Corporate

Recurso 2

ImagingCovid19AI.eu now an international initiative

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

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

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

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

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

Fighting COVID19 through AI

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

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

Our role in the initiative

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

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

Visit Imaging COVID-19 AI initiative site – LINK

 

 

NVIDIA QUIBIM

QUIBIM Brings AI to Radiology Workflows with NVIDIA EGX

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

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

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

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

QUIBIM Precision® and NVIDIA EGX

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

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

RSNA 2019

QUIBIM at RSNA 2019
with new features!

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

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

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

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Seamless AI for Radiologists

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

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

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

RSNA_schedule a meeting

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

More at RSNA 2019

AI THEATER PRESENTATION

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

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

AI WORKSHOP

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

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

Interested?  PLACES ARE LIMITED!

Register now

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

Join us at RSNA 2019!

 

 

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QUIBIM HOSTING EUSOMII ANNUAL MEETING 2019

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

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

Book a DEMO

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

Check out our participations at AMI 2019:

FRIDAY, October 18th 2019

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

SATURDAY, October 19th, 2019

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

POSTERS:

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

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

QUIBIM_Curso IA Banner

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

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

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

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

Puedes realizar la inscripción en las siguientes modalidades:

Recurso 4

PROGRAMA – Horario de 17 a 20 hrs.

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

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

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

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

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

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

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

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

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

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

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

InscribemeMás info: PROGRAMA 

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

 

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.

Welcome post QUIBIM - José Gascón and José Sánchez 2

Our family keeps growing

We are proud to announce the incorporation of two new Quibimers in our growing team. Both excellent computer scientists, passionate about practising sport and named José!

José María Gascón Artal

In our Development Department, we welcome José Gascón Artal as Full Stack Web Developer. With nearly 4 years of previous experience in the IT sector working in the multinational technology consultancy as a Software Developer and in Communications agencies as Chief Technology Officer, he has joined our development team to help keep developing and improving the QUIBIM Precision Platform with new features.

Thanks to his great knowledge of JavaScript technology and his agile way of working we are sure he will play an important role in future QUIBIM’s milestones.

José is enthusiastic about developing his own apps with the technology stack MEAN and when he is not with his PC you can find him riding his motorbike, snowboarding or playing volleyball.

José Sánchez García

With a Bachelor’s and Master’s Degree in Telecommunication Engineering, both at the Polytechnic University of Valencia (UPV), and a specialization in Deep learning and Computer Vision algorithms, José Sánchez García has become part of our team as Data Scientist in the AI (Artificial Intelligence) area. A part-time job he combines with the role of Biomedical Engineer at the Fundación Hospital Provincial de Castellón, where he is in charge of new imaging biomarkers development and its integrations into QUIBIM Precision Platform.

Previous to join QUIBIM José has worked in Visia Solutions, where he lead image processing projects in Ford and SRG Global between others and he was also Intern Programmer in Everis. It was during his Master’s Thesis that he started to collaborate with us integrating Artificial Intelligence in Neuroimaging Analysis and when the synergy arose.

To achieve comprehensive success, as he says, he likes to combine his time at work with working out at the gym, doing Crossfit and a healthy diet not to mention that José is also really keen on doing song covers and playing tennis.

We wish you both a brilliant future in QUIBIM. We couldn’t be happier to welcome you as a new Quibimers!

Meet our team

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.

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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