Preventing cancer starts with the science of risk

We’ve built the world’s most powerful consumer-facing meta-model for cancer risk, capable of calculating any individual's lifetime risk for cancer and generating an optimal action plan to reduce it.

Our science-led approach.

1.
Artificial Intelligence
+
2.
Computational
Biostatistics
+
3.
Behavioral
Psychology
+
4.
Precision
Screening
01. Artificial intelligence

AI to identify thousands of promising risk factors

We use AI to ingest an exhaustive feed of clinical research occurring across the world iteratively and in real time, identifying and organizing hundreds of promising cancer risk factors amongst the thousands under study. Identified risk factors, in turn, form the raw material to build a comprehensive cancer risk model.

+ 6%

Red meat intake

+ 13%

Lack of vegetables

+ 22%

Soy intake

+ 2%

Freckle density

02 computationAL biostatisticS

Computational biostatistics to model your personal cancer risk

Using large-scale computation biostatistics, we run every risk factor identified through the Catch AI against the world’s largest patient data sets to generate a comprehensive risk model isolating the top risk factors for every major cancer type and cancer as a whole. Next, we use your personal risk assessment to translate this model into both a risk score and risk map specific to your unique lifestyle and genetic risks.

03 Behavioral Psychology

Behavioral psychology to target maximum risk reduction

Up to 90% of cancer risk stems from lifestyle factors, yet making the changes needed to reduce your risk requires knowing which factors matter most, which ones are easiest to change, and how to change them. We’ve built the Catch Action Plan to solve each of these problems using cutting edge research on the science of behavior change.

Action Plan recommendations ranked based on impact and ease

Select up to 5 interventions at a time to increase focus and likelihood of success

See how your actions impact your risk scores in real-time

Build on your success and select new interventions as you progress

04 Precision screening

A precision cancer screening protocol based on your actual risks, not just your age

Cancer screening guidelines provided at most primary care practices today are based on just two risk factors: age and smoking history. Not surprisingly, this results in imperfect screening rates for any given individual - and many cancers being detected too late.

The Catch meta-model for cancer risk makes a new approach possible. By drawing on your personal by-cancer Risk Scores and full Risk Map, each of the tests recommended in your Annual Screening Protocol are tailored to your unique risk level for a specific cancer, helping you get screened for the right cancers at the right time for you.

Lung Cancer

Low Risk
No screening required
Medium Risk
Annual low dose CT scan
High Risk
Full-body MRI

Breast Cancer

Low Risk
Clinical breast exam
Medium Risk
3D mammogram
High Risk
3D mammogram and Breast MRI

Colorectal Cancer

Low Risk
FIT at-home stool test
Medium Risk
Stool DNA at-home test
High Risk
Colonoscopy

Skin Cancer

Low Risk
App-assisted self skin exam
Medium Risk
Annual clinical skin exam with dermatologist
High Risk
Annual clinical skin exam with dermatologist

“By drawing from multiple scientific disciplines, we’ve built three paradigm-changing applications in one: an AI learning machine for cancer risk, a powerful and comprehensive risk model, and most importantly, an action plan that translates the outputs into real world cancer risk reduction.”

How Catch brings the science of cancer prevention to you.

Universe of possible cancer risk factors
1.
Artificial Intelligence
Identify the most promising risk factors to test
2.
Computational Biostatistics
Calculate risk coefficients for every factor, generating a comprehensive meta-model
3. Behavioral Psychology
Translate risk factors into actionable lifestyle changes
4. Precision Screening
Translate risk cores by cancer into 1-to-1 cancer screening protocols.

Scientific Advisory Board

Neel Butala

Chief Science Officer

Neel Butala

Chief Science Officer

Neel Butala

Chief Science Officer

Neel Butala

Chief Science Officer