Case Studies > Improving Diagnosis Accuracy and Saving Lives

Improving Diagnosis Accuracy and Saving Lives

 Improving Diagnosis Accuracy and Saving Lives - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Machine Learning
Applicable Industries
  • Healthcare & Hospitals
Use Cases
  • Automated Disease Diagnosis
The Customer
The Mount Sinai Hospital
About The Customer
Dr. Partho Sengupta, Director of Cardiac Ultrasound Research and Associate Professor of Medicine in Cardiology at The Mount Sinai Hospital
The Challenge

Dr. Partho Sengupta needed a way to accurately identify disease patterns resulting from echocardiograms in order to improve diagnostics and save more lives. Specifically, he wanted to distinguish between two disparate diseases: cardiomyopathy, which directly impacts the heart muscle and often leads to heart failure, and pericarditis, which acts as if the heart is involved but doesn’t actually affect the heart. While both diseases present with similar heart conditions, the treatments are vastly different. For pericarditis, the treatment may include medication and, rarely, surgery. However, if the diagnosis is cardiomyopathy the patient undergoes medical management (i.e. a pacemaker) or in extreme cases, a heart transplant. Misdiagnosis of these disease conditions can put the patient’s life at risk and be very expensive for the hospital. Dr. Sengupta, therefore, looked to Saffron’s Natural Intelligence Platform to help his team increase the diagnosis accuracy of these medical conditions.

The Solution

Working with Saffron’s Natural Intelligence Platform, Dr. Sengupta initiated a blind study comprising 15 patients with constrictive pericarditis and 15 patients with restrictive cardiomyopathy. When the multi-dimensional echocardiography diagnostic data was ingested into Saffron’s associative memory base, the data consisted of 10,000 attributes per heartbeat per patient. The attributes were collected from 90 metrics in six locations of thecomplex echocardiography dataheart and 20 times within a single heartbeat.

Data Collected
Electronic Medical Record, Medical Diagnostic Instruments, Personal Medical, Health Symptoms
Operational Impact
  • [Data Management - Data Analysis]
    Saffron’s Natural Intelligence Platform was not only superior in computational speed than the traditional tools physicians normally use, but also significantly better in diagnostic accuracy.
Quantitative Benefit
  • Saffron’s Natural Intelligence Platform has a 90% diagnosing accuracy comparing to 56% average accuracy rate for participating physicians.

  • Nearly two million associations were examined in 15 patients with constrictive pericarditis and 15 patients with restrictive cardiomyopathy.

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