Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 intra-muscular fat for a more accurate analysis.

Abstract: 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.