(22 days)
The ImagenMD™ with ImagenQ™ system is software that allows the user to visualize raw PET and/or PET/CT data, assist in evaluating the quality of PET scans, and perform quantitative measurements of tracer uptake to aid in the interpretation of myocardial perfusion PET images.
ImagenMD™ with ImagenQ™ is a Windows software application which allows physicians and healthcare professionals to inspect, quantitatively and automatically perform calculations on myocardial perfusion PET images. The user can perform quality assessments, automatically and manually select myocardial boundaries and alignment, and visualize the results of quantitative perfusion calculations. The use of this system is limited to qualified, licensed healthcare providers (radiologists, nuclear cardiologists or nuclear medicine physicians) trained in the use of nuclear medicine imaging devices.
This submission, K080770, describes the ImagenMD™ with ImagenQ™ system, a Windows software application designed to inspect, quantitatively and automatically perform calculations on myocardial perfusion PET images. The submission focuses on demonstrating substantial equivalence to predicate devices, rather than presenting a performance study with acceptance criteria.
Therefore, the provided text does not contain information regarding
acceptance criteria, a specific study proving the device meets said criteria, sample sizes for test or training sets, data provenance, number or qualifications of experts, adjudication methods, multi-reader multi-case studies, standalone performance, or details on how ground truth was established for training.
The submission primarily states that the device is "equivalent to the predicates" based on "testing and comparison of technological characteristics and intended uses." This implies that the device's functionality and output are comparable to existing, legally marketed devices, rather than establishing numerical performance metrics against pre-defined acceptance criteria.
Based on the provided text, the following information can be extracted/deduced:
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A table of acceptance criteria and the reported device performance:
- Not provided. The submission asserts substantial equivalence to predicate devices without detailing specific performance metrics or acceptance criteria.
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Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):
- Not provided. The submission does not describe a clinical performance study with a defined test set.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not applicable. No test set or ground truth establishment process is described in the provided document.
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Adjudication method (e.g., 2+1, 3+1, none) for the test set:
- Not applicable. No test set or adjudication method is described.
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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 provided. No MRMC study is mentioned.
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If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Not provided. The device is described as assisting qualified healthcare providers, implying it's not a standalone diagnostic tool, and no standalone performance study is detailed.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Not provided. No information on ground truth is available.
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The sample size for the training set:
- Not provided. No information on a training set is available.
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How the ground truth for the training set was established:
- Not provided. No information on a training set or its ground truth establishment is available.
In summary, this 510(k) submission focuses on demonstrating substantial equivalence by comparing technical characteristics and intended uses to existing predicate devices, rather than presenting a detailed performance study with quantifiable acceptance criteria.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).