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FDA Posts New Draft Guidance on Computer-Assisted Detection Devices

Healthcare Blogs - Healthcare IT Blogs

It may be helpful to compare these new guidances with the pending MDDS rule, discussed here, in which the proposed rule defines an MDDS as Class I, the class with the lowest FDA scrutiny. Unlike MDDS, in the current case these CADe devices are not newly defined. However the FDA does acknowledge that the terminology may not widely known or used. A CADe system is not in the same class as an MDDS, and therefore is not an MDDS, because of the degree to which it analyzes medical device data.

The Federal Register posting defines CADe’s as “computerized systems that incorporate pattern recognition and data analyses capabilities (i.e. combine values, measurements or features extracted fro the patient radiological data) intended to identify, mark, highlight, or in any other manner direct attention to portions of the an image, or aspects of radiology data, that may reveal abnormalities during interpretation of patient radiology images or patient radiology device data by the intended user (i.e., a physician or other health care professional)”. As with the MDDS rule, it can be helpful to know what is excluded from the category as well as what is included. Here certain types of systems are defined to not be CADe. These include:

  • CADx devices (which) are computerized systems intended to provide information beyond identifying, marking, highlighting, or in any other manner directing attention to portions of an image, or aspects of radiology device data, that may reveal abnormalities during interpretation of patient radiology images or patient radiology device data by the clinician. CADx devices include those devices that are intended to provide an assessment of disease or other conditions in terms of the likelihood of the presence or absence of disease, or are intended to specify disease type (i.e., specific diagnosis or differential diagnosis), severity, stage, or intervention recommended. An example of such a device would be a computer algorithm designed both to identify and prompt lung nodules on CT exams and also to provide a probability score to the clinician for each potential lesion as additional information.