Quibim Precision

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!

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.

H2020-Web

QUIBIM made huge strides in
AI-powered diagnosis thanks to EU H2020

Taking part in the EU’s H2020 initiative translated into huge strides in disease detection and diagnosis by QUIBIM, by enabling the development of tools powered by artificial intelligence (AI) that allow to extract patterns from medical images and significantly improve human performance.

The program, which came to a term on June 30th, culminated in QUIBIM PrecisionⓇ significantly expanding its network of customers and collaborators and an improved international brand awareness.  More recently the Chest X-Ray Classification tool, another solution in the QUIBIM PrecisionⓇ suite, also received CE clearance.

We are very happy we’ve completed the program in time and line with all our objectives, allowing us to add one more stepping stone to the development of AI-boosted healthcare,” QUIBIM CEO Angel Alberich Bayarri said.

QUIBIM, a high-tech SME based in Valencia, Spain, received a €1.25M grant from the European Union’s Horizon 2020 research and innovation program in 2017, to help advance its machine learning and image processing algorithms to extract imaging biomarkers from images generated on CT, MRI, X-ray, US, DXA and PET scans.

With the EU’s support, the Spanish company could finalize the development, validation and automation of new imaging biomarkers in the areas of brain, lung and oncology, as well as achieve the integration of the new computing performance and data visualization frameworks for the QUIBIM platform.

Thanks to the automated analysis of imaging biomarkers, results are ready just within minutes with the best accuracy and reproducibility, through a medically certified, cost-effective and user-oriented solution, which is available to any physician using image-based diagnosis.

QUIBIM allows physicians to make more accurate diagnosis by providing additional information extracted from the analysis of acquired imaging studies. Our technology uses algorithms that scout the image and extracts features as imaging biomarkers that can be compared to normative information in our database, based on patterns that are not easily visible to the human eye. The algorithms combine both AI data-driven techniques and model-driven approaches. As such, our products help to reduce costs of medical testing and misdiagnosis, especially from specialists,” Alberich said.

QUIBIM actively started in 2015 as a spinoff company of La Fe Hospital in Valencia, to help specialists including radiologists and pathologists make the most of AI in their everyday practice, by providing a one-stop-shop solution using imaging biomarkers and powerful algorithms.

More than 60 hospitals worldwide are currently working with QUIBIM tools, most of which have received CE marks and/or are FDA pending. The company has notably developed the new teaching platform of the European School of Radiology and has supplied its QUIBIM Precision® image analysis platform to the AI Precision Health Institute at the University of Hawai‘i Cancer Center in Honolulu.

QUIBIM has received €3.5m funding ever since its creation and has offices in Spain and the U.S. The company forecasts to generate revenues above €36 million and 150 direct jobs by 2023.

European Commision Flag QUIBIM Project

 

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.

181220_IISLAFE PRIMAGE kick offprimage-logo-transparent
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.

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QUIBIM’s AI-FUELED CHEST X-RAY CLASSIFIER GETS CE MARK

  • Chest X-ray classifier is added as a new CE cleared tool within QUIBIM Precision platform, which already received CE mark class IIa certification earlier in 2019.

  • QUIBIM’s Chest X-Ray Classification AI-Tool dynamically learns using new images, meaning the system is continuously evolving and improving over time.

Valencia, Spain – 27th May 2019. Spanish healthcare AI company QUIBIM today announced that its AI-powered Chest X-Ray Classification tool has received CE certification. The company already obtained the class IIa CE mark earlier this year for the imaging biomarker analysis algorithms, the zero footprint DICOM viewer and the platform within the QUIBIM Precision platform, becoming the first Spanish firm to ever receive the clearance.

QUIBIM applies machine learning and image processing techniques to extract imaging biomarkers from medical images in order to assist radiologists and physicians in daily practice. With its AI-based chest x-ray classifier, QUIBIM helps to detect pathological findings that could go unreported due to the heavy workload of radiology departments.

QuibimStructuredReport_Chest X Ray Classifier

“Our tool was designed to prioritize unreported, potentially pathological radiographs, to help radiologists be more efficient by focusing their efforts on studies that are more likely to have pathologies. We are sure this will have an impact in big healthcare systems,” Angel Alberich-Bayarri, QUIBIM CEO and founder, explained.

The solution makes use of a novel architecture based on referee networks combined with Convolutional Neural Networks that have been trained with a database of more than 500,000 images, to calculate the final probability of the X-Ray of being abnormal. Afterwards, the probabilities are used to estimate the presence of pathologies in chest X-Rays.

Because of this Artificial Intelligence methodology, the classifier understands the visual patterns that are most indicative of the different pathologies using the knowledge extracted from the large dataset of radiographs used to train the networks. “QUIBIM’s Chest X-Ray Classification Tool is able to learn further using new images, which means that this system is continually improving and evolving with time,” Rafael López, Artificial Intelligence Engineer at QUIBIM, said.

QUIBIM’S Chest X-Ray Analysis Tool is already available at QUIBIM Precision®, accessible through the cloud with just a few clicks or it can be fully integrated in the radiology department’s workflow as a local solution for seamless interpretation of chest X-Rays.

OCT2019quibim

QUIBIM at the annual Outsourcing in Clinical Trials Europe 2019

We are very happy to share with you QUIBIM´s experience at the last Outsourcing in Clinical Trials Europe 2019 from 14th to 15th of May in Milan (Italy).

Clinical Trials Manager and Chief Strategy Officer, Irene Mayorga and Kabir Mahajan, joined this annual meeting to introduce the services that QUIBIM offers to CRO’s and pharmaceutical companies conducting imaging clinical trials. They had the pleasure of interacting with many novel drug development companies & CRO’s focusing on glioblastoma, pancreatic cancer, other cancers and diffused liver disease and had the chance to show QUIBIM´s experience on this front.

Also, we had the opportunity to discuss QUIBIM´s services as an imaging core lab, not only with the scientific and technical aspects, but also with the design and development of imaging documentation such as the imaging trial charter, the medical imaging validation and imaging acquisition quality control,  statistical analysis and other standard clinical trials management services.

If you did not have the chance to join us in Milan, you can meet Irene Mayorga Ruiz and Kabir Mahajan at the BIO Convention in Philadelphia from June 3- June 6, 2019 where they would be showing the latest developments in the QUIBIM Precision platform for managing clinical trials. You can use the following link to book a demo.

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Breast cancer QUIBIM

QUIBIM uses AI to boost
breast cancer research

As the first ESMO Breast Cancer Congress unfolded 2-4 May in Berlin, Germany, QUIBIM CEO highlighted the role played by artificial intelligence (AI) in breast cancer research.

The European Society of Medical Oncology focused on the progress in treatment options and improved outcomes for breast cancer (BC) patients during its first meeting dedicated to the topic (¹).

One of the key messages of the conference was that therapeutic innovations should go hand in hand with a multidisciplinary, fully integrated approach to patient care, a scenario in which AI can increasingly help make a difference, according to QUIBIM CEO & founder Angel Alberich-Bayarri.

Breast cancer_QUIBIM

QUIBIM breast cancer structured report

AI, and in particular image quantification, can help significantly advance knowledge of this multi-faceted disease, by enabling earlier and better detection,” he said.

From the Pacific to the rest of the world

Recently QUIBIM partnered with the AI Precision Health Institute (AI-PHI) at the University of Hawai‘i Cancer Center in Honolulu, to manage, store and quantitatively analyze medical images and algorithms in breast cancer research.

Using the QUIBIM Precision® image analysis platform, AI-PHI researchers will create large scale imaging repositories with automated extraction of imaging biomarkers to characterize patients’ status. The solution will first be used as the central repository for the mammography studies conducted in the Pacific and will include mammograms from over 5 million women.

The University of Hawai‘i Cancer Center is one of 69 NCI designated cancer centers in the United States and the only one in Hawai‘i and all of the Pacific Islands. It is the only institution that uses AI to analyze medical images to assess health and predict risk of disease in the region.

The project is expected to grow exponentially as its principal investigator Dr. John Shepherd is keen on inviting selected reference centers all across the world to join the network. “It is a very interesting and pioneering work for QUIBIM, and our first big cooperation in the breast cancer setting,” Alberich said.

Seamless integration into clinical workflow

The QUIBIM Precision® image analysis platform enables to incorporate AI algorithms regardless of the institution where it is deployed, to facilitate integration into workflow.

“Researchers can program their own algorithm and code, and add it as a plugin to the platform. This feature, combined with the advanced storage and multi-user annotation capabilities of the platform, allows for a wide adoption within research groups and institutions working with AI. Time for deployment of algorithms in the real world is shortened by 25%, since you have the whole AI pipeline in just one place,” he explained.

The solution, which includes not only quantitative image analysis but also structured reporting capabilities, can be integrated into the radiology workflow and add value to this service. It can for instance be used to integrate AI algorithms to detect cancer without the need of radiologists in a first-read.

Earlier this year the platform received CE Mark certification as class IIa Medical Device, including the imaging biomarker analysis algorithms, the zero footprint DICOM viewer and the platform hosting these components and medical imaging data. The platform is now available for commercial deployment in European hospitals and diagnostic imaging centers.

In addition to the platform, the certification includes 15 algorithms in the five key areas of interest of the company: oncology, neurology, musculoskeletal, liver and lung

Also included in the CE Mark certification is the DataMiner, which was designed in collaboration with the team of Prof. Daniel Keim from Konstanz University to provide advanced visual analytics of large databases of patients for population health management and scientific exploitation. The DICOM web viewer that enables the user to visualize the images of a sequence, draw ROIs or apply filters is also included in the certification.

QUIBIM applies machine learning to develop tools for imaging data quantification, to accelerate image reconstruction, segmentation, detection and data mining. Beyond the breast, the company has created AI algorithms to help detect changes produced by brain and prostate cancer, but also other diseases in the hematological scenario such as non-Hodgkin’s Lymphoma.

(¹): https://www.esmo.org/Conferences/ESMO-Breast-Cancer-2019?utm_campaign=PRESS%20-%20Breast&utm_source=hs_email&utm_medium=email&utm_content=72075686&_hsenc=p2ANqtz-9pH5mSHFhwzXnTGp6oSizohrD75uXccBxo1woBVGUFhJl3K5y_p4Ms4Hoa3LcwRJ_KvILSmOWhsrZxTZSWLwumEXOnOw&_hsmi=72075686

QUIBIM team

La biotecnológica Quibim asume la gestión del banco de imágenes médicas del Pacífico e Italia

  • La empresa participada por Angels, la sociedad de inversión de Juan Roig, cierra un convenio para formar a 5.000 radiólogos tras adaptar su plataforma de extracción de biomarcadores Quibim Precision como herramienta educativa

  • La empresa, que abre nueva sede en Valencia, permite ahorrar tiempo y dinero al sector de los ensayos clínicos gracias a su sistema de cuantificar parámetros en radiología y medicina nuclear

València, 18 de abril de 2019.- La biotecnológica valenciana Quibim ha asumido la gestión del biobanco de imágenes médicas del Pacífico, tras cerrar un acuerdo con el centro de investigación del cáncer de la Universidad de Hawaii. La startup, participada por la sociedad de inversión de Juan Roig Angels, acaba de convertirse, además, en proveedor de este mismo servicio para imágenes médicas en Italia, gracias al sistema Quibim Precision, una plataforma de algoritmos de inteligencia artificial y análisis de imágenes radiológicas que fue desarrollado por esta joven empresa (fundada en 2012) con financiación del programa H2020 de la Comisión Europea.

El sistema Quibim Precision permite analizar imágenes médicas en la nube -aunque puede instalarse en versiones locales en hospitales o compañías- a partir de pruebas de rayos X, ecografías, TAC, resonancias magnéticas (RNM) o PET. Gracias a la aplicación de algoritmos de análisis, el software de Quibim posibilita extraer datos cuantitativos, que sirven para que el profesional médico haga el diagnóstico con más información.

Estos parámetros extraídos reciben el nombre de Biomarcadores de Imagen y aportan datos extraídos de imágenes médicas, relacionadas con procesos biológicos normales, enfermedades (por ejemplo tumores) o respuestas terapéuticas. Los modelos computacionales aplicados a las imágenes sirven para medir de manera objetiva los cambios producidos por una lesión o por tratamientos farmacológicos.

La tecnología Quibim se aplica en la práctica clínica, los ensayos clínicos, la formación en radiología y los proyectos de investigación. Con la adaptación de la plataforma al sector de la investigación se puede hacer una evaluación más temprana sobre la respuesta a los tratamientos en ensayos clínicos, con el consiguiente ahorro de tiempo y dinero, aspectos clave en cualquier proyecto de I+D+i.

Quibim ha cerrado un acuerdo con la Escuela Europea de Radiología (European School of Radiology, ESOR) para que su plataforma de apoyo al diagnóstico sirva como herramienta de formación para más de 5.000 radiólogos en todo el mundo. Ello gracias a que la plataforma ha sido adaptada como instrumento educativo. Actualmente, los radiólogos han de recurrir a meras imágenes proyectadas en power point en sus procesos formativos. La joven startup está inmersa en un proceso de expansión internacional. Hoy está ya presente en España, Reino Unido, Italia, Holanda, Dinamarca, Polonia, Portugal, Chile, Uruguay, Argentina, Estados Unidos, Taiwán, Japón e India.

Entre sus objetivos en este 2019, tras conseguir el marcado CE el pasado año, está conseguir la certificación FDA -un prestigioso sello médico para comercializar en Estados Unidos-  para consolidar una fuerte presencia en este país, ampliar la cobertura  a 60 hospitales y centros de diagnóstico y participar en una docena de nuevos ensayos clínicos de ámbito nacional e internacional.

Inaugura sede corporativa en Valencia

Quibim acaba de inaugurar nueva sede corporativa en Valencia, en el Edificio  Europa, uno de los hubs de negocio de la ciudad. La compañía biotecnológica ha crecido en los últimos cinco años hasta convertirse en una de las siete pymes que lidera mundialmente el desarrollo de tecnologías de postprocesado de imágenes médicas basadas en inteligencia artificial. Cuenta también con oficinas en Madrid y Palo Alto (California). En la compañía trabajan ya una veintena de profesionales.

Según Ángel Alberich-Bayarri, CEO y fundador de la compañía, “estas nuevas instalaciones representan un paso más para la compañía. Comenzamos de forma activa en 2014 con una idea dentro del programa Lanzadera y hoy celebramos con la inauguración de nuestra nueva oficina la evolución que ha tenido la empresa a lo largo de estos años, un gran hito para QUIBIM”.

Sobre Angels

Angels es la sociedad de inversión impulsada por el empresario Juan Roig que tiene como objetivo invertir en líderes emprendedores para desarrollar empresas sostenibles. Ofrece un modelo de gestión basado en el Modelo de Calidad Total, su red de contactos, así como toda la infraestructura necesaria para acompañar al emprendedor, comenzando por las oficinas situadas en el entorno del polo emprendedor Marina de Empresas.

Sobre TechTransfer UPV

Tech Transfer UPV es el primer fondo de transferencia de tecnología impulsado dentro del sistema universitario español que apuesta por proyectos de transferencia de tecnología y emprendimiento generados en la Universitat Politècnica de València para acompañarlos en su salida al mercado.

Dispone de un patrimonio de 3,9 millones de euros. El Fondo está participado por 28 empresarios y profesionales valencianos y de Castellón de diferentes sectores que, además de invertir, participan como validadores de los proyectos. Entre ellos, el Instituto Valenciano de Finanzas, Caixa Popular Productos Citrosol, Air Nostrum, Grupo IVI, Multiscan, ACAL, Arca Telecom, S2 Grupo, Miarco, La Unión Alcoyana Seguros, Nero Family, Blast of partners, Tecnopaking, Vik Consultoría o Ca&CCA ingeniería.

Parenchima workshop quibim

First QUIBIM Precision Hands-On Parenchima Cost Workshop

Valencia, March 14, 2019 –  Celebrating the World Kidney Day, QUIBIM, with the support of the Parenchima initiative, organized the first QUIBIM Precision Training Workshop for leading scientific researchers in medical imaging of kidney from 25 European countries.

On March 12th – 13th, QUIBIM hosted its first face-to-face workshop in Valencia (Spain) to train researchers and engineers who want to integrate medical image quantification algorithms in the Quibim Precision® platform. This is an initiative by QUIBIM with the objective of opening the platform to radiologists, clinicians and researchers who want to develop and implement their own MRI algorithms for chronic kidney disease.

QUIBIM was selected in the previous annual meeting of the Parenchima consortium in  Prague, (October 4th-5th, 2018), as the best system for medical imaging data management and integration of new analysis algorithms for the COST action project. Thanks to its ability to centralise, manage and store data extracted from the medical images from the different partner hospitals in a single platform, QUIBIM  offers the project to allow for acquisition protocols and algorithms comparison in terms of quality and precision, all at the same place.

PARENCHIMA WORKSHOP QUIBIM

The outcome of the project will consist of  new standards for image acquisition and analysis of MRI of the kidney for chronic kidney disease. QUIBIM’s team enjoyed hosting the workshop for the partners and are excited to be part of such a groundbreaking initiative.   

About PARENCHIMA

Renal MRI biomarkers are underused today in research and in clinical practice due to the need for dedicated in-house expertise and development. Transferring solutions to other centres is therefore a challenge, and this leads to a significant duplication of efforts, a lack of standardisation in the methods, and difficulties in comparing results between centres. This also limits commercial exploitation, and hinders the set-up of multi-centre trials or translation into clinical practice.

The overall aim of PARENCHIMA is to eliminate the main barriers to the broader study, commercial exploitation and clinical use of renal MRI biomarkers.

PARENCHIMA will coordinate the research of leading European groups in this area to:

  • improve the reproducibility and standardisation of renal MRI biomarkers;
  • increase their availability by developing an open-access toolbox with software and data;
  • demonstrate biological validity and clinical utility in a prospective multicentre clinical study.

In order to increase the impact of this project we are reaching out to join the efforts. More info at: www.renalmri.org

About QUIBIM

QUIBIM is a company from Valencia (Spain) which applies artificial intelligence and advanced computational models to radiological images to objectively measure changes produced by a lesion or by a pharmacological treatment, offering additional quantitative information to the qualitative approach of radiology. QUIBIM technology and services are applied in clinical practice, clinical trials, radiology education and research projects. More info at: www.quibim.com