Elucid Bioimaging Inc.

Matthew Phillips

Andrew J Buckler

Ulf Hedin

Ljubica Matic

Systems and method can be provided to transform input data (e.g., CT imaging data) into structured representations to create interpretable models. Another aspect of the current invention can be generating labels synthetically to apply to real data according to a biologically-based labelling technique to guide the model training with a priori mechanistic knowledge.

Structured representations for interpretable machine learning applications in medical imaging

Provided herein are methods and systems for making patient-specific therapy recommendations of an anti-inflammatory therapy for patients with known or suspected cardiovascular disease, such as atherosclerosis.

Determining likely response to anti-inflammatory and pro-resolving therapies for cardiovascular disease non-invasively

Provided herein are methods and systems for making patient-specific therapy recommendations of an anti-diabetic therapy for patients with known or suspected cardiovascular disease, such as atherosclerosis.

Determining likely response to glycemic treatment for cardiovascular disease non-invasively

Provided herein are methods and systems for making patient-specific therapy recommendations of a combination of any two or more therapies selected from a lipid-lowering therapy, an anti-inflammatory therapy for patients with known or suspected cardiovascular disease, such as atherosclerosis.

Determining likely response to combination therapies for cardiovascular disease non-invasively

Non-invasive determination of likely response to anti-diabetic therapies for cardiovascular disease

Non-invasive determination of likely response to anti-inflammatory therapies for cardiovascular disease

Provided herein are methods and systems for making patient-specific therapy recommendations of a lipid-lowering therapy for patients with known or suspected cardiovascular disease, such as atherosclerosis.

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Boston, MA, United States of America

Matthew Phillips