Quibim Precision

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.

QUIBIM_chest_xray_classifier_logo3

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

QUIBIM TEAM ECR 2019

ECR 2019 sets the trend in Artificial Intelligence

The city of Vienna was at the real core of Medical Imaging and AI at the 25th European Congress of Radiology (ECR). At QUIBIM we always enjoy being at this annual meeting because it is the perfect combination of a science and industry exhibition. In this edition, the congress reached the milestone of 30,000 attendees, and the numbers are expected to go up with each year.

QUIBIM was glad to be participating in both, the scientific and industry sessions. Our oral presentations were mainly addressing current challenges of artificial intelligence (AI) and convolutional neural networks (CNN) in clinical needs like metabolic disorder, prostate cancer and osteoarthritis. Ana Jiménez-Pastor, Rafael López-González and Fabio García-Castro, R&D Engineers at QUIBIM presented our new research in image processing pipelines aiming to perform a virtual dissection of the organs through an automated segmentation combined with features extraction.

Personally,  I was happy to give 3 lectures focused on the future of radiology: 3D Post-processing in 2019, Deep Learning in Medical Imaging and Start-up in Radiology. In the 3D Post-processing lecture I introduced, what I think is the main revolution of AI in our field the concept of Virtual In-Vivo Dissection (VIVID), a name coined by my team and I at QUIBIM, which is a strategy of isolating human body organs in medical images for  characterization through features such as imaging biomarkers. This has several applications, challenges and is difficult to solve by traditional computer vision algorithms like liver or cartilage segmentation in Magnetic Resonance Imaging but it has become a reality thanks to the use of CNN architectures such as U-Net combined with deep supervision. In the Deep Learning in Medical Imaging session, I  focused on using other presentation formats and I was glad to give a TED talk and share the podium with Dr. Wiro Niessen, who spoke about Machine Learning in a Pecha Kucha format. This session was organized by the European School of Radiology (ESOR) and was chaired by Prof. Dr. Valérie Vilgrain, and I must say the atmosphere was excellent and the room was really packed! Finally, in a session chaired by Prof. Dr. Elmar Kotter,  we shared our insights on how to create a start-up company in Radiology from scratch, how to get funding from investors and the main considerations when scaling-up.

QUIBIM was also invited by the ECR to give a presentation within the Artificial Intelligence Exhibition (AIX) sessions, a new space for innovative AI companies. During this session, moderated by Dr Hugh Harvey and Dr Wim Van Hecke, we presented the newest version of the QUIBIM Precision V3.0 platform launched at the last RSNA 2018.

MRS QUIBIM Analysis_2

QUIBIM now offering advanced MR spectroscopy analysis including 2-HG for IDH1 mutation detection

Continuing its focus in the field of Oncology, QUIBIM is proud to offer an advanced MR Spectroscopy analysis tool. This tool developed in house by QUIBIM will help oncologists to improve diagnosis and treatment of brain gliomas, a disease that has an incidence of 22.6 per 100.000 population in United States.

In order to have reproducible, reliable and accurate information from tumors, we provide physicians with quantification of the metabolite by analyzing Magnetic Resonance Spectroscopy (MRS) images. MRS is a non-invasive imaging technique widely used to obtain chemical information by detecting relevant metabolites concentration from a spectrum.

Even though it is possible to detect almost any metabolite in the body, QUIBIM is currently focusing on the detection of the 2-Hydroxyglurate (2HG) because of its influence in the prediction of low-grade brain gliomas. 2HG is a metabolite, normally present at very low levels in healthy cells and tissues because of the reduction of α-ketoglutarate (α-KG) catalyzed by isocitrate dehydrogenase (IDH) protein. The IDH enzyme mutation in low-grade brain gliomas produces an accumulation of 2-HG which makes the measurement of this oncometabolite crucial to distinguish IDH-mutant gliomas from other brain mass (Stefan, et al., 2010).

MRI quality assurance

The quality assurance of the Magnetic Resonance Imaging (MRI) images is important and mandatory to ensure consequent and reliable results from the MR analysis. For that reason, a qualitative evaluation of the images is performed by QUIBIM to guarantee a strict and standard control of the images by checking the accomplishment of the MRI acquisition protocol set and the absence of artifacts.

MRS quality assurance

The first technical information QUIBIM provides with, is the graphical representation of the voxel placement in the sagittal, axial and coronal view depicted in a structural image. This quality review is important in order to assure the correct position of the voxel in the MRS acquisition avoiding necrotic tissue or non-tumor substances what can affect MRS results.

MRS quantification

Once the quality check of the MRS acquisition is done, QUIBIM produces a technical structured report, quantitative and graphical information about the MRS analysis.

QUIBIM_MRS Structured report

The report, apart from the main images and quantitative information, includes a spectrum graph and the voxel placement. Each peak of the spectrum corresponds to a different substance or metabolite with a different resonance frequency. This difference is usually measured in an independent scale besides the principal magnetic field (parts per million). The intensity of each peak is related to the concentration of the substances in the studied volume (Sánchez, et al., 2001).

To perform the analysis, the voxel in the tumor area and a reference acquisition corresponding to water are required. An automatic analysis of the spectrum is done providing this information and setting the up and down boundary value in ppm according to the metabolite searched. Then, we automatically process the spectrum filtering the water peak in the time domain, reducing the spectrum noise, adjusting the ppm scale according to Creatine and Choline position, and correcting the phase of the signal in order to also correct the baseline offset level to ensure the reliability and accuracy of metabolites’ concentration (Martí-Bonmatí & Alberich-Bayarri, 2013).

Therefore, it is possible to obtain from the studied metabolite its absolute concentration, the percentage of standard deviation (%SD) and its relative concentration using Creatine as a reference value (/Cr), sometimes adding other reference metabolites like PCr as the example of Figure 1 (/Cr+PCr).

The information provided by the standard deviation is essential to read MRS results because absolute concentration reliability depends on them: metabolites with a %SD less than 50 are practically undetectable with this data whereas a %SD<20 is a rough criterion for estimates of acceptable reliability. In addition, the third column indicates ratio relative to /Cr. This quantitative information will appear in the lower part of the report followed by a space for the signature of the scientist from QUIBIM once the analysis is done.

MRS analysis is now available in QUIBIM Precision® Platform to support oncologists and radiologists in brain gliomas treatment and diagnosis.

boton try quibim

References

  • Francesca Branzoli, *. A. (2018). Highly specific determination of IDH status using edited in vivo magnetic resonance spectroscopy. Neuro-Oncology, 907-916.
  • Martí-Bonmatí, L., & Alberich-Bayarri, Á. (2013). Disease Biomarkers: Modelling MR Spectroscopy and Clinical Applications in Bioinformatics of Human Proteomics. Valencia: Springer.
  • Min Zhou, *. Y. (2018). Diagnostic accuracy of 2-hydroxyglutarate magnetic resonance spectroscopy in newly diagnosed brain mass and suspected recurrent gliomas. Neuro-Oncology, 1262-1271.
  • Sánchez, J., Santos, A., SantaMarta, C., Benito, C., Benito, M., & Desco, M. (2001). Herramienta de Análisis de Espectros de RM. CASEIB. Madrid.
  • Stefan, G., Rob A., C., Mark D., M., Edward M., D., Mark A., B., Hyun Gyung, J., . . . David P., S. (2010). Cancer-associated metabolite 2-hydroxyglutarate accumulates in acute myelogenous leukemia with isocitrate dehydrogenase 1 and 2 mutations. Journal of experimental Medicine, 207(2), 339–344.
boothecr

Meet us at ECR 2019

We are delighted to share that QUIBIM will attend the 25th European Congress of Radiology 2019 (ECR) from Wednesday, February 27th to Sunday, March 3rd, 2019. This year we have changed our standard location, now you can meet us at the AI Exhibition area (EXPO X1) booth #AI-15.

ECR attendees will have the opportunity to explore our latest version of the platform QUIBIM Precision® V3.0, focused on the Symbiosis of Radiology and Artificial Intelligence to seamlessly integrate imaging biomarkers into radiology workflows. Come and try our AI solutions and imaging biomarkers analysis!

Book a DEMO

Make sure not to miss our scientific contributions:

  • Wednesday, February 27:
    • 3D post-processing in 2019 – Dr. Ángel Alberich Bayarri.   |  Imaging Informatics, Artificial Intelligence and Machine Learning (Room N) – 16:00 – 17:30
    • Deeply supervised networks for the automated liver segmentation and quantification on MECSE-MRI – Ana Jiménez Pastor | EPOS
    • Stress testing a deep learning algorithm for normal/abnormal classification of Chest X-rays on a spectrum-biased abnormal – Rafael López Gónzalez  |  EPOS
      weighted dataset.
  • Thursday, February 28:
    • Functional imaging of the liver Chairperson’s introduction – Dr. Luis Martí Bonmatí. |  Abdominal Viscera, Contrast Media (Room M 5) – 10:30 – 12:00
    • AI PITCH – Dr. Ángel Alberich Bayarri.  |  Artificial Intelligence Exhibition (AIX) Theatre ( AIX Theatre) – 11:40
    • What to think about when writing a paper – Dr. Luis Martí Bonmatí. |  Education, General Radiology, Professional Issues (Room: C&T 3) – 14:00 – 15:00
    • Deep learning (DL) in medical imaging – Dr. Ángel Alberich Bayarri.   |  Education, General Radiology, Artificial Intelligence and Machine Learning (Room: M3) – 14:00 – 15:30
    • Quantification and evaluation of pre-post exercise femoral cartilage thickness and T2 changes in ultramarathon athletes – Fabio García Castro  |  Musculoskeletal, Imaging Methods (Room: O) – 14:00 – 15:30
    • How to manage critical reviews – Dr. Luis Martí Bonmatí. |  Education, General Radiology, Professional Issues (Room C&T 3) – 15:00 – 16:00
  • Friday, March 1:
    • ECR Academies: Radiology Leaders’ Bootcamp: Dream team Chairperson’s introduction –Dr. Luis Martí Bonmatí. |  Management/Leadership (Room M 2) – 10:30 – 12:00
    • Start-up in radiology – Dr. Ángel Alberich Bayarri.   |  Management/Leadership (Room M2) –  14:00 – 15:30
  • Saturday, March 2:
    • Automated Prostate multiregional segmentation in Magnetic Resonance using deeply supervised Convolutional Neural Networks – Rafael López González  |  Artificial Intelligence and Machine Learning, Oncologic Imaging, Imaging Informatics, Genitourinary, Physics in Medical Imaging (Room G) – 16:00 – 17:30
  • Sunday, March 3:
    • Automatic visceral fat characterization on CT scans through Deep Learning and CNN for the assessment of metabolic syndrome –  Ana Jiménez Pastor | Artificial Intelligence and Machine Learning, Abdominal Viscera, GI Tract, Oncologic Imaging, Imaging Informatics (Room D) – 14:00 – 15:30
    • Liver Case-Based Diagnosis Training – Dr. Luis Martí Bonmatí. |  Education, General Radiology, General Radiography (Radiographers) (Room E1) – 13:00 – 15:30

Join us at ECR 2019!

 For more information, get in touch with us at contact@quibim.com

QUIBIM CE MARK - ISO13485 2016

QUIBIM algorithms, viewer and platform get
CE Mark certification

Valencia, Spain – 11th January 2019. QUIBIM Precision® image analysis platform has 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. With this milestone, the products are now ready for commercial deployment in European hospitals and diagnostic imaging centres.

The platform, which includes not only quantitative image analysis but also structured reporting capabilities, can be seamlessly integrated into the radiology workflow adding value to the specialists. In addition to the platform, this certification includes 15 algorithms grouped in the 5 main pillars of QUIBIM: Oncology (ADC Diffusion, IVIM Diffusion, Semiquantitative Perfusion, Pharmacokinetics Modeling, T1 mapping, T2 mapping) , Neurology (White matter lesions detection, Brain atrophy modules), Musculoskeletal (3D /2D Trabecular Bone Microarchitecture),  Liver (Liver fat and Iron quantification),  and Lung (Lung Emphysema and densities); Also included in the CE Mark certification is the DataMiner, which is designed to provide advanced visual analytics of large databases of patients for population health management and scientific exploitation, and the DICOM web viewer that enables the user to visualize the images of a sequence, draw ROIs or apply filters.

QUIBIM areas

“We are so proud of getting the ISO 13485:2016 and the CE certification by BSI, one of the most prestigious notified bodies. This allows QUIBIM to commercialize QUIBIM Precision® as a Medical Device in the European market, which is a major milestone for our company.”, states Ángel Alberich Bayarri, CEO & Co-founder of QUIBIM. He adds, “Since its inception, QUIBIM has done extensive research and validation for its products together with European, American, Indian, and South American partners, ranging from top academic and research hospitals & diagnostic centres to the leading pharmaceutical companies and CRO’s, sharing the results in publications in both national and international congresses. Now with the CE Mark approval, QUIBIM is excited to commercialize its efforts in Europe and the regions of the world where the CE Mark is accepted.” 

 “QUIBIM is a company committed to improving diagnosis, which has a huge impact on patients quality of life. Therefore, quality is one of the aspects we pursue the most in our company, not only by certifying the quality of our product with the CE Mark but also at the organizational level. This is why we have established our Quality Management System based on and certified by the ISO 13485:2016, a standard related to the quality management system applicable to medical devices.” pointed Belén Fos Guarinos, Quality Assurance And Regulatory Affairs Manager at QUIBIM.

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 Precision® is the image repository and analysis platform provided by the company and is increasingly being used by customers across hospitals, clinical trials, research projects, biobanks and educational institutions across the world. The company is member of the European Institute of Biomedical Imaging Research (EIBIR) and of the Avicenna Alliance and receives funding from the H2020 program of the European Commission.

More info at:

contact@quibim.com
+34 961 243 225
www.quibim.com

Download: QUIBIM algorithms, viewer and platform get CE Mark certification