EarlySign AI model is more accurate for early diagnosis of non-small cell lung cancer, new research shows


TEL AVIV, Israel, May 26, 2021 / PRNewswire / – EarlySign Medial (earlysign.com), a pioneering company developing AI-based clinical data solutions for the early detection and prevention of high-burden disease, today announced the publication of new research impacting early diagnosis non-small cell lung cancer (NSCLC). In collaboration with researchers from Kaiser Permanente Southern California, the Department of Health Systems Science at the Kaiser Permanente Bernard J. Tyson School of Medicine and the Department of Health Sciences, Brock University, St. Catharines, ON, Canada, the study authors found that EarlySign’s machine learning model was more accurate for the early diagnosis of NSCLC than the standard eligibility criteria for screening or modified PLCOm2012, demonstrating the potential to help prevent lung cancer deaths through early detection.

The retrospective peer-reviewed data study, Machine learning for early identification of lung cancer using routine clinical and laboratory data, was published in the American Thoracic Journal, “American Journal of Respiratory and Critical Care Medicine”.

The rationale for the study is that most lung cancers are diagnosed at an advanced stage while pre-symptomatic identification of high-risk individuals may trigger earlier intervention and improve long-term outcomes. The aim was to develop a model to predict a future diagnosis of lung cancer based on routine clinical and laboratory data, using machine learning.

Study results indicated that based on clinical features and laboratory tests performed 9 to 12 months prior to a clinical diagnosis of cancer, the EarlySign model was able to identify lung cancer with a sensitivity and specificity of 40 , 3% and 95%, respectively, with a positive test result indicating a 13-fold increase in the likelihood of lung cancer. With further validation and improvement, this model has the potential to help prevent lung cancer deaths through earlier diagnosis.

“Lung cancer is the leading cancer killer in men and women in the United States, with more than 150,000 deaths expected each year,” commented Michael K. Gould, MD, MS, professor of health systems science at Kaiser Permanente Bernard J. Tyson School of Medicine. “Identifying those at high risk early has the potential to improve lung cancer survival rates by detecting the disease at a localized stage where it is more likely to be cured. EarlySign can help advance lung cancer identification from nine to twelve months that can lead to earlier diagnosis and treatment, when it matters most. “

“The recent pandemic has caused a significant delay in diagnosis and treatment in all areas, with delays in screening, which means cancers can be more advanced and with more serious consequences,” said Eran Choman, Vice President of Clinical Research at EarlySign “Collaborative efforts with the research team have been extraordinary in revealing how advanced AI predictive modeling can increase the predictive power of a model that could impact significant benefit leading to further early diagnosis and treatment. serious illness. “

“EarlySign is now looking to exploit these results to further establish the value of this model for partnering with providers, payers and life sciences and increasing lung cancer identification and therefore treatment and better outcomes for the patients. Said Ori Geva, co-founder and CEO of EarlySign.

About Medial EarlySign

Medial EarlySign helps healthcare providers keep patients healthier longer through the early detection and prevention of high burden disease. Their software solutions derive actionable and personalized clinical information from health data. EarlySign’s AlgoMarkers and AI-powered solutions can help clients identify and prioritize patients when interventions are more likely to stop or prevent serious complications from the onset of illness. The predictive algorithmic models developed using the machine learning platform and business development approach are supported by peer-reviewed research published by internationally renowned healthcare organizations and hospitals . Founded in 2013, Medial EarlySign is headquartered in Tel Aviv, Israel. For more information, please visit: https://earlysign.com/.

Follow Medial EarlySign on LinkedIn: EarlySign Medial and Twitter: @MedialEarlySign

EarlySign contact:

Ori geva, Co-founder and CEO
[email protected]

Media contact:
Darrell atkin
[email protected]

SOURCE Medial EarlySign


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