How Medical Imaging Datasets Can Help AI to Diagnosis the Covid-19 Like Deadly Virus?
The highly contagious disease coronavirus or COVID-19 spreading rapidly confronted healthcare professionals globally with unprecedented clinical trials and diagnostic challenges. As they struggle to cope with such highly transmissible diseases, while at the same time also continue to take care of patients and diagnosis timely among new people who are at risk of being infected.
Here, AI can play a big role in detecting COVID-19 like diseases from the infected patients, helping others for early diagnosis without the help of radiologists. Over the past few years AI algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks with amazing results in medical imaging analysis.
AI in Healthcare
AI with the help of machine learning and big data in healthcare industry is already playing a vital role through various computer systems, applications and AI-enabled devices. It is working round-the-clock assisting patients, controling diseases, carrying medical supplies and disinfecting the hospital premises, buildings and other places automatically, without the help of humans, keeping them away from the risk of catching such infections.
AI in robotics , autonomous flying drones , AI security cameras and self-driving cars , is providing the automated solution in fighting against COVID-19 like deadly diseases. AI applications in healthcare industry are varied; from discovering to developing effective drugs and vaccines for new diseases with precise accuracy. So, in the radiology department, let us find out how AI in medical imaging can help in diagnosing and curing COVID-19 like deadly diseases.
AI in Radiology
AI in radiology works like an artificial mind detecting the disease with an acceptable level of accuracy. The AI-enabled machines or medical systems can not only detect diseases but can also suggest medicines as per the patient’s biological conditions and types of syndromes evident at the initial stage of diagnosis by the doctors or the medical attendants.
When AI-enabled devices or computer systems are trained with a huge quantity of annotated medical imaging datasets , with the right algorithm, they can diagnose such diseases without the help of radiologists being involved in the process. Similarly, to avoid human contacts, AI in radiology can be used to diagnose COVID-19 like deadly diseases with a high level of accuracy.
Earlier in radiological practice, doctors specialized in medical imaging analysis or you can say radiologists visually assessed the medical images for the detection, characterization, and monitoring of diseases. But now, AI systems perform automatically recognizing complex patterns in imaging data and providing qualitative, rather than quantitative assessments of radiographic characteristics.
AI in Epidemiology
Similarly, AI in epidemiology can help doctors and medical experts before the outbreak of deadly diseases to minimize its impact on the people and come up with coping strategy. Once again here, AI medical diagnosis system can do this job precisely if the model is trained with the right quality and quantity of medical imaging training datasets that have been prepared with CT Scan, MRI or ultrasound medical imaging reports of infected patients.
The hosting of such images at one place along with annotation and analysis framework will enable researchers to understand epidemiological trends. Hence, generate new AI algorithms to assist with COVID-19 disease detection, differentiation from other pneumonia and quantification of the lungs’ involvement on CT for forecast or for therapy planning in advance.
Apart from medical imaging, AI in epidemiology can be implemented into various other types of data that only big data experts or data scientists can analyze. For instance; certain trends in changing the behavior of people or other type of a sudden change in economic activities or unexpected increase in demand of specific medicines or healthcare products, etc.
AI in Coronavirus Diagnosis
In the case of COVID-19 outbreak, most of the patients had infected lungs and started to catch pneumonia or common cold, along with sneezing, throat infections, and shortness of breath. The diagnosis of all such symptoms is possible with imaging technology, like X-rays, CT Scans or MRIs of chest or lungs of patients which a radiologist can analyze to understand the severity of the infection.
AI Medical Imaging Analysis for COVID-19 Diagnosis: Use Cases
A Canadian startup and researchers from the University of Waterloo are open-sourcing COVID-Net, a convolutional neural network that aims to detect COVID-19 in X-ray imagery. In response to the pandemic, a global community of health care and AI researchers have produced number of AI systems for identifying COVID-19 in CT scans.
Similarly, tech giants like Yahoo and AI startups claimed that they have created systems capable of recognizing COVID-19 in X-ray or CT scans with more than 90% accuracy. Similarly, a new artificial intelligence-powered deep learning model has helped radiologists in China to distinguish COVID-19 from community-acquired pneumonia and other lung diseases in chest CT imaging.
The study developed as a part of a six-hospital study, where researchers refined the model using 4,356 exams from 3,322 patients. The COVID-19 Detection Neural Network for short—scored high marks, notching 90% sensitivity and 96% specificity for diagnosing the coronavirus infection.
These kind of amazing results demonstrate that the right machine learning datasets using the convolutional networks model can distinguish COVID-19 from community-acquired pneumonia. This model also scored high in differentiating such diseases from novel coronavirus, with 87% sensitivity rate and 92% specificity rate.
AI for Faster Detection with High Accuracy
AI is detecting the infection faster than doctors with better accuracy. In china using 5,000 confirmed cases as their training data, scientists built an algorithm claiming it can detect coronavirus infections in CT scans in just 20 seconds and with 96% accuracy.
Radiologists are giving results that are the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis.
In another study, researchers reached a similar conclusion after going through over 46,000 images. They said the deep learning model showed comparable levels of performance with expert radiologists, and greatly improves the efficiency of radiologists in clinical practice.
Similarly, in China, a new smart image-reading system has been launched by a company that can assist doctors with efficient and accurate diagnoses by leveraging AI technology and helping to control the epidemic through earlier diagnoses and treatment.
Role of AI in Safe Diagnosis of COVID-19
As, we know the COVID-19 virus is highly contagious, hence, doctors or radiologists are also vulnerable to get infected with this deadly virus. With the help of AI in medical imaging diagnosis, AI’s strengths find its ways and liberates the medical staff for more intimate care for the patients where human presence and interventions are indispensable and invaluable.
While handling a patient, a radiologist or technician is required to come in physical contact with the patient for instructions about how to position and breathe correctly. AI is used to take the human presence out of the exam room and allow the radiologists to guide the patient through the process contact-free minimizing their risk of getting infected.
Medical Imaging Datasets for COVID-19 Analysis
To develop an AI model that can detect such diseases through medical imaging analysis, a huge amount of training dataset is required. The COVID-19 smart image-reading system has been trained using similar clinical data and aims to decrease this gap eventually.
Moreover, AI in medical imaging and diagnostics can conduct a comparative analysis of multiple CT scan images of the same patient and measure the variation in the infection. This will help doctors to track the development of the disease, evaluate the treatment and arrive at the prognosis for the patients.
It can assist doctors in diagnosing, triaging, and evaluating COVID-19 patients speedily and effectively. The COVID-19 smart image-reading system also supports AI image-reading remotely by medical professionals outside the epidemic areas.
The medical imaging community globally united to control such diseases with early and safe detection using AI. Hence, to create and share the medical imaging dataset, the Radiological Society of North America continues to build on its extensive body of COVID-19 research and educational resources, announcing a new initiative to build a COVID-19 Imaging Data Repository.
This open data repository will compile images and correlative data from institutions, practices, and societies around the world to create a comprehensive source for COVID-19 research and educational efforts like training the new AI models.
Such data can also be used by a highly experienced radiologist to analyze and annotate the area of interest to create the medical imaging datasets for developing a more reliable AI model that can easily and timely detect such an epidemic with optimum precision.
To make the COVID-19 infection or contagion recognizable to machines, the medical images need to be labeled or annotated manually by experts or by experienced radiologists. Once the data is annotated, machine learning algorithms can detect and learn how to recognize the infection among such patients and diagnose the disease timely through a successfully developed AI model.
Cogito is providing healthcare training data for AI and machine learning development. We specialise in image annotation services with next level of precision to provide high-quality training datasets for computer vision-based AI models.
For deep learning medical imaging diagnosis, Cogito can be a game-changer to annotate the medical imaging datasets detecting different types of diseases done by highly-experienced radiologists making the AI in healthcare more practical with an acceptable level of prediction results in any given scenario.
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