Eon is the First Healthtech Company to Use Computational Linguistics to Identify Incidental Pulmonary Nodules from CT, MR, and X-Ray Reports
DENVER, Oct. 27, 2020 /PRNewswire/ -- Eon, a Denver-based healthtech leader, announced that its data science models have been expanded to now also identify incidental pulmonary nodules on Magnetic Resonance (MR) and X-Ray radiology reports. Eon's Essential Patient Management platform is a comprehensive lung cancer screening and incidental pulmonary nodule (IPN) identification management solution. EPM uses Computational Linguistics to identify incidental pulmonary nodules on computed tomography (CT) reports with 98.95% accuracy and 97% accuracy on MR and X-Ray radiology reports. This monumental update allows facilities to capture approximately 25% more incidental pulmonary nodules and empowers providers to identify lung cancer earlier when treatment is most effective.
"Any imaging that covers a lung field can identify an unexpected pulmonary finding, such as an IPN. Hundreds of thousands of IPNs each year are identified on CT and MR exams, often of anatomy other than the chest. Suspicious or concerning areas of abnormal density on radiographs are also common. Unfortunately, these nodules and abnormal regions are frequently lost to follow-up or inappropriately followed," said Dr. Erika Schneider, Chief Science Officer at Eon. "Our goal is to create technology that identifies disease before symptoms present, at its earliest and most treatable stages. By expanding our linguistics model, we now offer the most sophisticated solution on the market for early detection of lung cancer."
Eon uses Computational Linguistics, a data science discipline that interprets text similar to how the human brain does, to engineer the most advanced models on the market today. This approach allows providers to positively identify and track incidental pulmonary nodules with more accuracy than other forms of artificial intelligence like Natural Language Processing (NLP) and Computer Aided Detection (CAD). The technology is developed by a team of physicians and data scientists to provide incidental patient identification and management solutions with embedded evidence that decrease administrative burden and improve patient adherence to follow-up exams.
With Eon's proprietary Computational Linguistics data science model, EPM also extracts clinically relevant findings from radiology reports. The IPN model documents nodule location and characteristics like density, shape, edge, and calcification and automatically populates the information into the EPM dashboard. This allows evidence-based Fleischner Society guidelines to be automatically applied to create an actionable worklist. This approach helps providers by removing excess noise (false positives, low-risk nodules), ensures appropriate patient tracking, and automates complex follow-up.
Schneider adds, "Computational Linguistics is the gold standard for language understanding, in particular for lung nodule identification and characteristics extraction. By embedding evidence, the nodule characteristics focus providers' attention on patients with a high probability of having lung cancer. The high accuracy and reproducibility of our model reduces false positives and does not require radiologists to use a structured report. This approach, along with the embedded risk prediction and automation, should enable providers to prioritize patients and improve their outcomes."
Eon continues to raise the bar on incidental disease identification and management solutions. The company recently launched a AAA solution, an Actionable Findings module, and is expanding into Pancreas, Thyroid, and Breast in 2021. The company's dedication and drive are fueled by the positive outcomes of early identification and intervention of catastrophic disease.
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