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How AI in Medical Imaging Can Help in Diagnosis of Coronavirus?

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AI in Medical Imagining coronavirus

The highly contagious disease coronavirus or COVID-19 spread rapidly confronted healthcare professionals globally with unprecedented clinical trial and diagnostics challenges. As they struggle to cope with such highly transmissible disease at the same time also continue to take care of patients and diagnosis timely among new people who are at risk of getting infected.

Here, AIcan play a big role in detecting the COVID-19 like a disease from the infected patients, helping others for early diagnosis without the help of radiologists. Actually, 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

Though, AI in healthcare is already playing a vital role through various computer systems, applications and AI-enabled devices working round-the-clock to assist patients, control disease, carrying medical supplies and disinfecting the hospital premises, buildings and other places automatically without the help of humans keeping them away from such infections.

AI in robotics, autonomous flying drones, AI security cameras and self-driving cars are providing the automated solution in fighting with COVID-19 like a deadly disease. AI is also successfully involved in discovering and developing effective drugs and vaccines for such new diseases with the best level of accuracy. So, in the radiology department, let’s find out how AI in medical imaging can help to diagnose and cure the COVID-19 like a deadly disease.

Also Read: How AI is Used in Healthcare to Control the Coronavirus Disease

The Role of AI in Radiology

AI in radiology works like an artificial mind detecting the disease with an acceptable level of accuracy. And AI-enabled machines or medical systems not only can detect the diseases but can also suggest the medicines as per the patient’s biological conditions and types of syndromes evident at the initial stage of diagnosis by the doctors or medical attendants.

Also Read: How Does AI Work in Radiology: Applications and Use Cases

And when AI-enabled devices or computer systems are trained with a huge quantity of annotated medical imaging datasets, with the right algorithm, it can diagnosis such disease without the help of radiologists. Similarly, to avoid human contacts, AI in radiology can be used to diagnose COVID-19 like a deadly disease with a high level of accuracy.       

Earlier in radiology practice, medical imaging analysis specialist doctors or you can say radiologists visually assess 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 quantitative, rather than qualitative, assessments of radiographic characteristics.

Epidemiology and Artificial Intelligence

Similarly, AI in epidemiology can help doctors and medical experts before the outbreak of deadly disease to minimize its impact on the people. But 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.

Also Read: How AI Can Predict Coronavirus like Epidemic Before it Outbreaks

And the hosting of such images at one place along with annotation and analysis framework will enable researchers to understand epidemiological trends and to generate new AI algorithms to assist with COVID-19 disease detection, differentiation from other pneumonia and quantification of lung involvement on CT for forecast or therapy planning in advance.

Apart from medical imaging, AI in epidemiology can be implemented with various other types of other data that only big data experts or data scientists can analyze the certain trends in changing the behavior of people or other types of a sudden change in economic activities or unexpected increase in demand of specific medicines or healthcare products, etc.

AI in Medical Imaging to Diagnosis COVID-19

In the case of COVID-19 outbreak, most of the patients infected into their lungs started with pneumonia or the common cold, sneezing and throat infections with short of breathing. And the diagnosis of all such symptoms are possible with imaging technology, like X-rays, CT Scan or MRI of chest or lungs of patients which a radiologist can analyze to understand the severity of the infection.    

AI in 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 a number of AI systems for identifying COVID-19 in CT scans.

Similarly, tech giants like Yahoo and AI startups claimed they’ve 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 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 coronavirus infection.

This kind of amazing results demonstrates that the right machine learning approach using the convolutional networks model can distinguish COVID-19 from community-acquired pneumonia. And this model also scored high marks in differentiating such diseases from novel coronavirus, with the 87% sensitivity rate and 92% specificity rate.

AI Can Detect Coronavirus Symptoms Quickly & Accurately  

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 saying results 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 improve 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 can help to control the epidemic through earlier diagnoses and treatment.

Role of AI in Conducting the COVID-19 Diagnosis Process Safely

As, we know the COVID-19 virus is a highly contagious disease, hence, doctors or radiologists are also vulnerable to get infected with this deadly virus. But with the help of AI in medical imaging diagnosis, AI’s strengths find its ways and liberate the medical staff for more intimate care for the patients where human presence and interventions are indispensable and invaluable.

Actually, while handling a patient, a radiologist or technician has 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 radiologist to guide the patient through the process contact-free minimizing their risk of getting infected.

How Medical Imaging Datasets Used for COVID-19 Analysis?

To develop the AI model that can detect such disease through medical imaging analysis, a huge amount of training dataset is required. As the COVID-19 smart image-reading system has been trained using similar clinical data and aims to close this gap.

Moreover, AI in medical imaging and diagnostics can conduct a comparative analysis of multiple CT scan images of the same patient and measure the changes in infections. That helps 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 disease with early and safe detection of such disease using the 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 education resources, announcing a new initiative to build a COVID-19 Imaging Data Repository.

And this open data repositorywill compile images and correlative data from institutions, practices, andsocieties around the world to create a comprehensive source for COVID-19research and education efforts like training the new AI models.

And such data can be also used by a highly experienced radiologist to analyze and annotate the area of interest to create the medical imaging datasets for developing the more reliable AI model that can easily and timely detect such an epidemic with the best level of accuracy.

Data annotation companies are providing healthcare training data for AI and machine learning development. Actually, it is an expert in image annotation services with the next level of precision to provide high-quality training datasets for computer vision-based AI models.

For deep learning medical imaging diagnosis, such companies can be a game-changer to annotate the medical imaging datasets detecting different types of diseases done by the highly-experienced radiologist making the AI in healthcare more practical with an acceptable level of prediction results in different scenarios benefiting the humans.

Also Read: HowCan Artificial Intelligence Benefit Humans

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Artificial Intelligence

Artificial Intelligence in High-Quality Embryo Selection for IVF

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artificial intelligence embryo selection IVF

IVF treatment is becoming a common practice in today’s reality, where 12% of the world population struggle to conceive naturally. But thanks to artificial intelligence in IVF, the whole process is going to help the embryologists to select the best quality embryos for in-vitro fertilization improving the success of conception through artificial insemination.

As per the latest study published in eLife, a deep learning system was able to choose the most high-quality embryos for IVF with 90% accuracy. Compared to trained embryologists, the deep learning model performed with an accuracy of approximately 75% while the embryologists performed with an average accuracy of 67%.

As per the research stated, the average success rate of IVF is 30 percent. The treatment is also expensive, costing patients over $10,000 for each IVF cycle with many patients requiring multiple cycles in order to achieve successful pregnancy.

Risk Factors in IVF Treatment

While multiple factors determine the success of IVF cycles, the challenge of non-invasive selection of the highest available quality embryos from a patient remains one of the most important factors in achieving successful IVF outcomes.

artificial intelligence in ivf

Currently, tools available to embryologists are limited and expensive, leaving most embryologists to rely on their observational skills and expertise. As selection of quality embryo increases the pregnancy rates, that is now possible with AI.

Also Read: How Artificial Intelligence Can Predict Health Risk of Pregnancy

Researchers from Brigham and Women’s Hospital and Massachusetts General Hospital (MGH) set out to develop an assistive tool that can evaluate images captured using microscopes traditionally available at fertility centers.

artificial intelligence embryo selection

There is so much at stake for our patients with each IVF cycle. Embryologists make dozens of critical decisions that impact the success of a patient cycle. With assistance from our AI system, embryologists will be able to select the embryo that will result in a successful pregnancy better than ever before,” said co-lead author Charles Bormann, PhD, MGH IVF Laboratory director.

AI in Embryo Selection through Machine Learning

The team trained the deep learning system (sub branch of machine learning) using images of embryos captured at 113 hours post-insemination. Among 742 embryos, the AI system was 90% accurate in choosing the most high-quality embryos.

ivf machine learning
AIVF’s deep learning and computer vision algorithms applied to time-lapse videos and stills of embryo development with proprietary markers and identifiers. Image Credit

The investigators further assessed the system’s ability to distinguish among high-quality embryos with the normal number of human chromosomes and compared the system’s performance to that of trained embryologists help in healthy baby growth in the womb.

Also Read:  What Causes A Baby To Stop Growing In The Womb During Pregnancy

The results showed that the system was able to differentiate and identify embryos with the highest potential for success significantly better than 15 experienced embryologists from five different fertility centers across the US.

However, the deep learning system is meant to act only as an assistive tool for embryologists to make judgments during embryo selection but going to benefit clinical embryologists and patients. Actually, a major challenge in the field is deciding on the embryos that need to be transferred during IVF and such AI models can make right decisions. 

Machine Learning Training Data for AI Model

The research stated that deep learning model has potential to outperform human clinicians, if algorithms are trained with more qualitative healthcare training datasets. Advances in AI have promoted numerous applications that have the potential to improve standard-of-care in the different fields of medicine.

Though, few other groups use to evaluate different use cases for machine learning in assisted reproductive medicine, this approach is novel in how it used a deep learning system trained on a large dataset to make predictions based on static images.

Such findings could help the couples become parents through IVF with higher chances of conceptions with right embryos selections. And further with more improvement in training development of AI systems will be used in aiding embryologists to select the embryo with the highest implantation potential, especially amongst high-quality embryos.

Watch Video:  Future of AI in Embryo Selection for IVF

Source: Health Analytics

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How Artificial Intelligence Can Predict Health Risk of Pregnancy?

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artificial intelligence pregnancy risk

Artificial Intelligence (AI) in healthcare is going to improve the birth process of humans with better diagnosis method when baby is in mother’s womb. Yes, using the machine learning approach, now AI can help predict the pregnancy related risks.  

As per the published in the American Journal of Pathology, a machine learning model can analyze placenta slides and inform more women of their health risks in future pregnancies, leading to lower healthcare costs and better outcomes.

Placenta Complications During Pregnancy

Actually, when a baby is born, doctors sometimes examine the placenta for features that might suggest health risks in any future pregnancies. Providers analyze placentas to look for a type of blood vessel lesion called decidual vasculopathy (DV).

Placenta Complications During Pregnancy

These indicate that the mother is at risk for preeclampsia, a complication that can be fatal to both the mother and baby in any future pregnancies. Once detected, preeclampsia can be treated, so there is considerable benefit from identifying at-risk mothers before symptoms appear.

Also Read: What Causes A Baby To Stop Growing In The Womb During Pregnancy

However, although there are hundreds of blood vessels in a single slide, only one diseased vessel is needed to indicate risk. This makes examining the placenta a time-consuming process that must be performed by a specialist, so most placentas go unexamined after birth.

How Machine Learning Predict Pregnancy Risks?

Researchers said, pathologists train for years to be able to find disease in these images, but there are so many pregnancies going through the hospital system that they don’t have time to inspect every placenta with full attention and accuracy.

While on the other hand researchers trained a machine learning algorithm to recognize certain features in images of a thin slice of a placenta sample. The team showed the tool various images and indicated whether the placenta was diseased or healthy.

Because it’s difficult for a computer to look at a large picture and classify it, the team employed a novel approach through which the computer follows a series of steps to make the task more manageable.

First, the computer detects all blood vessels in an image. Each blood vessel can then be considered individually, creating similar data packets for analysis.

machine learning predict pregnancy
Image and blood vessel patches from data set: Image Source

Then, the computer can access each blood vessel and determine if it should be deemed diseased or healthy. At this phase, the algorithm also considers features of the pregnancy, such as gestational age, birth weight, and any conditions the mother might have. If there are any diseased blood vessels, then the picture is marked as diseased.

The tool achieved individual blood vessel classification rates of 94% sensitivity and 96% specificity, and an area under the curve of 0.99. While algorithm helps pathologists know which images they should focus on by scanning an image, locating blood vessels, and finding patterns of the blood vessels that identify.

The team noted that the algorithm is meant to act as a companion tool for physicians, helping them quickly and accurately assess placenta slides for enhanced patient care.

AI Assisted Pregnancy Risk Detection 

Though, this algorithm isn’t going to replace a pathologist anytime soon. The goal here is that this type of algorithm might be able to help speed up the process by flagging regions of the image where the pathologist should take a closer look.

artificial intelligence pregnancy risk

Such studies demonstrate the importance of partnerships within the healthcare sector between engineering and medicine as each brings expertise to the table that, when combined, creates novel findings that can help so many individuals.

Also Read: Artificial Intelligence in High-Quality Embryo Selection for IVF

Such useful findings have significant implications for the use of artificial intelligence in healthcare. As healthcare increasingly embraces the role of AI, it is important that doctors partner early on with computer scientists and engineers so that we can design and develop the right tools for the job to positively impact patient outcomes.

And with the high-quality healthcare training data for machine learning can further help to improve the risks level associated with pregnancies. AI companies are using the right training datasets to train such model to learn precisely and predict accurately.     

Also Read: Why Global Fertility Rates are Dropping; Population Will Fall by 2100

Source: Health Analytics

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What is Medical Image Annotation: Role in AI Medical Diagnostics

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Medical Image Annotation

AI in healthcare is becoming more prevalent with more effective computer vision-based machine learning model developments. The more training data is used with the machine learning algorithm the AI model will learn with more variations making it easier to predict the results with more accuracy in various scenarios for the healthcare sector.

And to make the training data useful and productive, the annotated medical images are used to make the disease or body aliments detectable through machines. Medical image annotation is the process used to create such data with an acceptable level of accuracy.

What is Medical Image Annotation?

Medical image annotation is the process of labeling the medical imaging data like Ultrasound, MRI, and CT Scan, etc. for machine learning training. Apart from these radiologist images, other medical records available in the text formats are also annotated to make it understandable to machines through deep learning algorithms for accurate predictions. 

Also Read: Types of Medical Diagnostic Imaging Analysis by Deep Learning AI

Medical image annotation is playing an important role in the healthcare sector, so right here we will discuss the importance and role of the medical image annotation. And what is the types of medical images can be annotated to create the training data sets for the different disease.

Role of Medical Image Annotation in AI Medical Diagnostics 

Medical image annotation is playing a big role in detecting the various types of diseases through AI-enabled devices, machines and computer systems. Actually, this process provides the real information (data) to the learning algorithms, so that model becomes user to detect such diseases when similar medical images put in front of the system.

From normal bone fracture to deadly disease like cancer, medical image annotation can detect the maladies at microscopic level with accurate predictions. Hence, you can find here the types of diseases or diagnosis performed by AI in medical imaging diagnostics, trained through set of data generated through medical image annotation.    

Diagnosis the Brain Disorders

Medical image annotation is used to diagnosis the disease including brain tumors, blood clotting, or other neurological disorders. Using the CT Scan or MRI, machine learning models can detect such diseases if well-trained with precisely annotated images.

AI in neuroimaging can be possible when brain injuries and other ailments are properly annotated and feed into the machine learning algorithm for the right prediction. Once the model, get fully trained to it can be used on the place of radiologist making with the better and more efficient medical imaging diagnosis process saving the time and efforts of the radiologist in taking other decision.

Diagnosis the Liver Problems

Liver related problems and complications diagnosed by the medical professionals using the ultrasound images or other medical imaging formats. Usually, physicians detect, characterize, and monitor diseases by assessing liver medical images visually. And in some cases, he can be biased due to his personal experiences and inaccuracy.

While medical image annotation can train the AI model to perform the quantitative assessment by recognizing imaging information automatically instead of such qualitative reasoning as more accurate and reproductive imaging diagnosis.

Detecting the Kidney Stones

Similarly, Kidney related problems like infection, stone, and other ailment affecting the functioning of the kidney. Though AI applications in kidney disease is currently no significant but right now it is mainly focused on various key aspects like Alerting systems, Diagnostic assistance, Guiding treatment, and Evaluating prognosis.

Also Read: How AI in Medical Imaging Can Help in Diagnosis of Coronavirus

And when the algorithms get the right annotated data sets of such images, the model comes capable enough to even diagnosis the possibilities if kidney failure. Apart from bounding box annotation, there are various other popular medical image annotation techniques used to annotate the images making AI possible in detecting the kidney related to various problems. 

Detection of Cancer Cells

Detecting cancers through AI-enabled machines is playing a big role in saving people from such life-threatening diseases. When cancer is not detected at the initial stage, it becomes incurable or takes extraordinary time to cure or recover from such illnesses.

Also Read: How Does AI Detect Cancer in Lung Skin Prostate Breast and Ovary

Breast cancer and prostate cancer are the most common types of cancers found in women and men respectively, globally with high death rates among both genders. But now AI models trained with medical image annotation can help machine learning models to learn from such data and predict with the condition of maladies due to cancer.  

Also Read: How Does Google AI Detect Breast Cancer Better Than Radiologists

Teeth Segmentation for Dental Analysis

Teeth or gums related problems can be better diagnosed with AI-enabled devices. Apart from teeth structure, AI in dentistry can easily detect various types of oral problems. Yes, a high-quality training data set, can help the ML algorithm recognize the patterns and store in its virtual memory to use the same patterns in the real-life.

Medical image annotation can provide high-quality training data to make the AI in Dentistry possible with quantitative and qualitative data used to train the model and accuracy will improve in machine learning for dental image analysis. 

Eye Cell Analysis

Eyes scanned through retinal images can be used to detect various problems like ocular diseases, cataracts, and other complications. All such symptoms visible in the eyes can be annotated with the right techniques to diagnosis the possible disease.   

Microscopic Cell Analysis

It is impossible to see the microscopic cells with normal human eyes, buy using the microscope it can be easily seen. And make such extremely small size cells recognizable to machines, the high-quality image annotation technique is required for right model development.

The images of these microscopic cells are enlarged on the bigger computer screen and annotated with advanced tools and techniques. And while annotating the images, the accuracy is ensured at the highest level to make sure the AI in healthcare can give precise results. Our experts can label microscopic images of cells used in the detection and analysis of diseases.

Diagnostic Imaging Analysis

Diagnostic imaging like X-ray, CT & MRI scan gives the better option to visualize the disease to find out the actual condition and provide the right treatment. Our experts in the image annotation team can generate imaging and label specific disease symptoms using diverse annotation techniques.

Medical image annotation is giving the AI in radiology a new dimension with a huge amount of label data for the right machine learning development. And for supervised machine learning, annotated images are must to train the ML algorithms for the right diagnostic imaging analysis.

Also Read: Types of Medical Diagnostic Imaging Analysis by Deep Learning AI

Medical Record Documentation

Medical image annotation also covers the various documents including texts and other files to make the data recognizable and comprehensible to the machine. Medical records contain the data of patients and their health conditions that can be used to train the machine learning models.

Annotating the medical records with text annotation and precise metadata or additional notes makes such crucial data used for machine learning development. Highly experienced annotators can label such documents with a high level of accuracy while ensuring the privacy and confidentiality of data. 

Types of Documents Annotated through Medical Image Annotation:

  • X-Rays
  • CT Scan
  • MRI
  • Ultrasound
  • DICOM
  • NIFTI
  • Videos
  • Other Images

To annotate such highly sensitive documents with acceptable levels of accuracy, and AI medical diagnostics companies need a huge amount of such data to train the AI model for the right prediction. Cogito offers the world-class medical image annotation service to annotate the medical image dataset for AI in healthcare. It can annotate the huge amount of radiology images with high-level accuracy.

Cogito offers a great platform to generate a huge amount of training data sets for AI in various industries and sectors. AI companies seeking high-quality training data for machine learning development into wide-ranging fields like healthcare, retail, automotive, agriculture, and autonomous machines can get the best quality training datasets available here at the best pricing.

This article was originally written & published for Cogito Tech

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