Abstract: The present disclosure is directed to the first methods and systems for diagnosing a patient being affected by a brain injury from a blast with greater accuracy that previous methods. The methods and system include an automated, non-invasive method for assessing the variance of a patient's ocular motility relative to a matched cohort of unaffected subjects, and using the variance in one or more ocular motility metrics to calculate a score that indicates a likelihood that the patient suffers from a blast brain injury. The score, referred to as a BIS, discriminates between affected an unaffected subjects with a sensitivity of greater than about 85%, a specificity of greater than 75%. The ROCAUC of the BIS is greater than 0.85.

Abstract: A surgical navigation system may include a processor and a display. The processor may receive a patient image and sensor data captured by a sensor, receive a medical image, generate a hologram of the medical image, perform coregistration between the patient image and the hologram, superimpose the hologram on the patient image, and display the superimposed image. Coregistration may be performed manually via a user interaction, or automatically based on one or more fiducials in the medical image and sensor data related to the fiducials. The system may monitor a change in the environment and update the display correspondingly. For example, the system may monitor a movement of a body of the patient, monitor the size of an organ of the patient as the organ is being under operation, or a movement of the surgical instrument. The sensor may be an augmented reality (AR) sensor in an AR device.

Abstract: Methods and systems for aiding in placement of surgical intervention sites for treating lesions for optimization of surgical outcomes are provided. Also disclosed are computer-aided software packages for identifying the optimal placement of a surgical intervention on a patient in need thereof. The packages, systems, and methods for optimizing placement provided herein help to achieve superior results (e.g. increased drainage), decrease hospitalization time, reduce recurrence of lesions and need for drainage, and improve cognitive outcomes for patients.

Abstract: A surgical navigation system may include a processor and a display. The processor may receive a patient image and sensor data captured by a sensor, receive a medical image, generate a hologram of the medical image, perform coregistration between the patient image and the hologram, superimpose the hologram on the patient image, and display the superimposed image. Coregistration may be performed manually via a user interaction, or automatically based on one or more fiducials in the medical image and sensor data related to the fiducials. The system may monitor a change in the environment and update the display correspondingly. For example, the system may monitor a movement of a body of the patient, monitor the size of an organ of the patient as the organ is being under operation, or a movement of the surgical instrument. The sensor may be an augmented reality (AR) sensor in an AR device.

Abstract: A method for predicting abnormal eye convergence in a human or animal subject may involve tracking eye movement of at least one eye of the subject to generate eye tracking data for the subject and using the eye tracking data to predict whether the subject has abnormal eye convergence. A method for diagnosing a brain injury in a human or animal subject may involve tracking eye movement of at least one eye of the subject to generate eye tracking data for the subject, using the eye tracking data to predict whether the subject has abnormal eye convergence, and predicting whether a brain injury has occurred in the subject, based on the prediction of whether the subject has abnormal eye convergence.

Abstract: A method for predicting abnormal eye convergence in a human or animal subject may involve tracking eye movement of at least one eye of the subject to generate eye tracking data for the subject and using the eye tracking data to predict whether the subject has abnormal eye convergence. A method for diagnosing a brain injury in a human or animal subject may involve tracking eye movement of at least one eye of the subject to generate eye tracking data for the subject, using the eye tracking data to predict whether the subject has abnormal eye convergence, and predicting whether a brain injury has occurred in the subject, based on the prediction of whether the subject has abnormal eye convergence.

Abstract: A surgical navigation system may include a processor and a display. The processor may receive a patient image and sensor data captured by a sensor, receive a medical image, generate a hologram of the medical image, perform coregistration between the patient image and the hologram, superimpose the hologram on the patient image, and display the superimposed image. Coregistration may be performed manually via a user interaction, or automatically based on one or more fiducials in the medical image and sensor data related to the fiducials. The system may monitor a change in the environment and update the display correspondingly. For example, the system may monitor a movement of a body of the patient, monitor the size of an organ of the patient as the organ is being under operation, or a movement of the surgical instrument. The sensor may be an augmented reality (AR) sensor in an AR device.

Abstract: The present disclosure is directed to the first methods and systems for diagnosing a patient being affected by a brain injury from a blast with greater accuracy that previous methods. The methods and system include an automated, non-invasive method for assessing the variance of a patient's ocular motility relative to a matched cohort of unaffected subjects, and using the variance in one or more ocular motility metrics to calculate a score that indicates a likelihood that the patient suffers from a blast brain injury. The score, referred to as a BIS, discriminates between affected an unaffected subjects with a sensitivity of greater than about 85%, a specificity of greater than 75%. The ROCAUC of the BIS is greater than 0.85.

Abstract: To that end, in order to overcome some of the deficiencies presented herein above, an exemplary system, method and computer-accessible medium for determining an attribute(s) of a brain of a patient, can include, for example, receiving information obtained from a computed tomography (“CT”) scan(s) of a portion(s) of the brain, generating a CT image(s) that can be based on the information, and determining the attribute(s) of the brain based on the CT image(s) by segmenting an intracranial space (ICS) in the CT image(s). The attribute(s) can include a presence or absence of Alzheimer's disease, total volume of the ICS, brain, CSF or a lesion or the volumes of ICS, brain, CSF or lesion(s) expressed as a percentage of other volume(s). The aforementioned areas can be segmented using a combination of thresholding, morphological erosions, morphological dilations, manual segmentation or semi-automatic segmentation techniques, all of which can be parallel procedures.

Abstract: To that end, in order to overcome some of the deficiencies presented herein above, an exemplary system, method and computer-accessible medium for determining an attribute(s) of a brain of a patient, can include, for example, receiving information obtained from a computed tomography (“CT”) scan(s) of a portion(s) of the brain, generating a CT image(s) that can be based on the information, and determining the attribute(s) of the brain based on the CT image(s) by segmenting an intracranial space (ICS) in the CT image(s). The attribute(s) can include a presence or absence of Alzheimer's disease, total volume of the ICS, brain, CSF or a lesion or the volumes of ICS, brain, CSF or lesion(s) expressed as a percentage of other volume(s). The aforementioned areas can be segmented using a combination of thresholding, morphological erosions, morphological dilations, manual segmentation or semi-automatic segmentation techniques, all of which can be parallel procedures.

Abstract: A method for predicting abnormal eye convergence in a human or animal subject may involve tracking eye movement of at least one eye of the subject to generate eye tracking data for the subject and using the eye tracking data to predict whether the subject has abnormal eye convergence. A method for diagnosing a brain injury in a human or animal subject may involve tracking eye movement of at least one eye of the subject to generate eye tracking data for the subject, using the eye tracking data to predict whether the subject has abnormal eye convergence, and predicting whether a brain injury has occurred in the subject, based on the prediction of whether the subject has abnormal eye convergence.