FIELD

The presently disclosed inventions relate generally to medical imaging techniques such as tomosynthesis, and more specifically to systems and methods for implementing a dynamic self-learning medical image network system. In particular, the presently disclosed inventions relate to interacting with, and observing user behavior pertaining to, one or more medical images at a plurality of nodes, in order to improve performance of the dynamic self-learning medical image network system.

BACKGROUND

Medical imaging systems, e.g., tomosynthesis systems, CT scanning systems, MRI systems, mammography systems, etc., have been used for screening and diagnosing a variety of conditions. Doctors and other medical professionals often rely on medical images to diagnose various health conditions. Accurate readings of the medical images are contingent on the quality and clarity of the image, as well as the knowledge and expertise of the medical professional reviewing the image. Specifically, in order for the medical images to be helpful to medical professionals, they must clearly and accurately portray respective body parts to which the image pertains, such that the medical professional can efficiently make a prognosis with reasonable certainty. Radiologists (or other medical professionals) typically study thousands of such medical images, and are trained to detect, through time and practice, recurring patterns in the medical images that are indicative of abnormalities (or other objects of interest) in human tissue.

However, even with extensive training, the objects of interest to the medical professional can be difficult to identify within the medical image for a number of reasons. For example, the medical image may not provide sufficient clarity, or focus, such that a potential abnormality is overlooked. Or, the abnormality may be too small, or otherwise difficult to ascertain. In another example, the abnormality may not be a well-known abnormality, or one that a newly-trained medical professional has previously encountered. In some cases, human error may result in certain abnormalities being overlooked or misdiagnosed. Furthermore, the experience and knowledge from highly experienced practitioners is not easily transferred to others. As will be appreciated, such errors and omissions may have serious, and sometimes even fatal, consequences for patients. Also, existing medical imaging analysis systems, including in particular Computer Aided Detection (CAD) systems, must be frequently programmed and modeled with new information and/or updated analysis techniques, which is time consuming and resource-intensive. Thus, a medical imaging analysis system that minimizes reviewing errors and is automatically updated to provide the latest information and analysis techniques would be highly desirable.

SUMMARY

In accordance with one aspect of the disclosed inventions, a method is provided for creating and using a dynamic self-learning medical image network system. In an exemplary embodiment, the method includes receiving, from a first node initial user interaction data pertaining to one or more user interactions with the one or more initially obtained medical images; training a deep learning algorithm based at least in part on the initial user interaction data received from the node; and transmitting an instance of the trained deep learning algorithm to the first node and/or to one or more additional nodes, wherein at each respective node to which the instance of the trained deep learning algorithm is transmitted, the trained deep learning algorithm is applied to respective one or more subsequently obtained medical images in order to obtain a result.

By way of non-limiting examples, the initial user interaction data may include at least one annotation on at least one of the one or more initially obtained medical images, a selection of one of more pixels associated with at least one of the one or more initially obtained medical images, an actual or estimated amount of time one or more users spent viewing one or more of the initially obtained medical images, an actual or estimated portion of at least one of the one or more medical images that was focused upon by at least one user, a description of a patient condition, and/or diagnostic findings that may be (without limitation) in a form of a written or a voice dictation report.

By way of non-limiting examples, the instance of the trained deep learning algorithm may be maintained at the first node and/or one or more additional nodes, and/or may run on a server accessed through a network.

By way of non-limiting examples, the result may include recognizing one or more objects in the medical image and/or providing a recommendation pertaining to the medical image.

In an exemplary embodiment, the method further includes receiving, from the first node and/or one or more additional nodes, subsequent user interaction data pertaining to one or more subsequently obtained medical images, wherein the subsequent user interaction data is used to modify the trained deep learning algorithm. By way of non-limiting example, the subsequent user interaction data may be used to modify the trained deep learning algorithm if it is determined that the subsequent user interaction data satisfies a predetermined threshold confidence level indicating that the trained deep learning algorithm should be modified. By way of non-limiting example, modification of the trained deep learning algorithm may include adding one or more layers to and/or changing the internal structure of, the layers in the trained deep learning algorithm.

In accordance with another aspect of the disclosed inventions, a dynamic self-learning medical image network system is provided, the system including a plurality of nodes, and a central brain server, wherein the central brain server is configured to receive initial user interaction data from one or more nodes of the plurality, wherein the initial user interaction data pertains to one or more user interactions with one or more initially obtained medical images, train a deep learning algorithm based at least in part on the initial user interaction data received from the node, and transmit an instance of the trained deep learning algorithm to each node of the plurality, and wherein each node of the plurality is configured to apply the instance of the trained deep learning algorithm to one or more subsequently obtained medical images in order to obtain a result.

In an exemplary embodiment, each node of the plurality is configured to maintain an instance of the trained deep learning algorithm.

By way of non-limiting examples, the initial user interaction data received by the central brain server may include at least one annotation on at least one of the one or more initially obtained medical images, a selection of one of more pixels associated with at least one of the one or more initially obtained medical images, an actual or estimated amount of time one or more users spent viewing one or more of the initially obtained medical images, an actual or estimated portion of at least one of the one or more medical images that was focused upon by at least one user at one of the nodes, a description of a patient condition, and/or diagnostic findings that may be (without limitation) in a form of a written or a voice dictation report.

By way of non-limiting examples, the result may include recognizing one or more objects in the medical image and/or providing a recommendation pertaining to the medical image.

In an exemplary embodiment, the central brain server is configured to receive subsequent user interaction data from one or more nodes of the plurality pertaining to one or more subsequently obtained medical images, and to modify the trained deep learning algorithm if the subsequent user interaction data satisfies a predetermined threshold confidence level indicating that the trained deep learning algorithm should be modified. By way of non-limiting example, central brain server may modify the trained deep learning algorithm by adding one or more layers to the trained deep learning algorithm.

These and other aspects and embodiments of the disclosed inventions are described in more detail below, in conjunction with the accompanying figures.

