT, MRI).
• Predicate Device: MIM™ 4.0 (NEURO) software (K060816).
• Performance Data Statement: "MIMvista has conducted performance and functional testing on the MIMviewer software. In all cases, the software passed its' performance requirements and met specifications."

In conclusion, the provided text offers a high-level overview of the device and its intended use but lacks the detailed performance study information, acceptance criteria, and methodology that your questions require. The statement regarding performance data is a general assertion rather than a detailed report of a study.

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The MIM software program should be used for the registration, fusion and display of medical images from multi-modalities, such as SPECT, PET, CT, and MRI. MIM assists in definition of structures in medical images including tumors, organs, and cardiac left ventricular cavity. MIM aids in the assessment of PET/SPECT brain scans by providing quantitative and statistical comparisons to other registered PET/SPECT brain scans.

MIM 4.0 (NEURO) is a software package designed for use in diagnostic imaging. It is a standalone software package which operates on Windows 2000/XP. Its intended function and use is to provide the physician with the means to display, register and fuse medical images from multiple modalities including DICOM FET, ECAT PET, SPECT, CT and MRI. Additionally it evaluates cardiac left ventricular function and perfusion including left ventricular end-diastolic volume, end-systolic volume, ejection fraction, volumetric curve, Region of Interest (ROI) contouring and quantitative/statistical analysis of PET/SPECT brain scans through nonlinear registration to template space.

The provided text, Koyo814, is a 510(k) Summary of Safety and Effectiveness for the MIM 4.0 (NEURO) device. It includes general information about the device, its intended use, and its equivalence to predicate devices. However, the document does not contain the detailed performance study information requested in points 1, 2, 3, 4, 5, 6, 7, 8, and 9.

The "Performance Data" section merely states: "MIMvista has conducted performance and functional testing on the MIM 4.0 (NEURO) software. In all cases, the software passed its' performance requirements and met specifications." This is a general statement and does not provide any specific criteria, results, or details about the studies conducted.

Therefore, I cannot provide the requested information from the given text.

To answer your specific points:

1. A table of acceptance criteria and the reported device performance: Not found in the document.
2. Sample sized used for the test set and the data provenance: Not found in the document.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not found in the document.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not found 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: Not found in the document.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The document states MIM 4.0 (NEURO) is a "standalone software package," which implies standalone performance, but no details of such a study are provided.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not found in the document.
8. The sample size for the training set: Not found in the document.
9. How the ground truth for the training set was established: Not found in the document.

The document focuses on substantial equivalence to predicate devices rather than providing detailed performance study data for the new device's specific claims.

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MIM 3.5 (CIRCA) is a software package that provides the physician with the means to display, register and fuse medical images from multiple modalities. Additionally, it evaluates cardiac left ventricular function and perfusion including left ventricular end-diastolic volume, end-systolic volume, and ejection fraction. Finally, the ROI feature reduces the time necessary for the physician to define objects in medical image volumes by providing an initial definition of object contours. The objects include but are not limited to turnors and organs.

The MIM software program should be used for the registration, fusion and display of medical images from multi-modalities, such as, SPECT, PET, CT, and MRI. MIM assists in definition of structures in medical images including turnors, organs, and cardiac left ventricular cavity.

MIM 3.5 (CIRCA) is a software package designed for use in diagnostic imaging. It is a standalone software package which operates on Windows 2000/XP. Its intended function and use is to provide the physician with the means to display, register and fuse medical images from multiple modalities including DICOM PET, ECAT PET, SPECT, CT and MRI. Additionally it evaluates cardiac left ventricular function and perfusion including left ventricular end-diastolic volume, end-systolic volume, ejection fraction, volumetric curve and ROI contouring.

The provided text makes a general statement that "MIMvista has conducted performance and functional testing on the MIM 3.5 (CIRCA) software. In all cases, the software passed its' performance requirements and met specifications." However, it does not provide specific acceptance criteria or details about the study that proves the device meets those criteria.

Therefore, I cannot populate the table or answer most of your questions based on the given document.

Here's a breakdown of what can be inferred or directly stated from the document regarding the study, and where information is missing:

1. Table of Acceptance Criteria and 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: Not specified.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective).

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.
• Qualifications of experts: Not specified.

4. Adjudication method (e.g., 2+1, 3+1, none) 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 specified as having been performed.
• Effect size: Not applicable, as an MRMC study is not mentioned. The device's stated function is to provide tools for display, registration, fusion, and evaluation, and to assist in defining structures, implying a human-in-the-loop context, but no comparative study vs. human readers alone is described.

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

• Standalone study: The document implies standalone software functionality ("standalone software package"), and the general statement "passed its' performance requirements and met specifications" refers to the software itself. However, specific details of a standalone performance study with metrics are not provided.

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

• Type of ground truth: Not specified.

8. The sample size for the training set

• Training set size: Not specified. (It's worth noting that at the time of this 510(k), the term "training set" in the context of machine learning was not as prevalent or explicitly required for device submissions as it is now. This device is described as "Medical Imaging Software" rather than an AI/ML algorithm in the modern sense.)

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

• Ground truth establishment for training set: Not specified.

Summary of available information regarding the study:

• What was done: Performance and functional testing.
• Outcome: The software passed its performance requirements and met specifications.
• Details missing: Specific metrics, study design, sample sizes, ground truth methodology, expert involvement, and comparative studies are all absent from this 510(k) summary. The submission focuses on substantial equivalence to predicate devices, which was primarily based on intended use and technical characteristics, not necessarily detailed clinical performance studies as would be expected for novel AI devices today.

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