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ESOR_QUIBIM Course

QUIBIM provides the platform for the GALEN Advanced Course organized by European School of Radiology

  • QUIBIM provided the software for the hands-on workshop activities, allowing a much more dynamic and interactive case discussion

QUIBIM was honored with the chance to participate in the last edition of the “GALEN Advanced Course on Oncologic Imaging of the Abdomen”. The event was organized by the European School of Radiology (ESOR), the educational initiative of the European Society of Radiology (ESR).

This course was aimed at senior residents, board-certified radiologists and fellows interested in abdominal oncologic imaging and focused on the application of the latest technical advancements and the new European guidelines for imaging.

ESOR_QUIBIM PrecisionQUIBIM provided the software for the hands-on workshop activities, allowing a much more dynamic and interactive case discussion. Based on our QUIBIM Precision® cloud Platform, this tool provides a powerful framework for the creation of new users, the uploading of imaging studies and relevant documentation and for the administration of the course.

Furthermore, the platform has been developed to provide lecturers with convenient features to share studies with students, visualize and edit them using our zero-footprint embedded DICOM Web Viewer and, most important, analyze the studies with any of the imaging biomarker plugins available at QUIBIM Precision®.

This event represents a great milestone for QUIBIM, as ESOR, with over 19.000 participants in more than 250 ESOR courses, has become the major provider of complementary radiological education in Europe and worldwide.

ESOR_QUIBIM

QUIBIM_FEDER2-IVACE

QUIBIM recibe la ayuda del IVACE – Proyectos de I+D de PYME (PIDI-CV)

QUIBIM ha obtenido financiación del Institut Valencià de Competitivitat Empresarial (IVACE) dentro del programa “Proyectos de I+D de PYME (PIDI-CV)” para la realización del proyecto “DESARROLLO DE ALGORITMOS DE INTELIGENCIA ARTIFICIAL PARA LA DETECCIÓN AUTOMATIZADA DE VÉRTEBRAS A PARTIR DE IMÁGENES DE TC EN OSTEOPOROSIS” con número de Expediente: IMITDA/2017/120.

OBJETIVOS Y RESULTADOS DEL PROYECTO

El presente proyecto tiene el objetivo de desarrollar nuevas técnicas de análisis de imagen y algoritmos de Inteligencia Artificial (Machine Learning y Deep Learning) aplicados  a la caracterización de la columna vertebral a través de la segmentación automática de vértebras en imágenes de TC (tomografía computarizada), que permita el soporte al radiodiagnóstico en pacientes con osteoporosis. Para ello, se crea una nueva herramienta software de soporte que permita detectar e identificar automáticamente las vértebras en una imagen de TC. Una vez integrado este nuevo desarrollo como un nuevo módulo en nuestra plataforma Quibim Precission, estaremos en disposición de  caracterizar la microarquitectura ósea de cada una de las vértebras, realizar una evaluación de la misma y ofrecer un radiodiagnóstico avanzado capaz de aportar mayor información sobre la enfermedad ósea del paciente. 

La propuesta de valor para QUIBIM es contribuir con el presente proyecto a completar sus líneas de I+D, en concreto su línea  musculoesquelética. Así este proyecto ha supuesto la creación de un sistema especializado en la caracterización automática de la microarquitectura ósea vertebral, ofreciendo al radiólogo información cuantitativa sobre ésta, para mejorar la evaluación de tratamientos médicos y agilizar el control y seguimiento de pacientes con osteoporosis.

QUIBIM_chest_xray_classifier_logo3

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

ESGAR Conference CCD Dublin Ireland 2018.

Co-Founder Prof. Luis Martí-Bonmatí receives ESGAR Gold Medal

During the 29th Annual Meeting and Postgraduate Course of ESGAR (European Society of Gastrointestinal and Abdominal Radiology) held in Dublin, our co-founder, Prof. Luis Marti-Bonmati received the ESGAR Gold Medal, the society’s highest award for outstanding contributions to the scientific community.

All QUIBIM team is proud and honoured. We congratulate Prof. Martí-Bonmatí on this achievement.

ESGAR Conference CCD Dublin Ireland 2018.

ESGAR Conference CCD Dublin Ireland 2018.

Photo credit: Roger Kenny Photography

Photo legend (from left to right): Steve Halligan (ESGAR President), Luis Martí-Bonmatí (Gold Medallist), Celso Matos (ESGAR Past President), Helen Fenlon (ESGAR Meeting President 2018).

Link to Insights into Imaging post: https://www.i3-journal.org/news/esgar-goldmedal/

 

QUIBIM Imaging Biomarkers made transparent

QUIBIM, AI imaging disruption to showcase the value of Precision

We are in the era of Precision Medicine, and so is Radiology. Nowadays, main imaging modalities like X-ray, computed tomography (CT), magnetic resonance (MR), positron emission tomography (PET) and hybrid machines, among others, have become measurement instruments. Images are not only pictures anymore, but thanks to the application of computational analysis and artificial intelligence, they are data, as you can learn in this excellent manuscript in Radiology.

Nowadays, when radiologists perform measurements of different organs, tissues and lesion properties using a workstation, they are used to get a number (i.e. lesion volume or perfusion). If they take the same images and they get to analyze them in a workstation from another vendor (not straightforward), it is pretty sure that they will obtain different results. This issue has introduced a sense of lack of standardization and homogenization in the quantitative medical imaging field.

I like to say that value is to trust in the product, and we have decided to be the first company in the world to open the validation process and tests results of our imaging biomarkers. Every time we buy a measurement device for daily life purposes (i.e. thermometer) we know the degree of uncertainty, why wouldn’t we do the same in AI algorithms and quantitative imaging?

We are proud to make this announcement at ECR 2018: Now it is possible to see the precision, accuracy and clinical evaluation results of our imaging biomarkers. We provide the precision (through Coefficient of Variation, CoV) and accuracy (through relative error, e) values through the publication of QUIBIM Technical Datasheets that you can find in the resources section of our webpage.

With this strategy QUIBIM is going a step further by being the first multi-vendor, web-based and real precision Medicine company of the medical imaging & AI market.

Concerned by the accuracy of your measurements? Let’s work together.

QUIBIM RECIBE LA AYUDA DEL IVACE-INTERNACIONALIZACION 2017

QUIBIM es un proyecto empresarial de alto impacto social y sanitario, que extrae información cuantitativa de las imágenes médicas radiológicas, mediante técnicas innovadoras y avanzadas de procesado computacional, con el objetivo de mejorar los procesos de diagnóstico de enfermedades con alta incidencia y evaluar adecuadamente los cambios que producen los tratamientos farmacológicos en el organismo.

025-FEDER2-declaracion14-20

Durante 2017 el proyecto de internacionalización QUIBIM ha recibido la ayuda IVACE – “ACCIONES DE PROMOCIÓN EN EL EXTERIOR QUIBIM 2017” (ITAPIN/2017/447) con el apoyo del Fondo Europeo de Desarrollo Regional (FEDER) por un importe de 4.405,16€

IVACE_QUIBIM
QUIBIM_MIUC

MIUC
the new toolkit of QUIBIM Precision® platform to beat traditional workstations

Quibim has implemented a new toolkit named MIUC (Medical Imaging Universal Connector) to close the gap between hospital IT systems and the Cloud. Whereas the Cloud satisfies the processing requirements, Quibim Precision® handle the functionalities related to communications and management of DICOM objects among the hospitals and radiological centers.

Quibim Precision® allows users in hospitals and radiology departments to have a seamless integration of imaging biomarkers analysis within the radiological workflow, due to the MIUC capabilities combined by the Quibim Precision® Cloud computing environment and the interoperability features implemented in our system. Both image upload and data retrieval are fully automated and users only need to access the PACS when they are notified that a new biomarker report is available.

MIUC is placed inside the hospitals and clinics and it is responsible to establish all the required communications between the PACS and Quibim Precision®. During the analysis of imaging biomarkers, the study is anonymized, sent to the Cloud and analyzed. The final result is a one-page report which is sent back to the MIUC or can be directly visualized in the Quibim Precision® web interface. Furthermore, in clinical environments, the report is converted into DICOM objects and stored in the PACS as a new series within the original study. To identify the original study, the MIUC implements backward traceability in the client side to reidentify the anonymized studies.

Our platform is intended to be used by radiologists, either from a clinical environment, thanks to the MIUC, or as a final user using the web interface. In the clinical environment scenario, radiologists using Quibim Precision® do not have to worry about where the study or the report is. Instead, these issues are transparent to the user, who do not have to perform any action to launch a biomarker process, given that the MIUC rule engine does such work for them. The user will be notified by email when a new biomarker report is ready (and available in both the PACS and the Quibim Precision® web interface).

Nowadays, our Quibim Precision® platform is compliant with the DICOM standard at both communication level and data management and formatting level. Specifically, our platform receives imaging studies from hospitals, radiological centers or pharma companies. Then, the system analyzes the study and obtains quantitative measures, which are stored in a quantitative database and structured in a one-page report on a per-patient basis. Finally, this report is returned back as a result.  Quibim Precision® allows annotating biomarker reports using terms from RadLex and MeSH, enhancing the interoperability of its biomarker reports with other health information systems. In fact, the imaging platform is seamlessly integrated with the hospital PACS, being able to query and retrieve medical studies, processing them and storing the resulting biomarker reports as DICOM objects in the hospital PACS. On the other hand, the processing stage is performed on the Cloud, taking advantage of its benefits: high-performance computing and real-time hardware scalability on demand.

But, what has changed?

In previous updates of our platform, we improved the performance, capabilities and user settings view. With this new suite software QUIBIM Precision®- MIUC does query/retrieve the PACS, anonymizes the PACS responses and forwards them to Quibim Precision® in the Cloud. Furthermore, the MIUC leads our solution to a higher level of automation, given that it monitors the PACS querying for incoming studies. Once a new study reaches the PACS, the MIUC analyzes its header and determines whether a new biomarker analysis must be launched or not, depending on some DICOM elements in the study like imaging modalities, study description, series description or body part among others. For each biomarker analysis available in Quibim Precision®, there is a predefined set of rules that establishes which studies are susceptible to be processed by each analysis method. An incoming study matches a given analysis method whenever it fulfils the predefined set of rules for such analysis method. When this happens, the MIUC automatically sends the study to the Quibim Precision® Cloud processing platform, where it will be processed by the matching biomarker analysis pipeline. Once processed, a biomarker report is generated with the results and sent back to the MIUC. Finally, the MIUC stores the report in the PACS as a DICOM object, making the report available for the specialist who requested it. This way, the Cloud platform remains centralized and, at the same time, fully integrated with the hospital IT systems.

With the arrival of MIUC toolkit the need for conventional workstations with expensive licenses in radiology departments completely disappears. As in other business areas that are evolving from product to service, the Quibim image analysis technology was designed to be offered as the service that puts disruptive image analysis solutions at your fingertips.

QUIBIM at BIO 2017

QUIBIM in BIO 2017

Our company was present again this year in the incredibly huge BIO International Convention in San Diego, California, from June 19th to 22nd. Our registration included both a booth at the Spanish pavilion and the access to the One-to-One partnering meetings.

We had 15 planned meetings and many other new contacts thanks to the interaction at our exhibitor space with agents interested in QUIBIM business model. We made several demonstrations of QUIBIM Precision platform and image analysis capabilities in clinical trials. From all the contacts and meetings performed at BIO, I wanted to point out the classification we found according to their profile:

  • Scientific parks and incubators (30%)
  • Investors in Life Sciences (20%)
  • CRO’s and Pharma companies (50%)

From our experience last year in San Francisco, in this edition there has been a higher interest from scientific parks and incubators beyond Boston and Silicon Valley to attract companies to their facilities, showing the benefits of establishing the companies in specific locations, specially in the different states of US. The number of investors stayed similar, but we have been an increasing interest in the field of Medical Devices. Regarding CRO’s and Pharma companies, most of them are progressively considering medical imaging in their clinical trials, and the best, considering us for their solutions. We are so proud to cover those unmet needs on advanced image analysis services for Clinical Trials, allowing pharma companies, CRO’s and Principal Investigators to follow-up in real time their study. In fact, one of the main trends at BIO this year was how data processing will change the way new drugs are developed and launched into market.

QUIBIM CEO (Angel Alberich-Bayarri) & Booth at BIO 2017

QUIBIM CEO (Angel Alberich-Bayarri) & Booth at BIO 2017

 

We were so glad to have this exhibitor space at the Spanish pavilion, and compared to previous editions, it was also the first time that the Valencia region had a dedicated area inside it (similar to Biocat from Catalonia and Biobasque from Basque Country). The Valencia area was organised by IVACE (Instituto Valenciano para la Competitividad Empresarial), and the organism was represented by Mrs. Mónica Payá (representative for foreign investment of IVACE). The Principe Felipe Research Centre (CIPF), was also represented by Oscar David Sánchez (Projects and Technology Transfer Manager).

Valencia region representatives at BIO 2017 in San Diego, Angel Alberich (QUIBIM), Mónica Payá (IVACE), Daniel Calvo (BIOPOLIS), Marisol Quintero (Biooncotech)

Valencia region representatives at BIO 2017 in San Diego, Angel Alberich (QUIBIM), Mónica Payá (IVACE), Daniel Calvo (BIOPOLIS), Marisol Quintero (Biooncotech)

 

Valencia region representatives at BIO 2017 in San Diego, Óscar David Sánchez (CIPF), Mónica Payá (IVACE), Daniel Calvo (BIOPOLIS), Angel Alberich-Bayarri (QUIBIM)

Valencia region representatives at BIO 2017 in San Diego, Óscar David Sánchez (CIPF), Mónica Payá (IVACE), Daniel Calvo (BIOPOLIS), Angel Alberich-Bayarri (QUIBIM)

 

All the days at BIO were so productive that there is a significant work to be done at home, contacting back with the people we met and following up these new relationships.

Obviously not everything is work and there is also some spare time for entertainment at BIO, in the following picture, a rock band playing at the middle of Gaslamp quarter in San Diego. The streets were closed to welcome BIO 2017 participants in a nice evening with food, drink and music, a nice experience!

Band performing at BIO 2017 in middle of Gaslamp quarter

Band performing at BIO 2017 in middle of Gaslamp quarter

Lymphatic system

Imaging Biomarkers in Lymphoma

Imaging has a crucial role in Lymphoma management nowadays. The main applications are based on the evaluation of disease extension in staging and in treatment response evaluation. Recently, thanks to the technology development of PET-CT and CT scanners, it has shown also a high utility in the evaluation of extra-nodular involvement, the early relapse and the transformation from indolent Lymphoma to an aggressive phenotype [1].

Evidence sets PET-CT and standard CT+contrast as the main imaging modalities for staging and treatment response evaluation. The most suitable modality will depend mainly on the aggressiveness and the FDG avidity of the lesion. Therefore, either for Hodgkin’s Lymphoma (HL), aggressive subtypes of non-Hodgkin’s Lymphoma (NHL) or for extra-nodal involvement evaluation in PET-CT will be the way to go for an appropriate staging. However, in cases of non-FDG avidity, mainly in indolent lymphomas (T-cell lymphoma and subtypes of NHL like Chronic Lymphocytic Leukemia, Marginal Zone Lymphoma, Lymphoplasmacytic Lymphoma), contrast enhanced CT is the main modality. Regarding response evaluation, a similar distribution of lymphoma subtypes per modalities is arrange, with the difference in the Follicular Lymphoma (FL), where PET-CT is the most suitable technique for those FL with a high tumoral burden, whereas low tumoral burden FL should be studied by CT with contrast when studying response. Up to now, Magnetic Resonance Imaging (MRI) has still not shown enough evidence in the management of lymphoma patiens beyond Primary Brain Lymphoma. PET-MR has a promising future in Lymphoma evaluation, specially in the current need for low dose follow-up studies that could be done with this modality.

Imaging Applications in Lymphoma

Imaging Applications in Lymphoma

 

Due to heterogeneities in FDG metabolic uptake in different Lymphoma subtypes, Deauville criteria were established to grade the avidity in comparison with mediastinum and liver. However, conventional PET-CT has limitations in the staging of nodular alterations, with the exception of FL, where PET-CT helps to increase the stage of Lymphoma by detecting additional disease in up to 29% of cases. Regarding response evaluation, PET-CT has been recently considered as the gold standard at end of treatment in FL. This is one of the main conclusions from GALLIUM study.

Despite the previous comments, we understand that the staging and response evaluation in PET-CT in patients under new treatments based on targeted therapies or immunotherapy can not be only based on SUVmax evaluations. New imaging biomarkers have been developed in order to evaluate complex clinical scenarios like indolent Lymphoma or reactive inflammatory changes at the end of treatment in patients that have responded to therapy.

In the following table, specific Imaging Biomarkers for different biological objectives are provided:

Objetive Modality Imaging Biomarker
Tumoral burden PET-CT Metabolic Tumor Volume (MTV)
Tumoral burden + Metabolic activity PET-CT Total Tumor Glycolisis (TTG)
Change in metabolic activity PET-CT Voxelwise Delta-SUV (ΔSUV)
Heterogeneity CT & PET-CT Textures

For Imaging Biomarkers implementation, we always follow the step-wise method that we developed and published and that was also considered in this European Society of Radiology guideline.

The first technical step of Imaging Biomarkers development workflow after an appropriate definition of the idea is the Images Acquisition. In PET-CT, European Association of Nuclear Medicine (EANM) guidelines should be followed, and centres should be ideally certified by EARL program.

Metabolic Tumor Volume (MTV)

The first Imaging Biomarker to be calculated is MTV. It is defined by consensus as those lesion voxels with a significant FDG uptake, that is >41% of SUVmax although different thresholds can be evaluated in practise. The typical units are cm^3. The analysis is performed semi-automatically by thresholding and manual correction.

Calculation of Metabolic Tumor Volume

Calculation of Metabolic Tumor Volume

 

Several studies have analysed MTV values in different types of lymphoma, in the following table from Schöder H. J Clin Onc 2016, a nice summary can be appreciated:

Schöder H, Moskowitz C. Metabolic Tumor Volume in Lymphoma: Hype or Hope? J Clin Oncol. 2016 Sep 6. pii: JCO693747. [Epub ahead of print] PubMed PMID: 27601547.

Schöder H, Moskowitz C. Metabolic Tumor Volume in Lymphoma: Hype or Hope? J
Clin Oncol. 2016 Sep 6. pii: JCO693747. [Epub ahead of print] PubMed PMID:
27601547.

The different thresholds used for MTV can be also appreciated (although 41% SUVmax is the one in the majority of them). Also, the wide range of MTV obtained show us the high heterogeneity of the disease and raises also the concern about treatment dose. Should we modulate the treatment given to patient by the MTV? or, on another way, does a patient with 600cm^3 of MTV have to receive the same treatment dose than a patient with 3000cm^3 ? Important research needs still to be done in this field.

Regarding MTV and Follicular Lymphoma, few studies have been performed. The most important one was a retrospective analysis from Meignan et al, where they calculated a MTV of 510cm^3 for 2-year Progression Free Survival (PFS). However, some controversy has arose mainly due to the fact that the inherent error in SUV measurements due to examination variability introduces a final MTV error in measurements around 20%, so the threshold should not be a single value, but a given range of MTV values that consider that error.

 

Total Tumor Glycolysis (TTG)

The TTG measurements is better applied for specific lesions rather than all lesion burden. Therefore, if we focus on specific lesions, the TTG combines information on the FDG avidity and the MTV of the lesion by the following equation:

TTG = MTV x SUVmean

 

Voxelwise delta-SUV 

The structural and anatomic information contained in the CT examination within the PET-CT acquisition can be used for spatial registration of scans of the same patient corresponding to different timepoints (e.g. registration of end-of-treatment CT on baseline CT). The idea behind this is to create a parametric map of the longitudinal SUV changes in the patient, and for that the deformation field resulting from spatial registration is applied to the end-of-treatment PET in order to convert it to the baseline geometry. After this process, the follow-up examination can be superimposed to the baseline and therefore even substracted to calculate the SUV difference between timepoints.

delta-SUV pipeline

delta-SUV pipeline

 

Textures analysis

The image regions can be also evaluated quantitatively by means of texture analysis. Texture analysis allows for the extraction of quantitative descriptors from voxel intensities relationships within an image or region. They are organised in first order (if directly extracted from histogram) or second order (if an additional step is required for their calculation). Texture analysis and specially heterogeneity biomarkers like the entropy and kurtosis have shown promising results in many different cancerous lesions, specially as a prognostic biomarker.

Texture analysis from lymphoma lesion in CT

Texture analysis from lymphoma lesion in CT

In lymphoma, a recent manuscript from Ganeshan B. et al. has shown excellent results in providing complimentary information to the interim PET as a prognostic biomarker.

However, texture analysis techniques can also be applied to other type of images such as the PET component, being able to determine the metabolic heterogeneity (MH) of the lesions. In this regard, lesions with different regional FDG avidity are having a worse prognosis than lesions with a homogeneous FDG uptake.

Metabolic Heterogeneity in FDG uptake in lymphoma

Metabolic Heterogeneity in FDG uptake in lymphoma

 

As you have discovered in this post, there are still many Quantitative Imaging Biomarkers that can be extracted from conventional FDG PET-CT examinations, and which are showing important relationship with lymphoma progression, according to recent investigations. In QUIBIM we are a committed team dedicated to the implementation of these techniques in clinical practise, research and clinical trials. If you want to collaborate with us in this field do not hesitate to contact us and potentially upload a case through our QUIBIM Precision® platform. It will be the best way to start working together in this emerging field.

 

 

Quibim Team

We are Hiring! Do you want to be a Quibimer?

We are excited to announce that we are seeking an Engineer to join our fantastic team in Valencia.

What do we need?

  • Enginner with 1-2 years of experience in the field of software development and medical imaging (biomedical, Computing, Electronics)
  • Experience as Java Developer
  • Experience with SQL databases
  • Experience in web development based on API-REST
  • Experience with GIT version control
  • Experience in application deployment with Apache Tomcat
  • Fluent english

Our ideal candidate’ Skills & Experience…

  • Experience with Spring or Jersey frames
  • Experience in integrating external APIs
  • Experience with the dcm4che library
  • Test Driven Development Experience (TDD / BDD)
  • Experience with the DICOM and HL7 standards
  • Experience in integration with PACS and RIS systems
  • Experience in developing CLI applications

What is a plus?

  • Experience with the C ++, Python or NodeJS languages
  • Experience as a FrontEnd
  • Experience with NoSQL databases
  • Experience with Microsoft Azure

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