Abstract: The present subject matter discloses a system and method for automatically detecting and quantifying a plaque/stenosis in a vascular ultrasound scan data in real time using Deep learning models. The system receives a video data and selects one or more frames/images for further processing to detect and quantify the plaque in the artery. Based on the selected one or more frames, the system detects a region of interest (ROI) and further processes the ROI. The system selects end points of a deposits of the plaque by taking a maximum length of the plaque in the artery/plaque boundary and determines the orientation of the vascular ultrasound scan. Based on the orientation and the selected end points, the system determines a vessel/artery boundary to identify a size of the plaque. Based on the determined vessel boundary and the orientation, the system determines plaque segments and measures parameters of the plaque.

Abstract: This disclosure generally pertains to methods and systems for automatically detecting acquisition errors in a medical image using machine learning. Certain embodiments relate to methods for the development of deep learning algorithms that perform machine recognition of specific features and conditions in imaging and other medical data. Another embodiment provides systems for detecting acquisition errors in an X-ray image, the system comprising a non-transitory computer-readable medium storing a preprocessing quality control module that, when executed by at least one electronic processor, is configured to generate associated classifications identifying characteristics of the medical image.

Abstract: This disclosure generally pertains to methods and systems for automatically detecting acquisition errors in a medical image using machine learning. Certain embodiments relate to methods for the development of deep learning algorithms that perform machine recognition of specific features and conditions in imaging and other medical data. Another embodiment provides systems for detecting acquisition errors in an X-ray image, the system comprising a non-transitory computer-readable medium storing a preprocessing quality control module that, when executed by at least one electronic processor, is configured to generate associated classifications identifying characteristics of the medical image.

Abstract: This disclosure generally pertains to systems and methods for detection of infectious respiratory diseases by implementation of an automated X-rays-based triage approach alongside algorithmic clinical sample pooling for molecular diagnosis. Certain embodiments relate to methods for the development of deep learning algorithms that perform machine recognition of specific features and conditions in chest X-ray imaging data. The chest X-ray imaging data is used to guide the pooling strategy of clinical samples for a molecular test.

Abstract: A method and a system for acquiring a 3D ultrasound image. The method includes receiving a request to capture a plurality of ultrasound image for a medical test corresponding to a medical condition. The method further includes determining a body part corresponding to the medical test. Further, the method includes identifying an imaging site particular to the medical test. Furthermore, the method includes providing a navigational guidance to the user in real time for positioning a handheld ultrasound device. Subsequently, the user is assisted to capture the plurality of ultrasound image of the imaging site in real time using deep learning. Further, the plurality of ultrasound images of the imaging site is captured. Finally, the method includes converting the plurality of ultrasound image to a 3-Dimensional (3D) ultrasound image in real time.

Abstract: A system and method for predicting a lung cancer risk based on a chest X-ray in which a nodule is detected in a chest of a patient based on an analysis of the chest X-ray using an image processing technique. A region of interest associated with the nodule is identified using the image processing technique. The region of interest is further analyzed using deep learning to determine a plurality of characteristics associated with the nodule. The plurality of characteristics comprises a size of the nodule, a calcification in the nodule, a homogeneity of the nodule and a spiculation of the nodule. Further, the plurality of characteristics is compared with a trained data model using deep learning. Based on the comparison, a risk score associated with the nodule is generated. Further, the lung cancer risk is predicted when the risk score exceeds a predefined threshold value.

Abstract: This disclosure generally pertains to systems and methods for detection of infectious respiratory diseases by implementation of an automated X-rays-based triage approach alongside algorithmic clinical sample pooling for molecular diagnosis. Certain embodiments relate to methods for the development of deep learning algorithms that perform machine recognition of specific features and conditions in chest X-ray imaging data. The chest X-ray imaging data is used to guide the pooling strategy of clinical samples for a molecular test.

Abstract: This disclosure generally pertains to methods and systems for processing electronic data obtained from imaging or other diagnostic and evaluative medical procedures. Certain embodiments relate to methods for the development of deep learning algorithms that perform machine recognition of specific features and conditions in imaging and other medical data. Another embodiment provides systems configured to detect and localize medical abnormalities on medical imaging scans by a deep learning algorithm.

Abstract: This disclosure generally pertains to methods and systems for processing electronic data obtained from imaging or other diagnostic and evaluative medical procedures. Certain embodiments relate to methods for the development of deep learning algorithms that perform machine recognition of specific features and conditions in imaging and other medical data. Another embodiment provides systems configured to detect and localize medical abnormalities on medical imaging scans by a deep learning algorithm.

Abstract: This disclosure generally pertains to methods and systems for processing electronic data obtained from imaging or other diagnostic and evaluative medical procedures. Certain embodiments relate to methods for the development of deep learning algorithms that perform machine recognition of specific features and conditions in imaging and other medical data. Another embodiment provides systems configured to detect and localize medical abnormalities on medical imaging scans by a deep learning algorithm.