Abstract: A method for identifying a misidentified study can utilize a set of photographs captured at substantially the same time as a corresponding set of medical images. The method can include determining similarities between the photographs through machine learning models and determining that a misidentified study exists when the similarity between the photographs fails to satisfy a threshold similarity.

Abstract: Methods for identifying a disease state in a patient and/or for treating a patient having the identified disease state are disclosed and can be based on characteristics identified through machine learning models such as deep learning convolutional neural networks and that are associated with video recordings, audio recordings, infrared images, photographs, and/or radiologic patient images.

Abstract: Methods for co-registering images of different imaging modalities include obtaining a photograph and a medical image, maximizing mutual information between the photograph and the medical image, automatically locating features common to both the photograph and the medical image based on maximized mutual information between the photograph and the medical image, and automatically highlighting the located features within the photograph or medical image.

Abstract: Methods for identifying a disease state in a patient and/or for treating a patient having the identified disease state are disclosed and can be based on characteristics identified through machine learning models such as deep learning convolutional neural networks and that are associated with video recordings, audio recordings, infrared images, photographs, and/or radiologic patient images.

Abstract: A method for identifying a misidentified study can utilize a set of photographs captured at substantially the same time as a corresponding set of medical images. The method can include determining similarities between the photographs through machine learning models and determining that a misidentified study exists when the similarity between the photographs fails to satisfy a threshold similarity.

Abstract: Patient misidentification errors in medical imaging can result in serious consequences, such as the misdiagnosis of a disease state or the application of an inappropriate treatment regimen. Systems, methods, and apparatuses disclosed herein can properly and consistently identify, adjust, and/or correlate radiologic images with the correct patient. Systems and methods for automated and robust image capture are also provided. Methods for identifying a disease state in a patient and/or for treating a patient having the identified disease state are disclosed and can be based on characteristics identified through deep learning convolutional neural networks and that are associated with photographic and radiologic patient images.

Abstract: Patient misidentification errors in medical imaging can result in serious consequences, such as the misdiagnosis of a disease state or the application of an inappropriate treatment regimen. Systems, methods, and apparatuses disclosed herein can properly and consistently identify, adjust, and/or correlate radiologic images with the correct patient. Systems and methods for automated and robust image capture are also provided. Methods for identifying a disease state in a patient and/or for treating a patient having the identified disease state are disclosed and can be based on characteristics identified through deep learning convolutional neural networks and that are associated with photographic and radiologic patient images.

Abstract: Systems and methods for identifying a transmission channel response and a feedback channel response from a plurality of composite system responses are disclosed. A plurality of shifted feedback signals are created by shifting a feedback signal frequency by a plurality of first offset values and/or by shifting a transmission signal frequency by a plurality of second offset values. The feedback signals are compared to an input signal to identify the transmission channel response and/or a feedback channel response. A control signal is generated for a pre-distortion circuit to modify the input signal by an inverse of the transmission channel response. The composite system response is measured at a plurality of operating frequencies and at the plurality of offset values. The measurements are stored in a matrix and singular value decomposition is applied to the matrix of measurements to calculate the transmission channel response and feedback channel response.

Abstract: Systems and methods for identifying a transmission channel response and a feedback channel response from a plurality of composite system responses are disclosed. A plurality of shifted feedback signals are created by shifting a feedback signal frequency by a plurality of first offset values and/or by shifting a transmission signal frequency by a plurality of second offset values. The feedback signals are compared to an input signal to identify the transmission channel response and/or a feedback channel response. A control signal is generated for a pre-distortion circuit to modify the input signal by an inverse of the transmission channel response. The composite system response is measured at a plurality of operating frequencies and at the plurality of offset values. The measurements are stored in a matrix and singular value decomposition is applied to the matrix of measurements to calculate the transmission channel response and feedback channel response.