International Business Machines Corporation

Other

Benedikt Werner Graf

Arkadiusz Sitek

Yiting Xie

Amin Katouzian

Marwan Sati

Mark D Bronkalla

Yusuke Takeuchi

Larissa Christina Schudlo

Lilla Boroczky

Ahmed El Harouni

Paul Dufort

Omid Bonakdar Sakhi

Pedro Luis Esquinas Fernandez

Kourosh Jafari-Khouzani

Frederic Commandeur

Arun Krishnan

Aly Mohamed

Vicky Guo

A mechanism is provided in a data processing system for automatic determination of b-value difference from diffusion-weighted (DW) images. The mechanism receives a series of images wherein a first image has a first b-value and a second image has an unknown b-value. The mechanism applies a generative adversarial network (GAN) model to estimate a difference between b-values in the series of images. The mechanism determines a b-value for the second image based on the first b-value and the estimated difference between b-values.

Automatic determination of b-values from diffusion-weighted magnetic resonance images

A method, computer system, and a computer program product for automated report generation is provided. The present invention may include receiving a plurality of patient images. The present invention may include retrieving relevant data based on an analysis of the plurality of patient images. The present invention may include providing the relevant data to a user, wherein the relevant data provided to the user is in accordance with a relevant medical guideline. The present invention may include monitoring data input to generate a tailored medical report.

Automated report generation using artificial intelligence algorithms

An approach for a computer program to receive image data of a subject including at least a portion of a spine of the subject and a chronological age of the subject. The approach includes the computer program pre-processing the image data including at least a portion of a spine. The approach includes determining an apparent age of the spine or a portion of the spine of the subject using a trained artificial intelligence deep learning algorithm.

Estimating spinal age

A computer implemented method, a data processing system and a computer program product to determine a likelihood of pneumothorax of a patient, the method including assessing a digital image of a chest x-ray of the patient, applying a standard detection pipeline to the digital image, applying a confounding factor detector to the digital image, and applying a high-resolution detection pipeline to the digital image.

Pneumothorax detection

A method, system, and computer program product provide disease simulation in synthetic projection imagery. The method obtains first medical imaging data of a first imaging type as source imaging data. A second imaging type to be generated from the source imaging data is identified. The method identifies a parameter set for the second imaging type. Second medical imaging data is modeled from the first medical imaging data based on the parameter set. A set of synthetic images is generated from the first medical imaging data based on the modeled second medical imaging data.

Disease simulation in medical images

Techniques for fracture detection are provided. A first image is received to be processed to identify rib fractures. A first set of regions of interest (ROIs) is identified by processing the first image using a first machine learning model, where each ROI in the first set of ROIs corresponds to a first potential fracture. Further, a first ROI of the first set of ROIs is upsampled, and the system attempts to verify the first potential fracture in the first ROI by processing the upsampled first ROI using a second machine learning model.

Automated fracture detection using machine learning models

A computer-implemented method for classifying and presenting a contrast phase (CP) of a contrast enhanced computerized tomography (CECT) scan is provided. The method includes training an artificial intelligence (AI) algorithm utilizing a set of CPs labeled CECT data to associate a set of characteristics of the data with a probability associated with the CP. The method includes receiving a new set of unlabeled CECT data, and applying the AI algorithm to the new unlabeled CECT data to associate a first probability of a first CP and a second probability of a second CP. The method also includes providing a graphical representation including the first probability of the first CP and the second probability of the second CP.

Method of determining contrast phase of a computerized tomography image

Mechanisms are provided for determining a measure of radiodensity of anatomical structures of interest and classifying medical imaging study data structures (studies) with regard to contrast phase. In some embodiments, this classification may be used to select/exclude slices for processing by other downstream computing systems. A subset of slices are selected from the study and, for each slice in the subset, a corresponding body part regression (BPR) score is determined. A linear regression on the BPR scores is performed and a representative slice is selected based on results of the linear regression. The representative slice is segmented and a statistical measure of a radiodensity metric for each segment in the representative slice is determined.

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Charleston, MA, United States of America

Benedikt Werner Graf