Posts Tagged :

imaging biomarkers

Hiring2-01

We are hiring a PROJECT MANAGER – R&D Engineer

QUIBIM is now hiring a highly motivated researcher to join our R&D Team in Valencia, with a strong background on the latest medical imaging and AI technologies for imaging biomarkers analysis. He/She will be dedicated to the development of a modular platform based on a plug-in software architecture of prostate imaging quantitative analysis. In addition to this, the candidate will have the opportunity to participate and further develop his/her research career within the framework of R&D projects QUIBIM is part of. This includes world-leading projects in COVID-19 and AI applied to Medical Imaging diagnosis on a variety of diseases (Brain & Stroke; Oncology and Musculoskeletal). This position is intended to be permanent and further professional development, responsibilities and benefits are expected after the 1-2 year.

Main job duties and responsibilities

  • Lead the R&D strategy for qp-Prostate Solution. Development of a modular platform based on a plug-in software architecture of prostate imaging quantitative analysis
  • Integration of new analysis methodologies. 
  • Participation in all Research Projects that QUIBIM is involved in. 
  • Oversee all algorithms design and changes in QUIBIM products.
  • Keep abreast of new trends and best practices in the technology AI and Medical Image sector. 
  • Take the initiative in thought leadership, innovation and creativity. 
  • Work closely with CEO, R&D Manager, Clinical Trials Division Manager and CTO to define and deliver new products and enhancements. 

Requirements/ Preferred qualifications

  • Computer Science / Technology / Medical sciences: PhD or equivalent 
  • PhD in Telecommunication Engineering and/or Biomedical Engineering. 
  • Research experience in data images processing and imaging biomarkers. 
  • Knowledge of techniques of high-resolution magnetic resonance images. 
  • Prior experience in medical imaging processing. 
  • Knowledge of cloud technologies. 
  • At time of recruitment, applicants must have not resided or carry out his/her main activity (work, studies, etc.) in Spain for more than 12 months in the 3 years immediately before the job starting date. Short stays such as holidays are not taken into account. 
  • Hands-on experience with complex project management.
  • Experience on Image Analysis companies is a plus. 
  • IT Knowledge: Python, MATLAB, C++, MongoDB, Azure, Node, Angular. 
  • Advanced level of English with oral fluency.
  • Highly analytical, detail-oriented and a strong sense of business acumen: you have a track record of managing new ideas and creative solutions.
  • Ability to manage and prioritize your workload in a fast-paced, high-growth, occasionally ambiguous environment. 
  • Proactive problem solver and critical thinking. 
  • Attention to details.
  • Integrity. 
  • Responsibility, leading the solution to challenges

If you are proactive, dynamic and forward-thinking profile Apply Here

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

 

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)

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.

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

D3YI165WAAUCunr

Comprehensive solutions will boost AI use in medical imaging

Integrating artificial intelligence (AI) into the radiologist’s workflow is still challenging, but all-inclusive solutions can help unfold algorithms’ power, QUIBIM CEO and founder Angel Alberich-Bayarri explained during the ESR AI Premium Event, which was hosted by the European Society of Radiology (ESR) and the European School of Radiology (ESOR) earlier this month in Barcelona.

Medical imaging AI is a bubbling field but very few solutions are used in daily practice today, Alberich told delegates during the busy meeting, which gathered top researchers in medical imaging AI and thousands of online attendees. “We have a lot of research, AI algorithms and start-ups, but few are really embedded in the radiology workflow,” he said.

A main obstacle to AI integration is a lack of knowledge of utilities. For most imaging biomarkers, the real application relationship with clinical endpoints on a large scale – diagnostic, prognostic and treatment response – remains unknown. Clinicians don’t want to integrate biomarkers that have not been validated, but if they don’t gather information massively and try to understand how biomarkers relate to the disease, they will never help advance healthcare, Alberich explained.

“We have to change our minds and not wait for biomarkers to be validated before they can be extracted on a daily basis. Similarly to genomics: we have to do sequencing and study diseases to detect unknown mutations by ourselves,” he said.

The lack of annotated data is another challenge. Radiology reports are not filled with a focus on data annotation, but on descriptive language that needs to be processed by natural language processing (NLP). However they would be an ideal source of knowledge, and not just for the clinicians.

One-stop-shop platform using biomarkers

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

“Offering a single solution embedded in workflows is key because radiologists will not buy all start-up micro developments but the best platform,” he said.

The very name of the company is an acronym and stands for Quantitative Imaging Biomarkers in Medicine. Imaging biomarkers enable to measure everything happening in the body to extract parameters that can provide information on the tissue of the lesion type beyond classification. Fuelled by AI, these biomarkers can deliver unprecedented information on disease.

“Many different imaging pipelines have dramatically changed thanks to AI integration. For example, we used to have lots of problems doing segmentation with traditional algorithms in tissues and organs such as the liver in MRI. Thanks to AI, segmentation has improved and with it our knowledge of liver disease,” he said.

A prerequisite is to integrate data mining solutions to make radiomics easy to everyone. This means it must be embedded in the same platform, as current solutions are not able to treat this amount of quantitative data from patient cohorts. “A lot of parameters are quantified in daily routine, but there is still no way to store and process them massively. Our current PACS systems are simply not prepared for quantitative data,” Alberich said.

There is a lot of sense in working within a structured report (SR), as it enables to annotate data that will further advance research. Prospectively the studies can be very well annotated if AI imaging biomarkers are integrated in the fields of the SR. Working with the SR would tremendously facilitate communication between clinicians and with patients.

“We are building the radiology report of the future, only we’re doing it now. Patients do not understand radiology reports, so we have to change the way we communicate. We are very much aligned with the standardized way blood test findings are reported, everyone understands whether findings are in or out of range and if there is any abnormality. We have to be more intuitive in our communication,” Alberich said.

QUIBIM is developing quantitative, one-page long structured reports that are actionable, quantitative and automated. The reports are designed with KOLs in each speciality, to make sure they reflect the reality of clinical practice.

Clinical input and powerful technology

Cooperation with clinicians is a key axis for the company, which uses a stepwise model to create new solutions with clinicians in the loop.

The company develops highly performing AI algorithms using unprecedented network architectures, for instance, multiscale convolutional networks ensemble in brain MR, 2.5D network in liver MR and referee network in chest x-ray, and hundreds of thousands of annotated images that have been acquired through cooperation with researchers and university hospitals.

QUIBIM has launched algorithms in different product areas to provide a head to toe imaging biomarker solution; in neuro for Alzheimer, ALS, MS, stroke and cerebrovascular events; in MSK for articulations; in chest for screening, COPD and fibrosis; and in breast for screening. More products will soon be released that will focus on other body areas.

More than 60 hospitals worldwide currently use QUIBIM tools, most of which have received CE marks and/or are FDA pending. The company has notably developed the new ESOR teaching platform 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.

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

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_Symbiosis of Radiology and AI

QUIBIM at RSNA 2018!

We are happy to announce that QUIBIM will be attending this year’s annual meeting of the Radiological Society of North America – RSNA 2018. From November 25 to 29, many of our Quibimers will be in Chicago demoing, sharing and showing our Radiomic solution for Hospitals and Radiology departments.

This year, QUIBIM developments are focused on the Symbiosis of Radiology and Artificial Intelligence to seamlessly integrate imaging biomarkers into radiology workflows.

QUIBIM_RSNA BOOTH 7367G

Attendees can find us at the Machine Learning Showcase – Booth #7367G  (North Building Level 3), where anyone is welcome to come over and explore the latest version of our QUIBIM Precision® Platform for medical images processing and imaging biomarkers analysis.

WANT TO DISCOVER OUR PLATFORM?

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QUIBIM will be also taking part in the Machine Learning Showcase through a communication from our CEO and Founder, Ángel Alberich-Bayarri, on November 25 at 12:30 pm. The communication is entitled “QUIBIM Precision 3.0: AI as a Means, Not an End, for Imaging Biomarkers Integration in Clinical Practice” and shares QUIBIM insights about the role of AI on imaging biomarkers integration.

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Furthermore, as members of the NVIDIA Inception Program, QUIBIM will be demoing at the NVIDIA booth #6568 on November 26 at 10:00 am. Don’t miss it!

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It is a great opportunity for QUIBIM to be engaged in such a recognised event and get in touch with professionals in the fields of radiology and medical imaging.

MEET US!