The situation that we are experiencing worldwide is something that we have not foreseen in any way, despite the fact that there had been other virus warnings. Given this reality, Artificial Intelligence (AI) can be a great ally and a powerful weapon when fighting this new highly contagious virus. This science makes it possible to predict the spread of the virus, the evolution within the population and the herd immunity that will be reached, likewise allows the analysis of medical images to carry out a pre-diagnosis.
AI allows both companies in the sector and experts to reduce their time in search of a possible cure. Companies are opting to use 'Machine Learning' and 'AI' to analyze their history and data in order to better anticipate and manage the resources available to them in the coming months.
The great hope is that numerous AI applications are already being developed in this situation, such as Entelai. This Chilean company created by doctors and experts in computer vision, have managed to develop a tool, the Entelai Pic Covid-19 , which in less than a minute, analyzes a chest X-ray and helps the doctor detect cases with Coronavirus and differentiate them from those with other pneumonias or without compatible findings of pneumonia.
Or without going that far, Quibim, the Valencian company that uses artificial intelligence and advanced mathematical models for the diagnosis and monitoring of diseases from medical images. The system developed in this case has been trained with the few cases of infected patients to whom they have been able to access (about 200 TACS), although for the moment it is not a diagnostic tool, since it has not passed the necessary certifications to it. Quibim is a start-up from the Hospital Politécnico de la Fe, which performs its calculations with a cluster SIE Ladon.
Below we leave you a small interview that we have done with the Quibim team;
Artificial intelligence is here to stay...
there are many applications for this technology, from object classification, facial recognition or also known as facial biometrics, understanding languages such as google translate or driving cars, we are surely facing a new industrial revolution.
One of the main difficulties in installing our technology in hospitals is that our national health system is not prepared to integrate this type of technology or there are currently many barriers to implementing it quickly. On the other hand, the lack of labeled data in the field of radiology, essential raw material when training any artificial intelligence algorithm. The lack of this data leads to a slowdown in the development of machine learning tools.
Today there are many AI-based tools in the field of health, for example, in the case of radiology, we develop tools for automatic segmentation of organs, detection of pathologies even in early stages or the extraction of knowledge with mass treatment. data, solutions that alleviate the hospital workload.
All these tools are based on deep learning, where a set of algorithms based on biological neural networks extract and learn characteristics from a large volume of data, so the following developments will depend on the volume of data available.
Currently, we are developing Artificial Intelligence algorithms both for application in different clinical scenarios more related to the study of the disease and in the field of prevention and health maintenance. In a more clinical setting, we continue to evolve our image analysis platform with the launch of a new version that incorporates an artificial intelligence-based pathology detector in chest X-rays and an integrated application for the diagnosis and characterization of prostate cancer, which will be the world's first artificial intelligence tool focused on this disease.
The management of these applications can be carried out by any member of the hospital staff, even on occasions, there are workflows where the technology acts autonomously to later inform the radiologist of the result. However, new subspecialties in radiology or even new engineering techniques will surely emerge in hospitals, which is why profiles such as biomedical engineers or data scientists will be incorporated more assiduously within the organization within the radiology departments. Existing profiles in Europe and the United States.
One of the next advances for health from radiology through AI, will be given by Radiomics, a science that will increase precision in diagnosis, prognostic evaluation, and prediction of therapeutic response. Thanks to the large amount of data that is currently being generated by the use of AI, radiomics plays an important role in population studies to monitor the evolution of the disease over time, and discover new imaging biomarkers that are related to the final clinical objectives: survival, response to treatment, among others.
At QUIBIM, a great commitment is being made to the future creation of digital twins of the human body, fed with information on the state of health of the organs obtained from medical images and integrating other clinical data provided by genomics, clinical tests or diagnostic tests. We think that image is the key to this Health of the future focused on prevention and periodic monitoring of how we are on the inside.
QUIBIM, a biotechnology company specializing in the post-processing of medical images using artificial intelligence (AI) and advanced mathematical models, has made its know-how available to the scientific community to find new diagnostic tools and ways to understand the mechanisms and aggressiveness of the disease caused by COVID-19.
After the open launch of its platform, QUIBIM Precision–COVID 19 and of the algorithms based on artificial intelligence “Chest X-Ray Classifier” and “Chest COVID-19 Similarity”, QUIBIM has become part of the initiative “Imaging COVID-19 AI initiative”, a multicentre European project to improve the diagnosis of COVID-19 in computed tomography (CT) through the use of artificial intelligence.
Led by the Netherlands Cancer Institute (NKI), together with the company Rovobision, the European Society for Medical Imaging Informatics (EuSoMII) and QUIBIM, this initiative aims to train an AI algorithm with anonymized image data from multiple hospitals and healthcare institutions. affected countries throughout Europe to automate the diagnosis of COVID-19 in CT and quantify the lung condition of infected patients, lightening the workload in hospitals and other health centers.
Recently, the Radiology Society of North America (RSNA), with more than 52.000 members from 153 countries around the world, has announced in a press release published last Monday, March 30, its willingness to join the initiative with the common goal of create a secure repository to share cases of those affected by the disease and thus discover patterns in CT images acquired from patients with COVID-19.
“There is currently no effective cure for this virus and there is an urgent need to increase global knowledge about its infection mechanisms, the distribution of damage in the lung parenchyma, and associated patterns. We could not sit idly by, QUIBIM's mission is to improve people's health through innovative developments and artificial intelligence (AI) applied to medical imaging and that is why we have joined this initiative," he explains. Angel Alberich Bayarri, CEO and founder of QUIBIM.
Despite all these advances, we must be realistic, a series of processes are needed to verify both tools and applications that will help us with certainty, and with prototypes of vaccines with a certain effect, all of which could delay the release of the expected vaccine from now to 5 years, but the use of ML and AI is expected to speed up the process, allowing a vaccine to be available in 1 year.
The situation that we are experiencing worldwide is something that we have not foreseen in any way, despite the fact that there had been other virus warnings. Given this reality, Artificial Intelligence (AI) can be a great ally and a powerful weapon when fighting this new highly contagious virus. This science makes it possible to predict the spread of the virus, the evolution within the population and the herd immunity that will be reached, likewise allows the analysis of medical images to carry out a pre-diagnosis.
AI allows both companies in the sector and experts to reduce their time in search of a possible cure. Companies are opting to use 'Machine Learning' and 'AI' to analyze their history and data in order to better anticipate and manage the resources available to them in the coming months.
The great hope is that numerous AI applications are already being developed in this situation, such as Entelai. This Chilean company created by doctors and experts in computer vision, have managed to develop a tool, the Entelai Pic Covid-19 , which in less than a minute, analyzes a chest X-ray and helps the doctor detect cases with Coronavirus and differentiate them from those with other pneumonias or without compatible findings of pneumonia.
Or without going that far, Quibim, the Valencian company that uses artificial intelligence and advanced mathematical models for the diagnosis and monitoring of diseases from medical images. The system developed in this case has been trained with the few cases of infected patients to whom they have been able to access (about 200 TACS), although for the moment it is not a diagnostic tool, since it has not passed the necessary certifications to it. Quibim is a start-up from the Hospital Politécnico de la Fe, which performs its calculations with a cluster SIE Ladon.
Below we leave you a small interview that we have done with the Quibim team;
Artificial intelligence is here to stay...
there are many applications for this technology, from object classification, facial recognition or also known as facial biometrics, understanding languages such as google translate or driving cars, we are surely facing a new industrial revolution.
One of the main difficulties in installing our technology in hospitals is that our national health system is not prepared to integrate this type of technology or there are currently many barriers to implementing it quickly. On the other hand, the lack of labeled data in the field of radiology, essential raw material when training any artificial intelligence algorithm. The lack of this data leads to a slowdown in the development of machine learning tools.
Today there are many AI-based tools in the field of health, for example, in the case of radiology, we develop tools for automatic segmentation of organs, detection of pathologies even in early stages or the extraction of knowledge with mass treatment. data, solutions that alleviate the hospital workload.
All these tools are based on deep learning, where a set of algorithms based on biological neural networks extract and learn characteristics from a large volume of data, so the following developments will depend on the volume of data available.
Currently, we are developing Artificial Intelligence algorithms both for application in different clinical scenarios more related to the study of the disease and in the field of prevention and health maintenance. In a more clinical setting, we continue to evolve our image analysis platform with the launch of a new version that incorporates an artificial intelligence-based pathology detector in chest X-rays and an integrated application for the diagnosis and characterization of prostate cancer, which will be the world's first artificial intelligence tool focused on this disease.
The management of these applications can be carried out by any member of the hospital staff, even on occasions, there are workflows where the technology acts autonomously to later inform the radiologist of the result. However, new subspecialties in radiology or even new engineering techniques will surely emerge in hospitals, which is why profiles such as biomedical engineers or data scientists will be incorporated more assiduously within the organization within the radiology departments. Existing profiles in Europe and the United States.
One of the next advances for health from radiology through AI, will be given by Radiomics, a science that will increase precision in diagnosis, prognostic evaluation, and prediction of therapeutic response. Thanks to the large amount of data that is currently being generated by the use of AI, radiomics plays an important role in population studies to monitor the evolution of the disease over time, and discover new imaging biomarkers that are related to the final clinical objectives: survival, response to treatment, among others.
At QUIBIM, a great commitment is being made to the future creation of digital twins of the human body, fed with information on the state of health of the organs obtained from medical images and integrating other clinical data provided by genomics, clinical tests or diagnostic tests. We think that image is the key to this Health of the future focused on prevention and periodic monitoring of how we are on the inside.
QUIBIM, a biotechnology company specializing in the post-processing of medical images using artificial intelligence (AI) and advanced mathematical models, has made its know-how available to the scientific community to find new diagnostic tools and ways to understand the mechanisms and aggressiveness of the disease caused by COVID-19.
After the open launch of its platform, QUIBIM Precision–COVID 19 and of the algorithms based on artificial intelligence “Chest X-Ray Classifier” and “Chest COVID-19 Similarity”, QUIBIM has become part of the initiative “Imaging COVID-19 AI initiative”, a multicentre European project to improve the diagnosis of COVID-19 in computed tomography (CT) through the use of artificial intelligence.
Led by the Netherlands Cancer Institute (NKI), together with the company Rovobision, the European Society for Medical Imaging Informatics (EuSoMII) and QUIBIM, this initiative aims to train an AI algorithm with anonymized image data from multiple hospitals and healthcare institutions. affected countries throughout Europe to automate the diagnosis of COVID-19 in CT and quantify the lung condition of infected patients, lightening the workload in hospitals and other health centers.
Recently, the Radiology Society of North America (RSNA), with more than 52.000 members from 153 countries around the world, has announced in a press release published last Monday, March 30, its willingness to join the initiative with the common goal of create a secure repository to share cases of those affected by the disease and thus discover patterns in CT images acquired from patients with COVID-19.
“There is currently no effective cure for this virus and there is an urgent need to increase global knowledge about its infection mechanisms, the distribution of damage in the lung parenchyma, and associated patterns. We could not sit idly by, QUIBIM's mission is to improve people's health through innovative developments and artificial intelligence (AI) applied to medical imaging and that is why we have joined this initiative," he explains. Angel Alberich Bayarri, CEO and founder of QUIBIM.
Despite all these advances, we must be realistic, a series of processes are needed to verify both tools and applications that will help us with certainty, and with prototypes of vaccines with a certain effect, all of which could delay the release of the expected vaccine from now to 5 years, but the use of ML and AI is expected to speed up the process, allowing a vaccine to be available in 1 year.