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Artificial Intelligence applied to Cancer Detection

Pancreatic cancers

are associated with poor prognosis due to high incidence of locoregional recurrence and metastases.
The overall prognosis associated with pancreatic cancer has not improved over the last 20 years.

Patients with Pancreatic Cancer
have a 5-year relative survival rate of
12.8%1

Pancreatic cancer is one of the few cancers whose incidence continues to rise with 66,440 new cases in USA in 2024.2 It is estimated that by 2030, it will be the second leading cause of cancer deaths.3 The overall prognosis associated with pancreatic cancer has not improved over the last 20 years.4 Since the early stages of the disease are asymptomatic, most patients receive a diagnosis when it is already in the advanced stage.

National Cancer Institute Statistics
Patients with Lung Cancer have
a 5-year relative survival rate of
26.7%5

Lung cancer is the 1st leading cause of cancer mortality worldwide.2 It is often diagnosed at advanced stages when treatment options are limited.6 The most common types of lung cancer are non-small cell carcinoma (NSCLC) and small cell carcinoma (SCLC). NSCLC is more common and grows slowly, while SCLC is less common but often grows quickly.7

National Cancer Institute Statistics
Percentage of Men diagnosed with
Prostate cancer during their lifetime
12.5%8

A total of 1,414,259 new cases of prostate cancer and 375,304 related deaths were reported in 2020 globally.9 Prostate cancer is the second most commonly diagnosed cancer and the fifth leading cause of cancer death among men worldwide.9
In the past two decades, Age-Standardized Incidence Rates trend increases in 65 countries.10

National Cancer Institute Statistics

Revolutionising Healthcare with AI

SKOVEN is a disruptive MedTech startup specialising in ARTIFICIAL INTELLIGENCE (AI) devoted entirely to the field of aiding the diagnosis and early detection of tumours and other pathologies in order to significantly improve patient survival rates. We have developped cutting-edge AI (Classification, Regression, Segmentation) models and Machine Learning algorithms to address critical challenges across a broad spectrum of diseases, including: different types of cancer, Alzheimer's disease and diabetes.

Our goal is to provide healthcare professionals with tools to outperform their accuracy in disease detection and prediction.

Our Vision

To revolutionize healthcare through the power of AI, creating a future where diseases are detected early, treated effectively, and ultimately prevented.

How is SKOVEN a revolution?

Icon Detection of multiple cancers:

Thanks to our expertise in Artificial Intelligence, we can analyse millions of clinical data sets, MRI images or histopathological samples to detect different types of cancer (lung, colon, pancreas, skin, etc.) at early stages, considerably increasing patients' chances of survival.

Icon Identifying the risk of pancreatic cancer:

The AIs developed by Skoven detect individuals at risk even before the onset of symptoms by analysing biomarkers in urine. This enables early intervention, considerably increasing patients' chances of survival.

Icon Identification of pre-diabetics:

Skoven has a family of algorithms for detecting and classifying individuals at risk of developing diabetes via analysis of critical biomarkers. This enables early intervention, reducing long-term complications.

Icon Monitoring the progression of Alzheimer's disease:

Our AI is able to detect early signs of Alzheimer's disease and track its progression, providing doctors with valuable information to adjust treatments and slow progression.

Our AI Expertise revolves around:

  • AI/ML Icon

    Cutting-edge AI/ML expertise:

    A team of world-class enthusiasts specialising in Artificial Intelligence
  • AI/ML Icon

    Robust data science foundation:

    We leverage massive datasets and advanced analytics to develop highly accurate and reliable AI models
  • AI/ML Icon

    Focus on clinical impact:

    We collaborate closely with clinicians and researchers to improve our solutions
  • AI/ML Icon

    Ethical AI:

    We prioritize data privacy, security, and responsible AI development in all our endeavors
Expertise

Applications of our different AI models in Cancer Detection

We have developed a 'forest' of different Artificial Intelligence models trained for several areas of oncology

Brain
Brain Cancer: A first Artificial Intelligence model helps to analyse MRI images and identify tumours with the utmost precision.
Skin
Skin Cancer: A second AI-based tool analyzes photos of skin lesions to classify malignancies.
Lung
Lung Cancer: A third family of Deep Learning algorithms process H&E stained tissue slides to detect and classify tumours.
Colon
Colorectal Cancer: Another model of Convolutional Neural Network algorithm has been trained to detect colorectal cancer.
Cervical
Cervical Cancer: A fourth family of Deep Learning models is specialised in histological analysis of stained human tissue samples to detect early signs of cervical cancer.
Prostate
Prostate Cancer: We have a Machine Learning model trained for classifying qualitative patient variables in order to diagnose the malignancy of prostate tumours.
Prostate
Prostate Cancer: A specific family of Deep Learning models is responsible for detecting and classifying early signs of prostate cancer on H&E stained tissue slides.
Pancreas
Pancreatic Cancer: A specific family of machine learning algorithms is dedicated to detecting early signs of pancreatic adenocarcinoma in urinary biomarkers.
Breast
Breast Cancer: A final family of machine learning algorithms is in charge of detecting benign or malignant tumours in H&E stained tissue.

Revolutionizing other Disease Detection

We have applied our expertise in several areas of Artificial Intelligence
to detect and monitor other diseases such as Alzheimer's

Alzheimer's disease

MRI image
MRI image segmentation: Our generative AI helps us improve MRI images by skull stripping. Skull stripping is a process used in medical imaging to remove the skull and other non-brain tissue from MRI images. This technique improves the accuracy of the second model and makes it easier to study structural changes or abnormalities, such as tumours, lesions or neurodegenerative diseases.
Alzheimer's disease: After the skull stripping phase, our second CNN-based model goes into action to classify brain MRI images for early detection of Alzheimer's disease progression.

One of a kind


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Meet the Visionaries Behind Our Success

key benefits:

SKOVEN

Exceptional accuracy:

Superior diagnostic accuracy rates ranging from 92 to 100%, surpassing traditional tools.

Reduced healthcare costs:

Early detection enables less invasive and more cost-effective treatments.

Improved Diagnosis:

Optimises the practitioner's time for the benefit of the patient .
Expertise

Future Perspectives

The integration of AI in cancer diagnostics is advancing rapidly, driven by technological breakthroughs. We are at the forefront of this field thanks to our multiple models capable of discovering what is all too often imperceptible to humans. Add to this the shortage of practitioners and the significant increase in the ageing population in OECD countries, and we believe that the need for AI solutions is only set to grow.

Our range of Artificial Intelligence tools can be used to track the evolution of a pathology such as cancer over time. By analysing sequential scans, blood tests or other data, these tools can detect subtle changes in the size, shape or composition of the tumour. In doing so, they help clinicians determine whether a treatment is working or whether adjustments are needed, enabling more adaptive and timely interventions.


Illustration of AI in cancer diagnostics