Machine Learning Digital Brain

Harnessing Information to Create Knowledge and Generate Actionable Intelligence

With the right data and technology, healthcare can rapidly improve. Machine learning can be trained to look at images, identify abnormalities, and find areas that need attention, which improves the accuracy of healthcare practices, including radiology, cardiology, and pathology. This method of data analysis will benefit both the family practitioner and internist while improving efficiency, reliability, and accuracy.

Teaching Machines to See  

Machine vision software uses computational techniques to allow computers to analyze pictures, select features of interest, and use that data to reveal insight.

Models of Health

We live in three dimensions, so should our healthcare data. 3D medical imaging provides context and diagnostic value by giving clinicians more than a stack of images. When combined with additional data, which itself can be pulled from the models, it’s possible to create powerful visualizations, improve diagnostic accuracy, or more effectively educate patients.

Assessing Atrial Fibrillation Treatment Efficacy

Machine vision techniques can provide tremendous insight into how to help cardiac arrhythmia patients. Atrial Fibrillation is the most common arrhythmia in the world and impacts tens of millions of people. To effectively stop atrial fibrillation, it is necessary to isolate electrical signals in the left atrium. Incompletely isolating the signals results in failed treatment.

High contrast medical imagery combined with machine vision and segmentation techniques greatly facilitate the assessment of treatment efficacy. It can lead to the building of patient-specific models to guide further intervention.

Machine Learning Allows the Building of Patient-Specific Models

It can be difficult to apply new therapies because of how labor-intensive it can be to create models outside of the research lab. Machine learning can be leveraged to segment images rapidly and efficiently without losing accuracy.

Medical imaging alongside computer vision, segmentation, and machine learning assessment can give insight to how far along the disease really is. MRI combined with image processing pipelines provide a powerful tool to inform doctors how to treat arrhythmias. Visualizations can help providers plan intervention and then to communicate findings and care plans to the patient. Over time, as more data is acquired, it is possible to even more effectively target therapy and predict outcome.

Partner with DVO Consulting – A Leader in AI

It’s clear that machine learning helps clinical decision making in the healthcare field. Our organization exists to help individuals and organizations and meaning in their data. We build software and services that empower fast decision making and consequential intelligence. We are able to create predictive models, recommender systems, scrapers, sentiment engines, and other tools which leverage both classical machine learning techniques and deep learning.

Contact us to learn more about our software and how DVO Consulting can be your strategic partner for IT consulting in Utah, Washington DC, and cities all across the nation. Our team of data scientists and engineers will work with your organization to locate relevant sources of information that will help you meet your project goals.