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Deep learning method to reduce contrast agent usage in MRI

CU20041

This technology is a computational algorithm that can minimize the dose of gadolinium-based contrast agents used in magnetic resonance imaging (MRI) without compromising imaging quality.

Unmet Need: Reduction of contrast agent use in MRI.

Furthermore, the cost of Gd agents is a large contributor to the high cost of MRI.

Deep learning Magnetic resonance imaging

Jia Guo Scott A. Small M.D.

Jerry Kokoshka

Neurology Psychiatry

Columbia University Medical Center (CUMC)

2019-08-08

Ultrasound based, deep-learning algorithm for preterm birth risk prediction

CU22367

This technology is a deep learning algorithm that uses ultrasound imaging data to extract cervix geometry and predict preterm birth (PTB) risk.

The Technology: Deep-learning algorithm to accurately predict preterm birth from transvaginal ultrasound images.

This technology uses machine learning to automatically detect and label cervical physiological features from TVUS images of patients in the 2nd and 3rd trimesters of pregnancy, records PTB markers and other important cervical and lower uterine features, combines additional patient data from electronic medical records with TVUS sonograms, and applies deep learning models to predict PTB risk.

11Algorithm Cervix Data set

8Alicia B. Dagle Helen Feltovich Kristin Myers

Dovina Qu

5Maternal Fetal Medicine Mechanical Engineering Obstetrics & Gynecology

4Columbia University Medical Center (CUMC)Fu Foundation School of Engineering and Applied Science (SEAS)Intermountain Medical Group

2023-02-20

Deep learning algorithm for early diagnosis of Alzheimer's disease

CU19273

This technology is an algorithm that utilizes deep learning tools to identify and diagnose diseases from MRI scans.

Diagnostic modalities are limited and underdeveloped since MRI scans require visual inspection by trained medical professionals.

Automated deep learning-based algorithms show great promise in their application to neuroimaging diagnostics, yet current deep learning MRI technologies are inaccurate in their ability to classify diseases.

6Convolutional neural network Deep learning Longitudinal study

Frank Provenzano Scott A. Small M.D.Xinyang Feng

Jerry Kokoshka

Biomedical Engineering Neurology

Columbia University Medical Center (CUMC)Fu Foundation School of Engineering and Applied Science (SEAS)

2019-03-21

A deep learning substitute for Gadolinium contrast detects focused ultrasound-induced blood-brain barrier (BBB) opening