Systems and methods for detecting anomalies using image based modeling

A system for detecting anomalies in a medical imaging system, the system comprising:

• an image recognition model based on training images converted from training data produced by the system, wherein the training data represents events occurring in the system, and wherein the events include at least one of expected normal events and non-normal events;
• a data aggregator that collects and aggregates new data from the system, wherein the new data also reflect the events occurring in the system;
• an image generator that converts the new data into input images;
• a computing system that analyzes the input images using the image recognition model to determine which of the events for the system are represented in the input images;
• wherein the anomalies are detected when the input images are determined to at least one of represents one of the non-normal events and fails to represent one of the expected normal events.

A method for detecting anomalies in a medical imaging system, the method comprising:

• collecting training data from the medical imaging system;
• converting the training data into training images using an image generator;
• designating each of the training images as corresponding to events for the medical imaging system, wherein the events are at least one of an expected normal event and a non-normal event;
• generating an image recognition model via machine learning based on the training images and the designations thereof;
• collecting new data from the medical imaging system, wherein the new data includes environmental data for the medical imaging system and logs for applications running on the medical imaging system;
• converting the new data into input images;
• identifying a pattern in the new data and modifying the input images corresponding thereto and demarcating the identification of the pattern therein;
• and
• analyzing the input images using the image recognition model to determine which of the events for the system are represented in the input images;
• wherein the anomalies are detected when the input images are determined to at least one of represent a non-normal event and fail to represent an expected normal event.