The General Hospital Corporation

Empire Technology Development LLC

Synho Do

Hyunkwang Lee

Rajiv Gupta

Thomas J Brady

Jung Hwan Cho

Michael Lev

Ramon Gilberto Gonzalez

Tarik Alkasab

Sehyo Yune

Michael Gee

Florian Fintelmann

Shahein Tajmir

A system for analyzing medical images to detect and classify a medical condition, the system includes an input for receiving a medical image, a convolutional neural network coupled to the input and configured to analyze the medical image to generate a prediction including a probability of the presence of the medical condition in the medical image and an atlas creation module coupled to the convolutional neural network and configured to generate an atlas comprising a set of image features and a set of training images. Each image feature is assigned with at least one training image associated with the medical condition. The system further includes a prediction basis selection module coupled to the convolutional neural network and the atlas and configured to create a prediction basis for the prediction generated by the convolutional neural network.

System and method for analyzing medical images to detect and classify a medical condition using machine-learning and a case pertinent radiology atlas

Supervised and unsupervised learning schemes may be used to automatically label medical images for use in deep learning applications. Large labeled datasets may be generated from a small initial training set using an iterative snowball sampling scheme. A machine learning powered automatic organ classifier for imaging datasets, such as CT datasets, with a deep convolutional neural network (CNN) followed by an organ dose calculation is also provided. This technique can be used for patient-specific organ dose estimation since the locations and sizes of organs for each patient can be calculated independently.

System and method for automated labeling and annotating unstructured medical datasets

Systems and methods for rapid, accurate, fully-automated, brain hemorrhage deep learning (DL) based assessment tools are provided, to assist clinicians in the detection & characterization of hemorrhages or bleeds. Images may be acquired from a subject using an imaging source, and preprocessed to cleanup, reformat, and perform any needed interpolation prior to being analyzed by an artificial intelligence network, such as a convolutional neural network (CNN). The artificial intelligence network identifies and labels regions of interest in the image, such as identifying any hemorrhages or bleeds. An output for a user may also include a confidence value associated with the identification.

Systems and methods for brain hemorrhage classification in medical images using an artificial intelligence network

A system and method for determining patient risk stratification is provided based on body composition derived from computed tomography images using segmentation with machine learning. The system may enable real-time segmentation for facilitating clinical application of body morphological analysis sets. A fully-automated deep learning system may be used for the segmentation of skeletal muscle cross sectional area (CSA). Whole-body volumetric analysis may also be performed. The fully-automated deep segmentation model may be derived from an extended implementation of a Fully Convolutional Network with weight initialization of a pre-trained model, followed by post processing to eliminate intramuscular fat for a more accurate analysis.

Patient risk stratification based on body composition derived from computed tomography images using machine learning

In accordance with some embodiments, systems, methods and media for generating a bone age assessment. In some embodiments, a method comprises: receiving an x-ray image of a subject's left hand and wrist; converting the image to a predetermined size; identifying, without user intervention, a first portion of the image corresponding to the hand and wrist; processing the first portion of the image to increase contrast between bones and non-bones to generate a processed image; causing a trained convolution neural network to determine a bone age based on the processed image; receiving an indication of the bone age; causing the bone age to be presented to a user as the result of a bone age assessment; and causing the bone age and the image to be stored in an electronic medical record associated with the subject.

Systems, methods and media for automatically generating a bone age assessment from a radiograph

A system and method for producing an image of a subject with a tomographic imaging system are provided. A tomographic imaging system is operated to rotate a radiation detector, radiation source, or both through a plurality of angular positions around a subject while acquiring data. As the radiation detector or source is rotated, the radiation detector or source is shifted at each angular position by a different shift value. An image of the subject is reconstructed from the acquired data using a reconstruction technique that incorporates the shifts applied to the detector, source, or both into a system matrix.

System and method for ultra-high resolution tomographic imaging

Technologies are generally described for employing ultra-short pulsed X-rays in X-ray computer tomography. Timing parameters of binary modulation applied to the X-rays at the source may be adjusted based on detector characteristics, industry standards, and/or user input. The timing for minimum X-ray intensity during each pulse may be selected to minimize afterglow effect. The timing for the maximum X-ray intensity may then be determined based on one or more of the minimum X-ray intensity timing, desired X-ray dosage, and/or other similar parameters.

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Lexington, MA, United States of America

Synho Do