International Business Machines Corporation

Noel Garry Faux

Jorge Andres Moros Ortiz

Suman Sedai

Christine Schieber

Hidemasa Muta

Roslyn I Hickson

Annalisa Jean Swan

Stefan Renard Maetschke

Benjamin W Goudey

Bowen Fung

Method and apparatus for predicting amyloid beta (A&#x3b2;) and phosphorylated tau (p-tau) biomarker levels in the cerebrospinal fluid (CSF) of patients. Embodiments include determining current values for a plurality of easily-measurable attributes of a first patient. Embodiments include analyzing data associated with a cohort of patients having known measurements of A&#x3b2; and p-tau biomarker levels, including determined values for the plurality of easily-measureable attributes. Embodiments include generating a predicted value for A&#x3b2; and/or p-tau biomarker levels for the first patient. Embodiments include generating a risk of the first patient developing AD at a future time, generating a probability of a patient's predicted rate of decline, and/or generating a probability of a patient's age at the onset of dementia, based on the predicted values for A&#x3b2; and/or p-tau biomarker levels.

Machine learning for amyloid and tau pathology prediction

A method for real-time detection and correction of shadowing in hyperspectral retinal images may include capturing receiving, using a processor, a hyperspectral image of a retina of a patient, detecting, by the processor, a shadow in the hyperspectral image, determining, by the processor that the shadow of the hyperspectral image exceeds a threshold, and in response to determining that the shadow of the hyperspectral image exceeds the threshold, initiating, using the processor, a capture of an additional hyperspectral image of the retina of the patient. Various other methods, systems, and computer-readable media are also disclosed.

Real-time detection and correction of shadowing in hyperspectral retinal images

Aspects of the invention include obtaining data regarding a plurality of devices in a viewing environment and analyzing a content item to be displayed in the viewing environment. Aspects also include identifying an interaction between a scene of the content item and at least one of the plurality of devices based at least in part upon the analyzing and identifying a viewer in the viewing environment and obtaining a user profile for the viewer. Based upon the interaction and the user profile, aspect include activating the at least one of the plurality of devices during playback of the scene. Aspects further include monitoring one or more characteristics of the viewer during playback of the scene and updating the user profile for the viewer based on the one or more characteristics.

Identifying and calibrating internet of things (IOT) devices to enhance content consumption experience

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Victoria, Australia

Noel Garry Faux