Medial EarlySign develops decision-support tools based on machine learning that expose the hidden layer of information in standard medical data. These tools enable personal and outcome-based interpretation of medical data, yielding individualized predictions and treatment options for each patient, as well as early prediction of life-threatening conditions.

Ori Geva

Ofer Ariely

Nir Kalkstein

Medial EarlySign

Medial EarlySign develops machine learning based decision support tools that expose the hidden layer of information in standard medical data. These new insights enable personal and outcome-based interpretation of medical data, yielding individualized predictions and treatment options for each patient, as well as early prediction of life-threatening conditions. Our cutting edge tools are designed to offer healthcare organizations a new way of looking at their data, empowering them with proactive, personalized and predictive care management capabilities.  

Medial EarlySign built and refined a unique algorithmic platform designed to handle large medical data and billions of medical data elements.  Our validated algo-medical platform creates thousands of parameters based on existing medical data, analyzing trends and interrelationships over time. This enables setting personalized risk levels for mass populations in moments, and identifying individuals who may require further intervention.

Our solutions go through rigorous clinical validation.  The company is currently conducting clinical trials in 14 leading research institutions around the world totaling more than 5 million patients.

Horizon Ventures

aMoon Fund

Horizons Ventures

Marius Nacht

Efforts to deploy advanced, proactive approaches leveraging #EarlySign’s AI predictive models can play a significant role in identifying prediabetic patients at a higher risk of progressing to diabetes in the near term, when prevention efforts can have the most effect. Check out this recent blog post from Lucem Health as they provide perspective data around the growing burden of prediabetes and how the benefits of #earlydetection can impact screening resource allocation and potentially reduce the human and financial cost of diabetes.
https://lnkd.in/gdfb5UzB

EarlySign is pleased to announce the US general availability of LungFlag™, an AI based solution that identifies patients who may be or are at risk for certain respiratory illnesses, including cancers of the lung or trachea, by analyzing routine clinical data. Working together with Geisinger, this launch builds upon numerous published research papers which validate LungFlag results in the clinical environment. We are grateful for our ongoing collaboration with Geisinger to build on a shared commitment for innovation geared to improve care.

EarlySign designed the LungFlag to enhance the effectiveness with which health systems can screen for cancer, allowing for the identification and targeting of high-risk patients and enabling the right care to be delivered to those who need it the most.  LungFlag is the latest addition to EarlySign’s expanding platform of validated clinical solutions impacting the early diagnosis of serious diseases.
https://lnkd.in/gG6Etiix

Early detection of serious disease in various sub-populations represents a significant opportunity for pharmaceutical companies to impact personalized, proactive treatments.  We are pleased to continue our ongoing collaboration with Roche as they continue important research to help impact the early detection of lung cancer, especially in younger populations.
#earlydetectionsaveslives 
https://lnkd.in/gPySxr9A

Geisinger and EarlySign are pleased to announce the acceptance of a joint submission by the EASTERN VASCULAR SOCIETY (EVS). Presented at the EVS Annual Meeting in Washington, DC, the submission was entitled: “Development and Validation of a Machine Learning Prediction Model to Improve Abdominal Aortic Aneurysm Screening.”
https://lnkd.in/d2_CdE6W

Despite recommendations by the U.S. Preventive Services Task Force, adoption of screening for abdominal aortic aneurysms (AAA) remains low. This study sought to demonstrate that by utilizing a machine-learning model, a novel algorithm could detect patients who are at high risk for AAA. By selecting individuals at greatest risk for targeted screening, this EarlySign developed algorithm resulted in a more than 150% increase in the detection of AAA, when compared with standard screening guidelines. 

We would like to thank the following individuals for their dedication and contributions to this important research.

Department of Vascular and Endovascular Surgery, Geisinger
James R. Elmore, MD
Evan J. Ryer, MD 
Gregory G. Salzler, MD
Robert W. Abdu, DO

Business Intelligence Advance Analytics - Steele Institute, Geisinger, Danville, PA 
Abdul Tariq 
Jim Urick 
Elliot Mitchell 
Rebecca Maff
Grant DeLong 

STAIR Program, Geisinger, Danville, PA
Stacey L. Shriner

Medial EarlySign, Hod Hasharon, Israel 
Tal Sagiv 
Alon Lanyado 
Eran Choman 

Metric
Company Name	Medial EarlySign
Symbol
MSH ID	COM:EARLYSIGN
Market	PRIVATE
Sector	Healthcare & Health Services
Industry	Healthcare & Health Services
Country	IL
Stage
Employee Count	20
Website	earlysign.com
LinkedIn	medial-earlysign
Founders

Metric
Market Cap
Enterprise Value
Monthly Web Traffic
Web Traffic Growth
Valuation
Raised Capital

Metric
Revenue
Revenue (LTM)
Revenue (NTM)
Gross Profit
EBITDA
Operating Income
Net Income
EPS
Diluted EPS
Revenue 2022
Revenue 2023
Revenue 2024
Revenue 2025
Revenue 2026

Metric
Revenue Growth (LTM)
Revenue Growth (NTM)
Gross Profit Growth (LTM)
EBITDA Growth (LTM)
Net Income Growth (LTM)
14-Day Growth Score
28-Day Growth Score
90-Day Growth Score
365-Day Growth Score

Metric
Gross Profit Margin
Operating Profit Margin
EBITDA Margin
Net Profit Margin
Return on Equity
Return on Assets
Return on Capital Employed

COM:EARLYSIGN

Medial EarlySign

Company Overview

Market Metrics

Financial Performance

Growth Metrics

Profitability Ratios

Valuation Multiples

Operational Metrics

Cash Flow Metrics

Balance Sheet Metrics

Efficiency Metrics

Metric
P/E Ratio
P/B Ratio
Revenue Multiple (LTM)
Revenue Multiple (NTM)
EBITDA Multiple (LTM)
EBITDA Multiple (NTM)
Price to Sales Ratio
EV Multiple

Metric
Days Sales Outstanding
Days Payables Outstanding
Days Inventory Outstanding
Operating Cycle
Cash Conversion Cycle
Asset Turnover

Metric
Operating Cash Flow
Free Cash Flow
Cash Flow to Debt
Operating Cash Flow/Sales
Free Cash Flow Yield

Metric
Cash & Equivalents
Accounts Receivable
Inventory
Goodwill
Debt to Capitalization
Debt to Assets
Current Ratio
Quick Ratio

Metric
Rule of 40 (LTM)
Rule of 40 (NTM)
Stock Comp to Revenue
R&D to Revenue
SG&A to Revenue