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Types of Medical Diagnostic Imaging Analysis by Deep Learning AI

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types of medical diagnostic imaging

Artificial intelligence (AI) and machine learning have enough potential to make various tasks in the healthcare industry possible with accurate performance. Patient’s timely disease diagnosis and the right decision is an important part of hospitals to improve the overall healthcare system.

Medical imaging is playing a vital role in diagnosing the various types of diseases among patients across the healthcare system. MRI, CT Scan, Ultrasound and X-Rays are the key medical images use to diagnosis the disease, which is usually performed manually by the specialist doctors called Radiologists.

But now, Artificial intelligence in medical diagnosis is helping radiologists to use such images and detect the maladies with an acceptable level of accuracy. And now AI is capable enough to detect the different types of critical diseases with more level of accuracy.

To make the AI model, learn and detect, similar medical images are used that are annotated by the experienced radiologist to make the affected areas recognizable to machines through computer vision. So, today we will discuss here types of medical diagnostic imaging used to train the machine learning models and what are the data annotation techniques to create such medical imaging training data for AI.

Types of Diseases Diagnosed with AI in Medical Imaging

Detecting the Neurological Abnormalities

AI in neuroimaging is now detecting brain-related injuries, blood clotting and other neurological disorders with a high level of accuracy. A team of Radiologists annotate and highlight such brain-related disorders and helping the algorithms learn from the same.

AI in neuroimaging

Once the machine learning algorithms, get fully trained to diagnose and predict the brain-related problems, it can be used on the place of radiologist making the medical imaging diagnosis process faster and more correct for timely detection of brain diseases.      

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

Semantic segmentation and polygon annotation are the leading techniques used to label the medical imaging training data for brain-related abnormalities.   

Screening of the Common Cancers

Cancer – one of the most common and deadly diseases, people are fighting to survive globally. Cancer is more life-threatening if diagnosed at last or critical stage of development. At this level, it becomes difficult for the oncologist to save the patients from dying.

ai in breast cancer diagnois

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

Breast cancer and prostate cancer are the most common types of cancers found in women and men respectively taking their lives due to lack of timely detection. In breast cancer, microcalcification is the key tissue sometimes difficult to conclusively identify as it could be either malignant or benign.

ai in prostate cancer diagnosis

And if the diagnosis was false positives it could lead to unnecessary invasive testing or treatment, while missed malignancies could result in delayed diagnoses and worse outcomes for the patients. Similarly, AI deep learning” can detect the prostate and other types of cancers with accurate results. 

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

Diagnosing the Infections in Kidney & Liver

AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. In liver medical imaging, physicians usually detect, characterize, and monitor diseases by assessing liver medical images visually.

ai in kidney liver diagnosis

However, sometimes, such visual assessment, done by experts or experienced radiologist doctors, may be biased due to personal experiences and inaccuracy. While on the other hand, AI can make a quantitative assessment by recognizing imaging information automatically instead of such qualitative reasoning as more accurate and reproductive imaging diagnosis.

Whereas, AI applications in kidney disease are currently no significant, but the potential of AI in the management of kidney disease is well recognized by clinicians, hence the role of AI in kidney disease diagnosis will improve the diagnosis process in the future.

Also Read: How AI Robotics is Used in Healthcare: Types of Medical Robotics

Acute kidney problems like severe infections and stones can be detected with an AI-enabled medical imaging process. Basically, the function of AI in kidney disease mainly focuses on four aspects: Alerting systems, Diagnostic assistance, Guiding treatment and evaluating prognosis.

And with well-organized and annotated training a machine learning models can predict the kidney failure possibilities among the patients suffering from kidney-related problems. To detect the kidney stones, bounding box annotation is used to make such ailments recognizable to machines.

Brain Tumor Detection with High Accuracy

Just like cancers, tumors are also life-threatening disease, especially if it is developed in the brain. And AI in brain tumor detection is also possible with a precise medical imaging technique. As per the various research and studies, in brain tumor detection, the AI-based diagnosis was 94.6% accurate, compared with 93.9% for the pathologist-based interpretation.

AI in brain tumor detection

Semantic segmentation technique is used to create such medical imaging data used for machine learning for brain tumor diagnosis. And as per the new studies, a novel method of combining advanced optical imaging with an artificial intelligence algorithm produces an accurate, real-time intraoperative diagnosis of brain tumors.

Machine Learning for AI in Dental Imaging Analysis

AI in dentistry for dental image analysis is playing an important role to find out the conditions of teeth helping doctors to recommend the right treatment. Using the dental image analysis, AI models can detect the teeth related problems including damaged teeth, uneven teeth structure or cavities and other maladies inside the roots of the teeth.

dental image analysis

Also Read: How Machine Learning In Dentistry Can Improve The Dental Imaging Analysis

In dentistry, the affected or damaged teeth conditions are outlined in the X-Ray images by an experienced radiologist, once such data sets get ready, it is used in ML. The ML algorithm learns from varied types of annotated dental x-rays, and learn from such source data, that is further used to detect when shown such x-rays and predict the teeth condition.       

Semantic segmentation is one of them used for detection, classification and segmentation of objects (teeth) in dentistry. Highly experienced dentist cum radiologist, examine the medical images or x-rays and annotate the affected areas with accuracy. Such a practice can create a huge amount of training data sets for machine learning in dentistry.     

Detecting the Bone Fractures and Musculoskeletal Injuries

The invisible bone fractures and other hidden musculoskeletal injuries can be fatal if undetected or untreated for a long time. Hip fractures and bone injuries in elderly persons are more critical due to reductions in mobility and associated hospitalizations.

AI in fracture detection

But medical imaging with machine learning, AI in fracture detection can diagnose the bone ruptures with the next-level of accuracy. X-ray imaging provides images of the body’s internal structures and when the fractured areas are annotated, it becomes delectable to machines through computer vision algorithm training with the huge amount of similar machine learning training data sets feed into the model.

Summing-up

AI in radiology is going to play a big role in diagnosing the various types of diseases including the critical maladies with a high level of accuracy. And further, with more improved or high-quality medical imaging data the diagnosis process and prediction accuracy will be better making the medical treatment and healthcare procedure more efficient and effective.

And Anolytics is one of the leading data annotation companies, providing the machine learning training data sets for AI developments in different industries like automotive, retail, agriculture and healthcare, etc. It is also offering a medical image dataset to train the AI models for diagnosing the various types of diseases with a high level of accuracy at every stage.

This article was originally written for anolytics.ai                 

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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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Artificial Intelligence

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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