Camerad Technologies

The Uab Research Foundation (the University of Alabama at Birmingham)

Texas Instruments Corporation

Carson Arthur Wick

Srini Tridandapani

Lei Ding

Fernando Alberto Mujica

Roland Sperlich

Milind Anil Borkar

Pamela T Bhatti

A system for determining subject orientation includes a medical imaging sensor configured to capture medical imagery of an imaging subject. The system also includes one or more interoceptive sensors, which are configured to obtain interoceptive sensor data indicative of an orientation associated with the system.

Systems and methods for determining imaging subject orientation

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.

Systems and methods for radiologic and photographic imaging of patients

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.

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.

A method for preventing wrong-patient errors includes receiving a selection of a current imaging subject. The current imaging subject is selected for a current image acquisition session comprising capturing one or more current images of the current imaging subject utilizing at least a first image sensor system of a first imaging modality. The method includes accessing one or more previous images of a previous imaging subject. The one or more previous images depict the previous imaging subject according to at least a second imaging modality that is different from the first imaging modality. The method includes presenting the one or more previous images on a display system and, in response to determining that the previous imaging subject matches the current imaging subject based upon the one or more previous images, performing the current image acquisition session.

Systems and methods for preventing errors in medical imaging

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.

Systems and methods for radiographic and photographic imaging of patients

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.

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Atlanta, GA, United States of America

Carson Arthur Wick