Enlitic couples deep learning with vast stores of medical data to advance diagnostics and improve patient outcomes

Data-Driven Medicine

All clinical diagnosis is based on data.  All clinical diagnosis is based on data. Every time a doctor sees a patient, she is solving a complex data problem. Symptoms, patient history, lab test results, medical images, comparison with other patient cases, the list of possible diseases or ailments, the treatment options -- all of these are forms of data that must be remembered, understood, and integrated correctly.

There is a lot of medical data to comprehend.  There are over 12,000 medical diagnoses, each with numerous different treatment options, in the International Statistical Classification of Diseases and Related Health Problems (ICD-10). At the health system level, the volume of digital medical data is growing rapidly: from 500 petabytes in 2012 it is expected to be an astonishing 25,000 petabytes by 2020 (IDC Health Insights Report, 2013). This growth is driven by medical imaging, laboratory testing, electronic health records, and increasingly available genomic data. Unfortunately, much of this data is not being used to improve the diagnoses and treatments of patients.

Past attempts at data driven medicine have been disappointingly limited.  Medical interpretation programs have not lived up to their promise. They have suffered from a myriad of problems, including slow workflows, inadequate accuracy, and lack of scalability to handle vast amounts of data. "Existing tools don't necessarily synthesize available data in a useful way to impact treatment paradigms," notes a doctor at a leading medical center. It is no surprise that the National Institute of Medicine estimates that 12 million Americans suffer from misdiagnoses - resulting in the unnecessary loss of lives and billions of dollars in cost (Improving Improving Diagnosis in Health Care, 2015).

Today we are on the cusp of an exciting revolution of improved healthcare.  Deep learning enables data driven medicine at scale. Listed as one of MIT Tech Review's Top 10 Breakthroughs of 2013, deep learning has completely revolutionized how computers handle massive amounts of big data. Thanks to deep learning, computer performance is as on par with humans at some of the hardest tasks in medicine (e.g., segmenting the human brain) -- and performance continues to improve rapidly.

12,000 +

Possible medical diagnoses

ICD-10 codes published by WHO

12M +

Americans who suffer from misdiagnoses

National Institute of Medicine estimates

Digital medical data growth

Petabytes of data

"Deep learning is an algorithm inspired by how the brain works, and as a result it is an algorithm which has no theoretical limitations. The more data you give it, and the more computation time you give it, the better it becomes."

Jeremy Howard, Enlitic Founder in 2014 TED.com talk.

Deep learning

Deep learning is revolutionizing what is possible for computers to achieve. It has become a breakthrough tool across financial, communication and online consumer industries. For example, deep learning allows Spanish and English speakers to communicate in real time via automated translation, and is defining the future of driving through driverless cars.

With Enlitic, doctors can for the first time use the predictive power of deep learning to directly improve patients' medical outcomes.

How does it work? Before deep learning, engineers had to create a set of rules to manually select a tiny subset of the massive amount of information available. The resulting software was only as powerful, comprehensive, and flexible as this set of rules. In contrast, deep learning automatically examines all of the available information to automatically discover, without human intervention, which parts are informative for the task at hand.

Enlitic is using deep learning to usher in a new era of data-driven medicine. The company's tool aims to augment doctors and make it possible to distill actionable diagnostic insights in real time from millions of prior patient cases and other medical data. Deep learning increases what each individual doctor can achieve, multiplying their effectiveness.

In initial benchmarking test against the publicly-available LIDC dataset, Enlitic technology detected lung cancer nodules in chest CT images 50% more accurately than an expert panel of radiologists

In initial benchmarking tests, Enlitic's deep learning tool regularly detected tiny fractures as small as 0.01% of the total x-ray image

Enlitic's deep learning tool is designed to simultaneously support many diseases

Enlitic's Premier Partner Program helps select medical institutions adopt data-driven medicine to improve patient outcomes.
In collaboration with Enlitic's deep learning experts, Premier Partners will lead the way in ushering in a new era of truly personalized patient care.

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