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Personalizing the future of lung cancer screening

22 Hospitals, 11 Countries
Our Mission

 

Lung cancer is the leading cause of cancer-related deaths worldwide. While smoking is the leading cause of lung cancer, lung cancer rates among nonsmokers are on the rise.

Every person has the right to know their risk of developing cancer — SYBIL is a deep learning model that accurately predicts a patient’s risk of lung cancer up to six years in advance from analyzing a low-dose CT scan, ensuring that lung cancer is detected in its earliest stages.

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22 Hospitals
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11 Countries
120K+ LDCT scans validated

SYBIL has been validated extensively on low-dose CT scans (LDCTs) from patients all over the world. This validation process is key to ensuring that SYBIL maintains high performance and that it can be safely used on real patients. Thanks to the support of Wellcome Trust, we have been able to expand the deployment of SYBIL to include hospitals serving under-resourced regions.

Try SYBIL

Collaborate with us

Hospitals around the world have installed SYBIL with some publishing independent research on SYBIL.

Our Sybil Lung Cancer Consortium aims to study the deployment of Sybil to change screening policies and transform lung health.

We welcome hospitals to join our Hospital Network.

How does SYBIL work?

SYBIL is named after the divine oracles of ancient Greece, who were also known as “sibyls.”

The deep learning model assigns a personalized risk score to the LDCT scan, helping clinicians determine when a patient should return for their next screening, or if the patient can avoid unnecessary screening.

Evaluate SYBIL’s performance.

The team behind SYBIL

Combining leading expertise in AI and clinical practice, researchers at MIT and clinicians at Mass General Brigham joined forces to build SYBIL.

With this breakthrough technology, we hope to improve early detection rates of lung cancer while reducing the burden on healthcare systems.

The scale and computational power needed for bold ideas to work is not possible without the support of grants, foundations, and donors. Your support would enable MIT Jameel Clinic to make a better, healthier future for all.

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We're in the process of running Sybil on all of our lung screening patients. We're particularly interested in whether Sybil will flag patients with pulmonary nodules as high risk and if they should they be followed up with more frequently. Mary Pasquinelli, APRN, FNP-BC
University of Illinois
Division of Pulmonary, Critical Care, Sleep, and Allergy at UI Health

Core Collaborators

Peter Mikhael MIT
Jeremy Wohlwend MIT
Headshot of Adam Yala
Adam Yala UC Berkeley
Jameel Clinic blue neural network icon
Ludvig Karstens MIT
Gigin Lin Chang Gung Memorial Hospital
Lecia Sequist Mass General Hospital
Florian Fintelmann Mass General Hospital
Regina Barzilay MIT

Regional Leads for Sybil

Sujoy Kar Regional Lead for India
Nacer Mami Regional Lead MENASA
Arnaldo Marìn Regional Lead, Latin America
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