Abstract: In some aspects, the disclosure is directed to methods and systems for machine learning-based data extraction using multiple string searching models. String extraction logic may differ depending on the type of document received. For documents identified to contain line item structures, broader searching models are applied to the document to account for the increased variability of data in the document inherent in data organized in line item structures. For documents identifier to contain non-line item structures, stricter searching models are applied to the document to account for predictable data in the document associated with data organized in non-line item structures.

Abstract: In some aspects, the disclosure is directed to methods and systems for machine learning-based data extraction using multiple string searching models. String extraction logic may differ depending on the type of document received. For documents identified to contain line item structures, broader searching models are applied to the document to account for the increased variability of data in the document inherent in data organized in line item structures. For documents identifier to contain non-line item structures, stricter searching models are applied to the document to account for predictable data in the document associated with data organized in non-line item structures.

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 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.