See-Mode - Empowering clinicians to predict stroke

AI serving clinicians to predict stroke

Harnessing the power of deep learning and computational modelling, See-Mode enables doctors to obtain critical risk factors of stroke from medical images.

Meet our first product

AVA / Augmented Vascular Analysis

World-first AI software for analysis and reporting of vascular ultrasound

Accurate automated analysis and reporting
Fast, no manual drawings needed
Seamless integration with your workflow
Secure, certified and regulatory approved

Regulatory Approved.

FDA 510(k) cleared and approved by Singapore’s Health Sciences Authority as a Class B medical device. Pending CE approval.

In collaboration with renowned clinicians

Prof. Bruce Campbell

Neurologist and Head of Stroke at Royal Melbourne Hospital

Prof. Vincent Thijs

Neurologist and Head of Stroke at Austin Health, Co-Head of Stroke at Florey

Prof. Leonard Yeo

Interventional Neurologist at National University Hospital, Singapore

Dr. Amine Korchi

Neuroradiologist, Réseau Radiologique Romand SA, Switzerland

Prof. Ruth Lim

Radiologist at Austin Health, Professor at University of Melbourne

Dr Leonard Yeo

Stroke Neurologist
National University Hospital

Dr Amine Korchi

Neuroradiologist

Prof. Vijay Sharma

Stroke Neurologist

Prof. Bruce Campbell

Head of Stoke, RMH

Prof. Leonard Yeo

Interventional Neurologist

Prof. Vincent Thijs

Head of Stroke Florey
Inst. & Austin Health

Backed by leading investors

See-Mode Technologies - Investor Day

Meet The 30 Under 30 Scientists And Founders Revolutionizing Healthcare In Asia

Milad Mohammadzadeh of See-Mode on Empowering Doctors to Predict Strokes and Save Lives

Melbourne's See-Mode has built software that sees a stroke before it strikes

2020 Innovators Under 35: Sadaf Monajemi

See-Mode Technologies - Investor Day

Meet The 30 Under 30 Scientists And Founders Revolutionizing Healthcare In Asia

We’re building the future of stroke care, in partnership with leading healthcare institutions

Under clinical validation

Detecting high-risk plaques in ultrasound and CT

Under clinical validation

Detecting high-risk blood flow patterns in CT and MRI