Medical Imaging Startup Uses AI to Classify Conditions from Sinus and Brain Scans
Radiologists are tasked with diagnosing some of the most serious medical conditions — but their workloads are becoming increasingly demanding as the volume of imaging studies such as CT and MRI has steadily gone up.
Houston-based InformAI is stepping in to help reduce fatigue and stress for radiologists by building deep learning tools that can help them analyze medical scans faster.
“We wanted to build diagnostic-assist tools for clinicians to speed up information workflow and decision-making at the point of care to benefit patients,” said InformAI CEO Jim Havelka.
InformAI trains its deep learning image classifiers and patient outcome predictors on NVIDIA V100 GPUs through the Microsoft Azure cloud platform and with an onsite NVIDIA DGX Station. The startup worked with data science consulting firm SFL Scientific to develop a convolutional neural network-based deep learning technology stack using top technology resources.
In less than 30 seconds, InformAI’s image classifier scans for 20 sinus conditions and flags which ones might be present in a patient’s 3D CT scan. This AI tool has also formed the basis for other image classification applications that analyze 3D scans of soft tissue — including detecting common brain cancers from MRI scans.
AI Spots Sinus Conditions
Figuring out the structure of an individual’s sinuses is harder than it sounds. Each person’s sinus cavities look different, making it challenging for AI to determine if an infection or abnormal mass is present in the eight major sinus cavities and passageways that connect them.
Doctors perform around 700,000 sinus procedures each year in the United States. Using AI to speed up the diagnostics workflow can save on healthcare costs and shorten the time it takes to begin treatment.
InformAI and its healthcare partners built a training dataset was built consisting of approximately 6 million images from 20,000 patient studies. The scans were labeled by a team of radiologists and medical residents who worked with the company on the project.
Radiologists using the startup’s platform can examine and analyze 3D sinus CT scans while the predictor neural network is running. In under a minute, the AI results pop up for 20 sinus medical conditions, which the doctors can then use to assist in their diagnosis and treatment planning process.
InformAI is deploying the sinus classifier this spring at a hospital and several clinics to test its effectiveness as an assist tool for radiologists and ear, nose and throat physicians. The team is also going through the regulatory process required for the AI to be certified as a direct diagnostic tool.
A Neural Network for Neurological Disorders
In general terms, the sinus classification neural network extracts 3D segments from a CT scan to analyze whether a particular disease or set of diseases is present in those image segments, Havelka said. Since the network was trained on such a large medical dataset, it can be repurposed using transfer learning to solve image classification problems for a broad range of soft tissue medical applications.
The startup is doing just that. Using transfer learning, the team trained a neural network to detect disease from another kind of soft tissue: the brain.
When a tumor or lesion is identified in the brain, “it can be life-and-death for patients,” said Havelka. “Early detection and classification are critical in providing the best treatment options and outcome for patients.”
But different brain tumors and lesions can look alike, and can also resemble other neurological disorders with different treatments. As a result of this classification complexity, a patient’s treatment plan can evolve over time.
When radiologists are unable to make a conclusive diagnosis from a brain MRI scan, physicians turn to invasive brain biopsies to obtain additional information. An AI tool that can assist radiologists in making an earlier and more certain diagnosis could reduce the number of required biopsies.
Using a 3D CNN, InformAI is developing a tool that analyzes brain MRI scans to detect whether a tumor or lesion is present, and can classify an abnormal scan as one of four conditions: glioblastoma, metastatic brain tumor, multiple sclerosis or lymphoma.
The deep learning model for brain cancer detection, which is still under development, was initially trained on around 100,000 image scans from 1,000 patient studies.
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