Abstract: Systems, methods, and computer programs disclosed herein relate to training and using a machine learning model for classifying individuals suffering from chronic cough at least partially based on information about cough events of the patient.

Abstract: Systems, methods, and computer programs disclosed herein relate to training and using a machine learning model to detect, identify and/or characterize body actions, in particular cough events.

Abstract: Described are systems and methods for assessing a likelihood of chronic thromboembolic pulmonary hypertension (CTEPH) within a subject patient based upon an analysis of characteristics indicative of chronic thromboembolic pulmonary hypertension within an imaging study of the subject patient. The systems and methods can identify characteristics of one or more anatomical structures within a cardiac region of the subject patient indicative of CTEPH, characteristics of a pulmonary vasculature of the subject patient indicative of CTEPH, and characteristics of a chronic abnormality in a lung of the subject patient indicative of CTEPH. The systems and methods can assess these characteristics to compute the likelihood of CTEPH within the subject patient.

Abstract: The present disclosure pertains to the analysis of the cardiac region in CT images. Provided herein are a method, a computer system and a computer program product for the segmentation of the cardiac region in CT images.

Abstract: Described are systems and methods for assessing a likelihood of chronic thromboembolic pulmonary hypertension(CTEPH) Within a subject patient based upon an analysis of characteristics indicative of chronic thromboembolic pulmonary hypertension within an imaging study of the subject patient. The systems and methods can identify characteristics of one or more anatomical structures within a cardiac region of the subject patient indicative of CTEPH, characteristics of a pulmonary vasculature of the subject patient indicative of CTEPH, and characteristics of a chronic abnormality in a lung of the subject patient indicative of CTEPH. The systems and methods can assess these characteristics to compute the likelihood of CTEPH Within the subject patient.

Abstract: A method (100) for recognizing weed in a natural environment may be provided. A digital image of weed in an early development stage is captured (102) among cultivated crop. Areas with a predefined color and texture specification are contoured (104) defining a boundary contour. The weed is displayed (106) together with a frame and a quality indicator. Moreover, the digital image is stored (110) only if a predefined quality criterion is met. Then, unnecessary color information may be reset (112), the digital image is sent (116) for a further examination, and a weed name of the weed of the captured image and a related probability value indicative of a probability of a match between the weed name and the weed of the captured digital image together image is received (118).

Abstract: The present disclosure pertains to the analysis of the cardiac region in CT images. Provided herein are a method, a computer system and a computer program product for the segmentation of the cardiac region in CT images.

Abstract: A method for identifying of a type of weed in a natural environment may be provided. The method comprises receiving a digital image of the weed in an early growth stage together with related metadata, contouring areas of leaves and determining whether the weed is a monocotyledon or a dicotyledon. Furthermore, the method comprises determining a growth stage and determining the type of weed identified by a weed name and a probability of the correctness of the determination using at least one out of a plurality of the received metadata and a plurality of metadata determined during the contouring, during the determining whether the weed is a monocotyledon or a dicotyledon, and during the determination of the growth stage of the weeds as input parameters to a set of classifiers using a storage comprising names of types of weeds together with a plurality of sets of metadata per weed type.

Abstract: A method (100) for recognizing weed in a natural environment may be provided. A digital image of weed in an early development stage is captured (102) among cultivated crop. Areas with a predefined color and texture specification are contoured (104) defining a boundary contour. The weed is displayed (106) together with a frame and a quality indicator. Moreover, the digital image is stored (110) only if a predefined quality criterion is met. Then, unnecessary color information may be reset (112), the digital image is sent (116) for a further examination, and a weed name of the weed of the captured image and a related probability value indicative of a probability of a match between the weed name and the weed of the captured digital image together image is received (118).

Abstract: A method for identifying of a type of weed in a natural environment may be provided. The method comprises receiving a digital image of the weed in an early growth stage together with related metadata, contouring areas of leaves and determining whether the weed is a monocotyledon or a dicotyledon. Furthermore, the method comprises determining a growth stage and determining the type of weed identified by a weed name and a probability of the correctness of the determination using at least one out of a plurality of the received metadata and a plurality of metadata determined during the contouring, during the determining whether the weed is a monocotyledon or a dicotyledon, and during the determination of the growth stage of the weeds as input parameters to a set of classifiers using a storage comprising names of types of weeds together with a plurality of sets of metadata per weed type.