Artificial Intelligence

Ángel Alberich-Bayarri, CEO de QUIBIM

QUIBIM secures €8M for radiology AI platform that detects COVID-19 and other diseases early

  • Allows early detection of COVID-19 by radiologists through imaging biomarkers and is the main AI platform for screening for the virus across Europe

  • Investment will enable QUIBIM to expand range of algorithms and high-value components available through its AI-based radiology platform

Valencia, Spain – July 21, 2020 – Medtech company QUIBIM has closed €8M in new financing in a seed funding round co-led by Amadeus Capital Partners and Adara Ventures, with participation by Apex Ventures, Partech, Crista Galli Ventures and existing shareholders, including Tech Transfer UPV, managed by Clave Capital and Juan Roig.

QUIBIM specialises in radiomics, the extraction of standardised, quantitative information from medical imaging data sets using artificial intelligence (AI). QUIBIM’s medical image postprocessing and extraction of imaging biomarkers enables hospitals and pharmaceutical companies to detect diseases early and systematically.

QUIBIM has already launched more than 20 algorithms for a range of conditions including cancer, Alzheimer’s, osteoarthritis and liver disease. It has recently launched chest X-ray and CT scan products for COVID-19. QUIBIM Precision®, its proprietary platform, extracts and quantifies disease-specific biomarkers from medical images with ultra-high accuracy. Its products are used in over 70 hospitals and 11 clinical trials across the world, with 600,000 analyses and 6.5 million images processed to date.

QUIBIM was founded by Dr. Ángel Alberich-Bayarri and Prof. Luis Marti-Bonmati, two innovators at the forefront of medical imaging. They established the process for the development of biomarkers, adopted in 2013 by the European Society of Radiology (ESR) as the official industry standard. They again set a benchmark when, in March 2020, QUIBIM became the main AI platform for screening for COVID-19 across Europe. Shortly after, the Radiological Society of North America (RSNA), with more than 52,000 members from 153 countries, joined the initiative with the goal of creating a global medical repository of COVID-19 cases, with the European arm running on QUIBIM Precision®.

By incorporating AI technologies, QUIBIM also improves the performance and workflow of radiology departments. The company is launching its qp-Suites product family, designed to support radiologists by centralising the essential tools for clinical diagnosis in one platform, increasing operational efficiency.

Angel Alberich Bayarri, founder and CEO of QUIBIM, said, “QUIBIM is now at a scale-up point ready to grow internationally while maintaining great science at the core of our mission. This latest round of funding will be used to boost the AI platform, our available algorithms and high-value components, to provide a seamless, all-in-one solution supporting healthcare providers.” 

“Our new investors will open opportunities for us in new markets and help us to strengthen our brand internationally. We will be able to promote our new prostate, musculoskeletal and oncology solutions and increase sales globally, by expanding our workforce over the coming year.”

Pierre Socha, Partner, Amadeus Capital Partners, added: “We are excited by the potential of radiomics to improve disease detection and enable precision treatments guided by imaging biomarkers. QUIBIM’s all-in-one platform is the answer that radiologists have been waiting for and we look forward to helping them grow internationally while continuing the fight against COVID-19 and other life-threatening conditions.”

“QUIBIM’s rapid adoption is an indicator of how innovative doctors and researchers are eager to have access to and exploit AI technology and methods that can automatically recognise complex patterns in imaging data, to get quantitative assessments and improve the service they provide”, said Rocio Pillado, partner at Adara Ventures.

Legal advisers on the investment were Garrigues and Araoz&Rueda.

About QUIBIM

QUIBIM is a medtech company specialized in Artificial Intelligence (AI) and image processing technologies applied to the development of imaging biomarkers in the medical imaging field. The company has a proprietary software platform QUIBIM Precision® and AI algorithms for quantitative imaging biomarkers used by hospitals, pharmaceutical companies and R&D centres.

QUIBIM Precision® image analysis platform has received CE Mark certification as a Class IIa Medical Device, including its imaging biomarker analysis algorithms, the zero footprint DICOM viewer and its platform hosting these components and medical imaging data.

About Amadeus Capital Partners

Amadeus Capital Partners is a global technology investor. Since 1997, the firm has raised over $1bn for investment and used it to back over 150 companies. With vast experience and a great network, Amadeus’ team of investors and entrepreneurs share a passion for the transformative power of technology.

Pioneering businesses we’ve backed include cyber security vendor ForeScout (NASDAQ:FSCT); Graphcore, innovators in intelligent microprocessors; IVF genetic testing company, Igenomix, IndiaMART, the B2B online marketplace (NSE: INDIAMART) and speech recognition company VocalIQ (acquired by Apple). Find us at https://amadeuscapital.com and @AmadeusCapital.

About Adara Ventures

Founded in 2005, Adara Ventures is a Venture Capital firm managing over €175 million in capital and dedicated to investments in European Deep Tech companies addressing enterprise (B2B) markets. Adara invests primarily in the early stages of development with a particular focus on Cybersecurity, Horizontal and Vertical Data/AI solutions, Cloud computing, and Enterprise/Industrial software. For more information, visit www.adaravp.com and follow us @Adaraventures.

 About Apex Ventures

APEX Digital Health, the 2nd fund under the APEX umbrella, invests in promising early-stage companies in the healthcare sector. APEX Ventures is a European venture capital firm with headquarters in Vienna and Frankfurt, focusing on deep-tech companies. The team is not only acting as investors but also as company builders with a mission to support the most talented start-up teams in building global market leaders.

About Partech

With a portfolio of almost 180 companies spread across 30 countries in Europe, the US, Africa, and Asia, Partech has been one of the leading international investors helping visionary founders for almost 40 years. The Partech team – made up of both former entrepreneurs and executives from 15 different countries – brings capital, experience, strategic support, and networks to entrepreneurs at every stage of development: seed, venture, and growth. With over €1.5B under management, Partech invests from €200K to €50M in B2B and B2C technologies reshaping industries. Companies backed by Partech have completed more than 21 IPOs and more than 50 strategic M&A transactions valued over $100M.

See Partech’s current portfolio: https://partechpartners.com/companies/

 About Crista Galli Ventures

Crista Galli Ventures is an evergreen healthtech fund, investing at seed and series A. The fund is backed by a Danish Family office and has offices in London and Copenhagen. Led by Dr. Fiona Pathiraja, Crista Galli Ventures is one of the only healthtech funds on the continent to have a senior physician as managing partner. The firm’s portfolio companies are based across Europe and the UK, including deep-tech radiology companies contextflow and SmartReporting.

More information:

communication@quibim.com

Recurso 2

ImagingCovid19AI.eu now an international initiative

These are difficult times, it is clear that this COVID19 pandemic that is assailing the world is going to change our way of life. It is time to be united and to collaborate, where doctors, researchers, mathematicians, physicists and the entire scientific community unites to fight the COVID-19 virus by sharing our knowledge and research.

After opening up free access to our QUIBIM Precision – COVID19 platform and AI algorithms to the scientific community to find new diagnostic tools and ways to understand the mechanisms and aggressiveness of the disease, we co-founded the Imaging COVID-19 AI initiative, a multicenter European project to enhance computed tomography (CT) in the diagnosis of COVID-19 by using artificial intelligence. QUIBIM_AI_COVID19

This collaborative initiative coordinated by the Netherlands Cancer Institute, together with Rovobision, the European Society of Medical Imaging Informatics (EuSoMII) and QUIBIM, has had a great response with the participation of several hospitals, radiology centres and research groups from across the world including Italy, Spain, Netherlands, India, and Korea among others.

Furthermore, last March 30th the Radiological Society of North America (RSNA) announced (press release) its willingness to join this initiative. We are proud to welcome our partnering with this renowned society by joining the Imaging COVID-19 AI initiative to spread it throughout the medical imaging community around the world.

“The organizations expressed the common goal of creating a secure way to share COVID-19 imaging, in order to assess lung involvement more accurately with AI. They will collaborate to enable hospitals to provide imaging data securely and efficiently with researchers, respecting privacy and ethical principles. They will define and publish protocols for selecting and labeling imaging data associated with COVID-19 as a tool for researchers and practitioners. Other interested organizations are invited to join this coalition to share information and facilitate a rapid response to COVID-19.” the Radiological Society of North America declares in the press release issued on March 30th, 2020.

Fighting COVID19 through AI

This initiative for automated diagnosis and quantitative analysis of COVID-19 will create a deep learning model for automated detection and classification of COVID-19 on CT scans. This model will also be used for assessing disease severity in patients by quantification of lung involvement to rapidly develop an artificial intelligence solution.

The number of people affected by COVID-19 is increasing every day with healthcare systems across the world on the verge of collapsing, which is why QUIBIM took part in this initiative to develop a tool to support doctors against this virus. As the initiative states “automated image analysis with artificial intelligence techniques has the potential to optimize the role of CT in the assessment of COVID-19 by allowing accurate and fast diagnosis of infection in a large number of patients. AI has the potential to support clinical decision making and improve workflow efficiency.”

Our role in the initiative

As a company specialized in machine learning and image processing technologies for medical images, QUIBIM provides to the initiative the research platform QUIBIM Precision for development and deployment of the deep learning model. The data will be transferred directly and securely from each participating hospital to the servers of the company. The QUIBIM platform, as well as other software utilities to upload images and clinical information provided, enforces a role-based authentication mechanism which guarantees that Study Data remain protected and only available to authorized users.

In that sense, QUIBIM places at the service of the project its experience on interconnectivity with hospitals and sending images through its tool MIUC (Medical Imaging Universal Connector) following all regulations of GDPR, anonymization and personal data processing.

Visit Imaging COVID-19 AI initiative site – LINK

 

 

Lung texture outcomes in Chest Xray

Imaging, AI and radiomics to understand and fight coronavirus Covid-19

  • There is currently no effective cure for this virus and there is an urgent need to increase global knowledge in its mechanisms of infection, lung parenchyma damage distribution and associated patterns.
  • Artificial Intelligence and radiomics applied to X-Ray and Computed Tomography are useful tools in the detection and follow-up of the disease.

In December 2019 the city of Wuhan (China) became the center of a pneumonia outbreak of an unknown cause with global implications. In early 2020, Chinese scientists isolated a novel coronavirus (CoV), from patients in Wuhan, formerly  known as 2019-nCoV 1 and now renamed as Covid-19 by the World Health Organization (WHO). Patients infected with this strain present a wide range of symptoms 2, most seem to have mild disease, with about 20% appear to progress to severe disease, including pneumonia, respiratory failure and in around 2% of cases death 3. Common signs of infection include respiratory symptoms, shortness of breath and breathing difficulties, fever and cough 4.

Coronaviruses (CoV) are a large family of viruses that cause illness ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS-CoV). This novel coronavirus (nCoV) is a new strain not previously identified in humans. Although this outbreak had its start in China, today there are several countries around the world with identified cases, making it a worldwide public health concern.

Confirmed cases of COVID-19 acute respiratory disease reported by provinces, regions and cities in China, 13 February 2020*

Table 1. Confirmed cases of COVID-19 acute respiratory disease reported by provinces, regions and cities in China, 13 February 2020*

How could AI and imaging biomarkers aid to fight against this emerging zoonotic illness?

There is currently no effective cure for this virus and there is an urgent need to increase global knowledge in its mechanisms of infection, lung parenchyma damage distribution and associated patterns, not only for disease detection or to complement the diagnosis, but also to support the design of a curative therapy. AI and radiomics applied to X-Ray and Computed Tomography are useful tools in the detection and follow-up of the disease. As stated in 5, conspicuous ground grass opacity lesions in the peripheral and posterior lungs on CT images are indicative of Covid-19 pneumonia. Therefore, CT can play an important role in the diagnosis of Covid-19 as an advanced imaging evidence once findings in chest radiographs are indicative of coronavirus. AI algorithms and radiomics features derived from Chest X-rays would be of huge help to undertake massive screening programs that could take place in any country with access to X-ray equipment and aid in the diagnosis of Covid-19 6.

QUIBIM_StructuredReport_Chest-X-Ray-Classifier

FIGURE 1: QUIBIM – Quantitative Structured Report – Chest X-Ray Classifier

In order to speed up the discovery of disease mechanisms, QUIBIM’s Chest X-Ray Classifier (Figure 1) can be used to detect abnormalities and extract textural features of the altered lung parenchima that could be related to specific signatures of the Covid-19 virus. We have combined all our knowledge in AI and radiomics in this novel analysis pipeline specifically designed to extract disease patterns. First, the Chest X-Ray is automatically analyzed using a deep learning classifier to provide an abnormality score between 0 and 1. Any abnormality score above 0.3 is considered as an abnormal case. After this initial analysis, lungs are automatically segmented using a Mask R-CNN like convolutional neural network architecture and finally, a massive extraction of texture features is applied (figure header). This pipeline has been completely automated and will serve to provide additional information to the diagnosis of Covid-19.

QUIBIM is committed to provide access to our existing AI technology to find new diagnostic tools and ways to understand the mechanisms and aggressiveness of the disease, contributing to the efforts to find a cure.  Any clinician can fill this form created by QUIBIM to get free credentials for the use of the AI Chest X-Ray classification analysis technology available in the QUIBIM Precision Cloud platform. This research tool is offered to any doctor worldwide with the need of analyzing Chest X-Rays with suspicion of Covid-19.

References:

  1. https://reference.medscape.com/slideshow/2019-novel-coronavirus-6012559
  2. https://www.ncbi.nlm.nih.gov/pubmed/31978945
  3. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200128-sitrep-8-ncov-cleared.pdf
  4. https://www.who.int/health-topics/coronavirus
  5. https://pubs.rsna.org/doi/10.1148/radiol.2020200274
  6. https://www.auntminnie.com/index.aspx?sec=sup&sub=xra&pag=dis&ItemID=127983

Authors:

 

Rafael López González – R&D Engineer

 

NVIDIA QUIBIM

QUIBIM Brings AI to Radiology Workflows with NVIDIA EGX

Artificial intelligence is becoming a reality in radiology as new AI solutions are moving from research to clinical validation and daily clinical workflow.

QUIBIM (Valencia, Spain) has a proprietary software platform and develops AI algorithms across imaging modalities for quantitative imaging biomarkers used in hospitals, radiology centers and clinical trials focusing on body ( liver, prostate) and musculoskeletal analysis algorithms.

QUIBIM’s solutions have already demonstrated a major impact in partner hospitals and radiology centers with a 70% reduction in reporting time of multiple sclerosis cases of the brain using QUIBIM’s White Matter Lesions algorithm. In addition, a large hospital in Valencia, Spain, has experienced significant cost savings using QUIBIM’s Chest X-ray classifier product.

By being able to seamlessly integrate AI solutions in the radiology workflows, QUIBIM helps healthcare providers stay ahead of increasing amounts of data needed for patient care. For example: with the QUIBIM Precision® data mining tool, it is possible to obtain new disease phenotypes based on non-supervised AI clustering. The combined power of AI and edge computing can retain critical processing tasks on devices at the point of care to help in earlier diagnosis of disease and eliminating manual tasks of the radiologists, thereby enabling them to optimize reporting and interpretation.

QUIBIM Precision® and NVIDIA EGX

Delivering AI at the edge minimizes data privacy concerns and enables real-time AI for clinical decisions. QUIBIM and NVIDIA are bringing AI to the edge of medical imaging, the most important healthcare tool in early detection, with the NVIDIA EGX Intelligent Edge Computing Platform. Having the possibility to containerize algorithms on NGC, which is optimized on EGX systems, QUIBIM is able to expand its reach, which helps in the democratization of AI and the ability to provide access to care using AI even in the remote regions of the world.

By delivering new diagnostic and operational capabilities that enhance patient care, QUIBIM and NVIDIA EGX are ushering in a new generation of smart hospitals and radiology departments.

RSNA 2019

QUIBIM at RSNA 2019
with new features!

For the third consecutive year, and coinciding with our business expansion, currently installed in more than 60 hospitals and used by more than 20 clinical trials worldwide, QUIBIM attends this RSNA 2019 edition with new solutions based on artificial intelligence.

Located at the AI Showcase booth #10418, QUIBIM has set up three demo points where participants can interact and navigate the platform testing their main features:

  • AI app’s for quantitative analysis and workflow optimization
  • Radiomics Data Miner tool.
  • Quantitative and radiological structured reporting.
  • Vendor-agnostic system compatible with all PACS vendors and equipment manufacturers.
  • Head-to toe solution (neurology, chest, body, and musculoskeletal).
  • Advanced visualization tools: Zero-footprint DICOM viewer. 

map-01

Seamless AI for Radiologists

QUIBIM Precision® platform integrates AI algorithms into the radiology department workflow with no clicks, making it an efficient system that provides a complete radiology solution covering the AI and quantitative needs.

In our strategy of providing value-driven solutions, QUIBIM uses AI as a tool for organ segmentation (prostate, liver, vertebraes, fat) using Deep Learning, lesions detection (white matter lesions), and classification (chest X-ray). These AI solutions are seamlessly integrated with PACS and RIS making them a part of daily clinical practice, by activating smart back-end rules engine to schedule post-processing tasks.

Discover how QUIBIM empowers radiologists’ workflow at booth #10418 – AI Showcase in the North Hall Level 2.

RSNA_schedule a meeting

In addition, as an advanced research tool QUIBIM has integrated a prostate nosological imaging module based on a non-supervised AI algorithm using quantitative data obtained from multiparametric magnetic resonance (mp-MR) images. This method could serve as a pipeline for the development of nosologic maps and speed up the case assessment and reporting time. This tool helps radiologists’ daily work leading them focus on small zones with malignant features that would be undetected in most of the cases.

More at RSNA 2019

AI THEATER PRESENTATION

Discover at the AI Theater our presentation AI Integrated in Daily Workflow with QUIBIM Precision: Visualize, Annotate, Quantify, Report and Discover how QUIBIM Precision® is providing a seamless solution for AI in radiology, with a complete integration in clinical routine and a completely automated rules engine to get all results before reporting. Special analysis modules for brain, musculoskeletal, lung and body-oncology applications. Presented by Angel Alberich-Bayarri, PhD, our CEO and Founder.

Monday 12:00-12:20 PM | AI24 | Room: AI Showcase, North Building, Level 2.  ADD TO YOUR CALENDAR

AI WORKSHOP

Also, we have organized a special workshop for those interested in a Head-to-Toe Hands-on with AI and Imaging Biomarkers Integrated in PACS. QUIBIM Precision. We will show how to empower radiologists’ daily practice by offering full control over our AI solutions. We will show how AI solutions are seamlessly integrated with PACS and RIS on a daily practice and how to interpret quantitative imaging and AI results.  Presented by Angel Alberich-Bayarri, PhD | Fabio Garcia-Castro | Mar Roca-Sogorb, PhD

Tuesday 1:00-2:30 PM | HW32 | Room: AI Showcase, North Building, Level 2

Interested?  PLACES ARE LIMITED!

Register now

In order to get the best experience for this workshop, it is highly recommended that attendees bring a laptop with a keyboard and decent-sized screen.

Join us at RSNA 2019!

 

 

eusomii-annual-meeting-2019-senza-indirizzo

QUIBIM HOSTING EUSOMII ANNUAL MEETING 2019

Next 18th and 19th October 2019, Valencia will host the Annual Meeting of the EUSOMII society (AMI2019). The society has planned a two days congress combining educational and scientific sessions in the field of medical imaging informatics.

In this edition, QUIBIM will participate with 2 lectures, 3 oral communications, 7 posters and a QUIBIM Precision exhibit space for all those interested in our artificial intelligence algorithms and imaging biomarkers solutions. If you want to book a demo, do not hesitate and book a timeslot!

Book a DEMO

As a board member of EUSOMII, it is an honor to bring to Valencia, our city, such an interesting program. Holding this meeting in an innovative environment like the Hospital Universitario y Politécnico La Fe de Valencia is a sign of the change that healthcare is experiencing nowadays” explained Ángel Alberich-Bayarri, CEO & Founder of QUIBIM and Chair Industry and Startup Committee of EUSOMII.

Check out our participations at AMI 2019:

FRIDAY, October 18th 2019

  • Keynote Lecture I – Imaging biomarkers and radiomics: source of big data for AI – Dr. Luis Martí-Bonmatí

SATURDAY, October 19th, 2019

  • Didactic Lecture II-III – Imaging Biomarkers – Dr. Angel Alberich-Bayarri
  • Oral Communication – Image analysis using an intercontinental infrastructure for the deployment of
    trustworthy cloud services: the ATMOSPHERE project. Authors: Ignacio Blanquer, Eduardo Camacho-Ramos, Andrey Brito, Ana Jimenez-Pastor, Christof Fetzer, Altigran da Silva, Amanda Calatrava, Fabio García-Castro, Ángel Alberich- Bayarri, Franciso Brasileiro.
  • Oral Communication – Quantification and evaluation of pre-post exercise femoral cartilage thickness and T2
    changes in ultramarathon athletes. Authors: Fabio García-Castro, Jordi Catalá March, Daniel Brotons Cuixat, Miquel Llobet Llambrich, Eduard Sánchez Osorio, Ángel Alberich-Bayarri.
  • Oral Communication – Automated Lung Segmentation in Chest Radiographs using Deeply Supervised Convolutional Neural Networks Trained by means of a Database Augmented with a Generative Adversarial
    Authors: Rafael López.

POSTERS:

  1. Automatic cartilage segmentation in 3D T2w high resolution MR using a Deeply
    Supervised Multi-Planar Convolutional Neural Network. Authors: Ana Jimenez-Pastor, Fabio García-Castro, Ángel Alberich-Bayarri, Luis Marti- Bonmati.
  2. Automatic quantification of white hyperintensities in a healthy aging cohort using
    Convolutional Neural Networks. Authors: Ana Jimenez-Pastor, Eduardo Camacho-Ramos, Ángel Alberich-Bayarri, Carles Biarnes, Josep Garre, Joan Carles Vilanova, Rafel Ramos, Reinald Pamplona, Salvador Pedraza, Josep Puig.
  3. Adaptation of TLAP-certified radiological structured reports to be used in a cloud
    platform environment. Authors: Fernando Bacha-Villamide, Eduardo Camacho-Ramos, Alejandro Mañas-Garcia, Luis Martí-Bonmatí, Angel Alberich-Bayarri.
  4. Development and validation of an inter and intra-sequence registration algorithm in
    multiparametric prostate resonance imaging. Authors: Matías Fernández, Mar Roca-Sogorb, Fabio García-Castro, Raúl Yébana, María Asunción Torregrosa, Leonardo Bittencourt, Margarita García Fontes, Paula Pelechano, Luis Martí- Bonmatí, Ángel Alberich-Bayarri.
  5. Computer aided diagnosis for Rheumatic Heart Disease by AI applied to features
    extraction from echocardiography. Authors: Eduardo Camacho-Ramos, Ana Jimenez-Pastor, Ignacio Blanquer, Fabio García- Castro, Ángel Alberich-Bayarri.
  6. Outcome prediction after acute stroke through functional magnetic resonance imaging. Authors: Eduardo Camacho-Ramos, Ana Jimenez-Pastor, Carles Biarnes, Ángel Alberich-Bayarri, Salvador Pedraza, Josep Puig.
  7. Implementation of an interactive radiological structured report management system
    with AI annotation capabilities. Authors: Alejandro Mañas-Garcia, Eduardo Camacho-Ramos, Ismael Gonzalez, Fernando Bacha, Ángel Alberich-Bayarri, Luis Marti-Bonmati.

Our Quibimers are already heating engines for AMI2019. We look forward to meeting you!

QUIBIM_Curso IA Banner

CURSO PROPIO: Introducción a la Inteligencia Artificial aplicada a la Imagen Médica

Tenemos el placer de anunciar la primera edición de nuestro curso: Introducción a la Inteligencia Artificial aplicada a la Imagen Médica. 

Se trata del primer curso teórico-práctico centrado en dar conocer más a fondo algunos temas de máxima actualidad relacionados con la creación de algoritmos básicos de preproceso de las imágenes médicas y biomarcadores de imagen. Además, se practicará el desarrollo de algoritmos de inteligencia artificial basados en redes neuronales convolucionales aplicados a las imágenes médicas.

Dirigido a estudiantes de grado y máster de ingeniería biomédica, telecomunicaciones, informática, ciencia de datos, matemáticas, así como de otras carreras técnicas. Investigadores interesados en la imagen médica, la inteligencia artificial y los biomarcadores de imagen.

Puedes realizar la inscripción en las siguientes modalidades:

Recurso 4

PROGRAMA – Horario de 17 a 20 hrs.

DÍA 1 – Lunes 18 de noviembre de 2019 (3 horas)

  • Introducción al curso: la imagen médica desde Valencia al mundo.
  • Modalidades de adquisición:
  • No ionizantes (resonancia magnética y ultrasonidos)
  • Ionizantes (rayos X, tomografía computarizada y medicina nuclear) e imagen híbrida.

DÍA 2 – Martes 19 de noviembre de 2019 (3 horas)

  • Estándares en imagen médica: DICOM y otros.
  • Ejercicio-Notebook: Manipulación de formatos de imagen médica.
  • Repaso de conceptos.

DÍA 3 – Miércoles 20 de noviembre de 2019 (3 horas)

  • Procesamiento de imágenes médicas.
  • Biomarcadores de imagen: ¿Qué son?
  • Biomarcadores estructurales
  • Biomarcadores funcionales
  • Ejercicio-Notebook: Análisis de imágenes.

DÍA 4 – Jueves 21 de noviembre de 2019 (3 horas)

  • Introducción al Machine Learning
  • Ejercicio-Notebook: Inteligencia artificial aplicada: Clasificación con Redes Neuronales Convolucionales

FECHA: Del 18 de noviembre al 21 de noviembre de 2019

LUGAR: (COITCV) Col·legi Oficial d’Enginyers de Telecomunicació de la Comunitat Valenciana. Avinguda de Jacinto Benavente, 12. 46005, Valencia, España

¿Te interesa? Puedes realizar la inscripción en el siguiente enlace:

InscribemeMás info: PROGRAMA 

ALZ WORLD DAY

Imaging Biomarkers in Alzheimer

Alzheimer’s disease (AD) is a neurodegenerative disorder considered the most common cause of dementia worldwide. Dementia is a general term for the loss of cognitive functions –memory, thinking or reasoning- and behavioural abilities that affects the daily life of a person. According to the World Alzheimer Report, about 50 million people worldwide suffer from dementia. This number is estimated to be almost doubled every 20 years, reaching 131.5 million patients in 2050. AD is deemed the main form of dementia and contributes to 60-70% of the cases.

In this disorder, the connections between the nerve cells that make up the brain are affected, causing the death of these cells and the loss of brain tissue. In AD, abnormal levels of beta-amyloid and tau proteins are found in the brain. Evidence suggests that a complex interaction between these proteins is the main responsible of Alzheimer’s brain changes. Depending on the brain regions affected, different alterations may be presented in the patient. Some of the first regions to be altered are the entorhinal cortex and hippocampus, which are closely related to memory performance.

AD is a progressive disorder in which more parts of the brain are damaged over time. As this happens, more dementia’s symptoms are developed and the patient’s condition gradually worsens. In the early stages, people often present a reduced ability to take and remember new information; and may be accompanied by word-finding problems, vision or spatial issues, or impairment in reasoning or judgement. As AD progresses, patients suffer an increased memory and cognitive loss. They can experiment difficulties in recognizing family and friends, loss the ability to have conversations or be unable to respond to their environment. In severe cases of AD, brain tissue shrinks significantly, making people unable to communicate and completely dependent on others.

There are no effective treatment options for AD patients able to detain its progression. Nevertheless, symptoms can mitigate by means of medication and a healthy lifestyle. Some risk factors, such as age and genetics, are out of control; while others can be overcome to take care of brain-health. According to the Alzheimer’s Research & Prevention Foundation, regular physical exercise can reduce the risk of developing AD up to 50%. Others such as social engagement, healthy diet, mental work and stimulation, good sleep and stress management has proven to fortify the brain and reduce the risk of suffering any form of dementia.

As shown, prevention is the most effective way to fight this devastating disease. An early diagnosis is fundamental for this purpose. Nowadays, this is performed mainly by means of questions to the patients, blood/urine tests, and memory, attention or problem-solving exercises. Brain scans, such as computed tomography (CT), positron emission tomography (PET), or magnetic resonance imaging (MRI) are also key elements in diagnosis because they allow to detect the presence of abnormal concentrations in proteins and atrophy.

In this way, our team has developed automatic brain tools for the detection and assessment of Alzheimer in a subject. Our Brain Atrophy suite and Voxel-Based Morphometry analysis modules are designed to evaluate the shrinkage of the brain by comparing its volume and morphology with a set of healthy subjects (paired in age and gender).

two examples of Brain Atrophy- Hippocampal Asymmetry analysis reports

Above there are two examples of Brain Atrophy: Hippocampal Asymmetry analysis reports over a healthy and over an Alzheimer’s brain. This analysis provides a view of the brain focused on hippocampus’s status (volume and level of asymmetry) framed in a comparison with a set of healthy people. Taking a look at them, relevant differences in the Alzheimer’s brain come to light: the left hippocampus volume falls below the normal ranges (red region in the table), while the left-right hippocampus asymmetry is also presented out of normal values.

Other important tool to assess brain atrophy is our Voxel-Based Morphometry analysis module, which gives volumes and statistical scores comparing morphology differences between the pathological brain and the previous set of healthy subjects.

QuibimStructuredReport_Voxel-Based Morphometry-01

The analysis above has been performed over the same Alzheimer’s brain than the previous Hippocampal Asymmetry evaluation. Both reports highlight alterations in the hippocampus, which is one of the first affections in AD.

Other tools we have included in the Brain Atrophy suite, such as global brain screening, frontal-temporal dementia and motor cortex evaluation can be also performed to obtain a more enlightening sight of the brain.  The combination of these brain assessment tools with traditional AD evaluation methods can lead to a better understanding and an earlier diagnosis of this terrible pathology.

2019-06-26

QUIBIM to develop platform in leading research project to fight pediatric cancer

QUIBIM is helping to advance knowledge of the most lethal pediatric tumors through EU-funded program PRIMAGE, which exploits precision information from medical imaging to establish tumor prognosis, and expected treatment response using radiomics, imaging biomarkers and artificial intelligence (AI).

Pediatric cancer is a rare disease, but treatment remains challenging. Improving knowledge is key to adequately plan therapy and boost survival, and the latest AI techniques have the potential to harness unprecedented information from medical images.

A few months ago, the European Commission funded the PRIMAGE (1) project with over €10M, to help identify the most efficient treatment and a tumor’s main characteristics without the need for biopsy, by using computational processing of medical images on the cloud.

The PRIMAGE consortium will create a bank of images obtained through AI, using an open cloud-based platform to support decision-making in the clinical management of Neuroblastoma (NB), the most frequent solid cancer of early childhood, and Diffuse Intrinsic Pontine Glioma (DIPG), the leading cause of brain tumor-related death in children. The PRIMAGE platform will implement the latest advancement of in-silico imaging biomarkers and modeling of tumor growth towards a personalized diagnosis, prognosis and therapies follow-up.
The project involves 16 European partners, including internationally recognized institutions, and four leading industrial partners, including Spanish biotechnology company QUIBIM, all working under the aegis of the Imaging Biomedical Research Group (GIBI230) based in La Fe Hospital, Valencia.

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Sharing high-end knowledge of AI tools

QUIBIM is responsible for the central task of developing the PRIMAGE platform’s architecture, adaptation and design. The company recently obtained CE mark for its Chest X-Ray Classification AI-Tool and its imaging biomarker analysis algorithms, zero footprint DICOM viewer and platform within the QUIBIM Precision platform.

QUIBIM researchers are now bringing their expertise in medical image post processing and management to PRIMAGE, by passing on their knowledge of clinical trials design and validation, imaging biomarkers extraction and validation, radiomics, data clustering and visualization, and development of AI-fueled tools, such as organ segmentation models.
“Much work remains to be done to improve our knowledge of pediatric brain cancer. NB and DIPG have a complex therapeutic approach and we need proper tools to improve survival. Extracting quantitative information from medical images with AI can help visualize tumor growth with extreme precision, and help to tailor therapy to each individual patient,” Ángel Alberich-Bayarri  said.

QUIBIM’s input will also help to define the methodologies and standards to be used in the different development areas, to facilitate interoperability between the platform ́s modules and for future interoperability with their cloud-based platforms for functionality add-ons.

Transferrable knowledge to other cancers

Cancer has a very low incidence among children and experts estimate that 500,000 EU citizens will be pediatric cancer survivors by 2020. Nonetheless, cancer remains the first cause of non-traumatic death among children.

Neuroblastoma is the most common extracraneal tumor in children, representing 8-10% of all pediatric cancers. In Europe, 35,000 new cases are diagnosed each year, 1,000 in Spain alone.

Diffuse Intrinsic Pontine Glioma is a very rare disease in childhood and is associated with low survival (10%), despite many existing treatments and on-going research. Treatment is not curative, only palliative, i.e. radiotherapy to improve the patient’s life. 16 new cases are diagnosed each year in Spain, accounting for 2.5% of oncological pediatric patients and 13% of pediatric tumors of the central nervous system.

Because of the peculiarities of computational approximation in these two types of tumors that are proper to childhood, investigation done in that area will also be applicable to other types of tumors. Because it will gather considerable scientific effort, PRIMAGE should also help advance research on other types of cancer.
(1): PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers (PRIMAGE)

MS WORLD DAY_QUIBIM

IMAGING BIOMARKERS IN MULTIPLE SCLEROSIS

Pain, depression, slurred speech and feeling of numbness, tingling, or weakness. These are just some symptoms of Multiple Sclerosis (MS) disease, a long-term condition that affects the brain and spinal cord.

In MS, the immune system confuses myelin with a foreign body and attacks it. The loss of this protective sheath that covers the nerve fibers will disrupt the messages travelling between the brain and the body. These messages may be slowed down, interrupted, or may not occur at all.

Eventually, the person sees affected the ability of controlling their own actions. The signs and symptoms of MS may vary greatly depending on the stage of the disease and the location of the affected nerve fibers. Movement affections, vision problems, altered speech and dizziness are common consequences of this condition.

It is the most widespread neurological disorder of young adults globally. The disease can be developed at any age, but its main incidence appears in the range of 20-50 years old. The National Multiple Sclerosis Society estimates that near 2.3 million people are living with this disease around the world. It also calculates that 1 million of them are placed in the United States, where 200 new cases are diagnosed every week.

Together with blood tests, medical history and neurologic exams, imaging scans have also proven to be a key element for the diagnosis of MS, concretely the Magnetic Resonance Imaging (MRI) is the reference diagnostic technique for the identification of lesions in MS.

Damaged white matter has a prolonged T2 relaxation time due to increased tissue water content and to degradation of the myelin, being well depicted on MRI and concretely on Fluid Attenuated Inversion Recovery (FLAIR) images. In this MR-sequence, MS lesions are seen as white matter hyperintensities (WMH). Nowadays, manual segmentation of WMH areas is still the gold standard to quantify the total lesion volume and to know the number of lesions in the brain. However, this methodology turns MS patient’s diagnosis and follow-up in a cumbersome and time-consuming task with high intra- and inter- observer variabilities.

Zero-click tools based on Artificial Intelligence (AI) and, more concretely, Convolutional Neural Networks (CNN) can be used to automatically segment WMH on FLAIR images in a few minutes. Novel designed architectures are composed of an ensemble of CNNs built on standard convolutional, dilated and residual layers.

Multiple Sclerosis_QUIBIM

These tools are capable of fine segmentation of the lesion avoiding the physiological WMH as the ependymal layer. Physicians can obtain quantitative information that helps them to achieve a more accurate and earlier diagnosis, thus reducing the workload and improving the time-efficiency while enhancing patient assessment.

What information does it provide?

Once WMH are segmented, relevant lesion statistics are quantified: lesion number, total lesion volume, dominant lesion volume, dissemination, or entropy among others. All this information can be summarized in a structured report along with the most characteristic slices. These processes will easily assist physicians in the diagnosis of MS patients not in the future, but now.

QuibimStructuredReport_White matter lesions

AUTHORS:

Eduardo Camacho Ramos.

Ana Jiménez Pastor.