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.
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.
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.
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 currently under CE-mark* ispection
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.
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.
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 under ispection to become a CE-Mark medical device according to the European regulation (UE) 2017/745
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 .
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 876145.