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Artificial Intelligence

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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.

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.

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Ines Perea, Strategic Advisor at QUIBIM

Quibim’s Strategic Advisor Inés Perea discusses the radical disruptions that quantitative imaging analysis can provide to cancer research

Quibim’s Strategic Advisor Inés Perea discusses the radical disruptions that quantitative imaging analysis can provide to cancer research amongst other areas

Inés Perea is a Doctor that graduated with honors in the Pharmacy and Executive Business Program by Instituto de Empresa of Madrid. Mrs. Perea has extensive and versatile experience spanning more than 20 years in the Pharmaceutical industry working within different roles within the Medical, Access and Commercial (Marketing, Sales, Strategic Planning) areas and international experience in Global and regional teams. Her main area of expertise is oncology and biologics where she has been involved in the strategic planning and commercialization of more than 10 drugs/indications, most of them linked to some form of biomarker diagnosis.

How did you come to know QUIBIM before becoming their strategic advisor?

I was in contact with founder and CEO, Angel Bayarri months prior to my start with Quibim. A mutual friend of ours, a director of a large genetics biomarker laboratory in Spain was working specifically on oncology genetic biomarker testing and I would get involved with his organization in the arenas of strategic value and development, as those are two of my core competencies. I was involved in the development and strategic value of genetic/biologic biomarkers in clinical trials in oncology. It was back in the 90’s when biomarkers were a thing of science fiction. Well, he introduced me to Angel and I really thought he had a fantastic idea when it came to implementation of 3-d imaging biomarkers and using algorithms to quantitatively analyze tumors. Once I learned about Quibim’s technology, I knew that the growth could be exponential if he partnered with strategic partners.

Your field of expertise is Oncology. The Quantitative Medical Imaging field has evolved significantly but we still use RECIST criteria for evaluation of treatment responses, what do you think is the main reason for this? Standardization?

This is the foundation for therapeutic trials. It is a standard when examining patients, however, it has some practical limitations. For example, with the latest breakthrough area in oncology research, immunotherapy, many immuno-oncology drugs are known to cause fluctuations in tumor size, which when analyzed simply with RECIST criteria, would appear as an increase to the tumor size, but it is now understood that tumor fluctuations occur regularly during immunotherapy treatments, often causing the tumor to grow prior to cell DE progression, which is in fact a good sign that would be captured with RECIST criteria well after the fact. In those cases, RECIST does not allow for factoring in the context of the holistic viewpoint that quantitative digital image analysis would facilitate.

What therapeutic indications are best served by incorporating quantitative imaging analysis into clinical research trials?

There is a very simple answer to this question: any therapeutic indication where standardized radiological assessments are a primary or secondary safety or efficacy endpoint of a study. This includes solid tumors, liver diseases, brain diseases, lung diseases, and many others.

Let’s discuss costs. Quantitative imaging analysis sounds expensive. However, automation and extraction of biomarkers as soon as a hospital acquires and uploads images related to a clinical trial can and do have a macroeconomic effect of lowering costs, therefore long-term costs decrease. What do you respond with when someone brings up this objection?

You are right, it is a tremendous cost saver from a macroeconomic point of view. What will continue to help this cause are national health systems, private funding, insurers, and other payers to see the long-term cost savings and efficiencies that quantitative imaging analysis facilitates. Essentially, we need to get quantitative image analysis included in the health system reimbursement system much like traditional biomarkers of a decade ago. As a matter of fact, it took health systems quite a bit of time to include standard biomarkers that we have all grown accustomed to, included on their reimbursement schedules. While it essentially all boils down to payers and health authorities, us researchers and technology pioneers need to continue to prove medium and long-term efficiencies and validate it with radiologists, clinicians and imaging researchers. Once we are able to do that effectively, the market will adjust accordingly, it always does.

The value seems to be in real-time results that can help in both safety and efficacy outcomes. Please expand further on this thought.

Real time results are everything if you really think about it. The main benefit is obviously how this technology facilitates the real time patient care capabilities of healthcare practitioners. Safety and efficacy instant results from treatments are critical when analyzing real time clinical research data. Imaging biomarkers can and will play a tremendous role in improving patient standard of care, and ultimately can make a real impact on disease outcomes.

by: Dan Sfera, also available at: https://medium.com/quibim/quibims-strategic-advisor-in%C3%A9s-perea-discusses-the-radical-disruptions-that-quantitative-imaging-54cef3a3e082

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.