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QUIBIM Precision

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.

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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!

 

 

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

 

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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

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.

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.

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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

FEDER+Ivace+Declaracion-CS

QUIBIM recibe la ayuda del IVACE – Certificación I+D+i 2018

QUIBIM ha obtenido financiación del Institut Valencià de Competitivitat Empresarial (IVACE) dentro del programa Certificación I+D+i 2018. Una ayuda financiera de la Unión Europea (Expediente: IMACPA/2018/205) para la realización del proyecto “DESARROLLO DE NUEVOS ALGORITMOS DE INTELIGENCIA ARTIFICIAL APLICADOS AL ANÁLISIS AUTOMATIZADO DE BIOMARCADORES”. 

El objetivo del proyecto es el desarrollo de nuevos algoritmos para la segmentación automática de distintas áreas anatómicas del cuerpo humano (espina dorsal, hígado, pulmón, corazón, etc.). Estos algoritmos, basados en nuevas técnicas de Inteligencia Artificial, están específicamente diseñados y entrenados para discernir las regiones a analizar, segmentar órganos específicos y realizar mediciones automatizadas de biomarcadores de imagen.

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New Chest X-Ray Classification Tool

Despite the technological evolution of imaging modalities like CT, US and MRI, conventional X-ray remains the most performed examination in radiology departments, and remains a fundamental tool for anatomical analysis in the detection and diagnosis of respiratory diseases and bone tissue alterations. However, radiology departments have limitations in reporting the X-Rays due to the limited resources available (link).

QUIBIM has developed a Chest X-Ray Classification Tool that offers a solution to this problem which can help radiology departments become even more efficient. This classifier developed in collaboration with Hospital Universitario y Politécnico La Fe, estimates the probability of chest X-Rays of having a pathology using Artificial Intelligence.

How does it work?

This tool makes use of fourteen Convolutional Neural Networks trained with a database of more than 100,000 images (NIH ChestXray14 dataset) to estimate the probability of presence of the following pathologies in chest X-Rays: atelectasis, cardiomegaly, effusion, infiltration, mass, nodule, pneumonia, pneumothorax, consolidation, edema, emphysema, fibrosis, pleural thickening and hernia. Afterwards, the probabilities are used by a Fully Connected Neural Network to get the final probability of the X-Ray of being abnormal.

XRAY chest

Because of this AI 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.

 What information does it provide?

Once all the quantifications are performed this tool provides an intuitive Structured Report with the patient’s information, the abnormal probability of the radiographs analyzed and the representation of the findings. In addition, if the image is classified as abnormal, the report shows the three pathologies that are more likely to be found and a heatmap that highlights the most abnormal regions. This report is designed to be very user friendly, to assist the user in understanding the tool’s findings at a glance in order to make its usage highly efficient.

Why is this new tool so useful?

Using this technology it is possible to prioritize unreported, potentially pathological radiographs which allows radiologists to focus their efforts on studies that are more likely to have pathologies and thereby become more efficient. This tool essentially ensures that pathological findings, which could have been unreported due to the heavy workload of radiology departments, are correctly reported.

QUIBIM’s goal is to provide the radiology departments with an optimal solution for automatic reading of X-Rays without interfering in the  workflow of the department.

QUIBIM’S Chest X-Ray Analysis Tool is already available at QUIBIM Precision® Depending on the needs of the department, this tool is 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.

boton try quibim