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A diagnostic platform powered by advanced deep learning algorithms,

using active reinforcement learning to speed up training

and minimize the knowledge transfer effort

of human doctors.

We are currently active

in ophthalmology, cardio

and radiology.

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PROBLEM

01

NCDs

Non-communicable diseases

(NCDs) are the leading cause of death and disability worldwide (60%).

Among NCDs, cardiovascular, ocular and respiratory diseases account for 60% of deaths and 53% of total healthcare expenditures. 

02

AI

Fully supervised diagnostic AI suffers

from several limitations, such as the need to train on huge amounts of labeled data and its difficulties in managing inputs that are noisy, incomplete or simply different from the original dataset.

03

BARRIER

In a situation of shrinking budgets where 97% of resources are spent on acute treatment and not prevention, the adoption of new technologies is hindered by a cost barrier, both in EU and in LDCs. AI can address this problem by supporting Medical Doctors in early detection of disease.

PRODUCT
ABOUT

SOLUTION

OUR STORY

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OUR VISION

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TECHNOLOGY

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SOLUTION

 

A semi-supervised learning AI platform
able to learn from small labelled datasets
augmenting their dimension,
and predicting possible variations or

noise that could be met in the future,

to make the diagnosis robust in real-world settings.

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Ophthalmology

The software we have clinically tested is capable of

segment OCT retinal scans to highlight diagnostic signs

and retinal layers for Diabetic Macular Edema. 

The software is CE-marked*

and will revolutionize ordinary clinical practice.

Further development will involve other ocular diseases.

Radiology 

The software we have developed is capable of estimating the probability of the presence of abnormalities, such as benign and malignant tumours, in mammography scans.
Detection is performed by analysing the images with our advanced deep learning techniques, developed by active learning and semi-supervised learning features.

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Arrhythmias & Emotions

We developed a software able to estimate the probability of the presence of several heart conditions such as atrial fibrillation, 
atrial flutter, other arrhythmias, hypertension, and abnormal heart rate variability. Detection is performed by analyzing the PPG trace created by a video selfie (rPPG) of the user staring at the smartphone camera. The platform can also detect respiratory rate abnormalities and emotional states to remotely measure the patient's well-being.

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Active Learning

Our platform is capable of selecting, from a clinical dataset, only the most informative samples, in order to present them to human doctors in an iterative fashion and get their feedback.

This way, the knowledge transfer effort of the doctors is minimized, and AI training can go faster, saving up to 90% of time and costs.

* Ophthal is a CE-Mark medical device according to the European regulation (UE) 2017/745, certificate No. ITH24059691, issue date 27/11/2023

HOW IT WORKS

The Active Reinforcement Learning platform detects the most informative samples of a clinical dataset and submits them to Medical Doctors for labelling.

Then, the semi-supervised learning platform propagates labels from small, manually labelled, image datasets to all the unlabelled ones, so that the diagnostic AI can be trained to identify conditions without
the need for costly and time-consuming human labelling .

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FEATURED

Ophthal Scientific Supervisor

Prof. Edoardo Midena, MD, FARVO

Professor of Ophthalmology and Visual Sciences

Department of Neuroscience - Ophthalmology

University of Padova

Padova, Italy

CONTACT

STAY IN TOUCH

Tel: +39 3332255009
via Pietro Blaserna, 40
00146 Rome, Italy

STAY IN TOUCH

Tel: +39 3332255009
 Rome, Italy
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 876145.

Funding
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