Eon uses computational linguistics to identify incidental pulmonary nodules from CT, MR, and X-ray reports

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Eon uses computational linguistics to identify incidental pulmonary nodules from CT, MR, and X-ray reports

Press releases may be edited for formatting or style | October 27, 2020 CT Health IT MRI X-Ray
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.

Eon's Essential Patient Management (EPM) platform empowers facilities to capture approximately 25% more incidental pulmonary nodules and identify lung cancer earlier when treatment is most effective.
Eon's Essential Patient Management (EPM) platform empowers facilities to capture approximately 25% more incidental pulmonary nodules and 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."

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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.

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