Abstract: There is provided a system for computing a single-label neural network for detection of an indication of an acute medical condition, comprising: hardware processor(s) executing a code for: providing a multi-label training dataset including anatomical images each associated with a label indicative of visual finding type(s), or indicative of no visual finding types, training a multi-label neural network for detection of the visual finding types(s) in a target anatomical image according to the multi-label training dataset, creating a single-label training dataset including anatomical images each associated with a label indicative of the selected single visual finding type, or indicative of an absence of the single visual finding type, and training a single-label neural network for detection of the single visual finding type, by setting the trained multi-label neural network as an initial baseline of the single-label neural network, and fine-tuning and/or re-training the baseline according to the single-label tra

Abstract: There is provided a method for predicting risk of osteoporotic fracture, comprising: receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, the CT scan being performed with settings selected for imaging of non-osteoporosis related pathology; processing the imaging data to identify the bone portion; automatically extracting features based on the imaging data denoting the identified bone portion; computing an osteoporotic fracture predictive factor indicative of the risk of developing at least one osteoporotic fracture in the patient, or the risk of the patient having at least one severe osteoporotic fracture, based on the extracted features, the predictive factor calculated by applying a trained osteoporotic fracture classifier to the extracted features, the osteoporotic fracture classifier trained from data from a plurality of CT scans performed with settings selected for imaging non-osteoporosis related pathology; and providing the predictive fact

Abstract: Computerized methods and systems for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data by receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, segmenting the bone portion from the imaging data , computing at least one grade based on pixel associated values from the bone portion, and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan. The grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.

Abstract: Computerized methods and systems for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data by receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, segmenting the bone portion from the imaging data , computing at least one grade based on pixel associated values from the bone portion, and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan. The grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.

Abstract: Computerized methods and systems for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data by receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, segmenting the bone portion from the imaging data, computing at least one grade based on pixel associated values from the bone portion, and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan. The grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.

Abstract: Computerized methods and systems for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data by receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, segmenting the bone portion from the imaging data , computing at least one grade based on pixel associated values from the bone portion, and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan. The grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.

Abstract: Computerized methods and systems for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data by receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, segmenting the bone portion from the imaging data, computing at least one grade based on pixel associated values from the bone portion, and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan. The grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.

Abstract: There is provided a computer-implemented method for detecting a fatty liver, comprising: receiving imaging data of a computed tomography (CT) scan performed using a single source CT Scanner with settings selected for imaging of non-fatty-liver pathology, segmenting a region of the liver by creating a binary image by applying binary segmentation to a sub-set of pixels of the imaging data according to a first set-of-rules, and mapping the region of liver of the binary image to the segmented region of the portion of the liver of the imaging data, calculating liver parameter(s) for the segmented region of the liver from Hounsfield unit (HU) value(s), and detecting the presence of a fatty liver by analyzing the calculated liver parameter(s) according to a second set-of-rules.