AI in healthcare is becoming more crucial with early detection of various diseases with better accuracy. Cancer is one the widespread deadly disease can be now detected through machine learning and AI-enabled automated machines.
Breast cancer is most common among women worldwide. However, more than 90% of women diagnosed with breast cancer at the earliest stage survive their disease for at least 5 years compared to around 15% for women diagnosed with the most advanced stage of the disease, which is now possible with AI.
Though, AI is already diagnosing breast cancer but owing to accuracy, the reliability among the doctors was less. But now Google has developed an AI system that can detect the early signs of breast cancer better than radiologists.
Research of Google Breast Cancer AI
In the research, the Google’s AI model called DeepMind AI was trained and tuned on a representative data set comprised of de-identified mammograms from more than 76,000 women in the U.K. and more than 15,000 women in the U.S.
The AI model is trained to see if it could learn to spot signs of breast cancer in the scans. The model was then evaluated on a separate de-identified data set of more than 25,000 women in the U.K. and over 3,000 women in the United States.
In this study, researchers conducted a test where Google’s AI system was pitted against six radiologists where the AI managed to outperform all six of them at accurately detecting breast cancer among these women at better accuracy.
Earlier, when a similar study is conducted in which the team first trained AI to scan X-ray images, then looked for signs of breast cancer by identifying changes in the breasts of the 28,000 women. They then checked the computer’s guesses against the women’s’ actual medical outcomes, reducing the reliability of such applications.
But now, the accuracy level has improved and reduced false negatives by 9.4% and cut down false positives by 5.7% for women in the US. In the UK, where two radiologists typically double-check the results, the model cut down false negatives by 2.7% and reduced false positives by 1.2% make AI more reliable.
How Does AI Detect Breast Cancer?
Breast cancer diagnosis is done by oncologist’s human knowledge and intuition of what major risk factors might be, such as age, family history of breast and ovarian cancer, hormonal and reproductive factors, and breast density.
While AI in breast cancer diagnosis, rather than manually identifying the patterns in a mammogram that drives future cancer, the MIT/MGH team trained a deep-learning model to deduce the patterns directly from the medical imaging data.
Video: How AI Model Improves Breast Cancer Detection on Mammograms
And using the information from more than 90,000 mammograms, the model detected patterns too subtle for the human eye to detect the cancer cells. The Google breast cancer AI algorithms are used to learn such patterns and predict.
You can check below an image showing the visualization of tumor growth and metastatic spread in breast cancer with screening to detect breast cancer early, before symptoms develop.
Actually, the demographics of the population studied by the authors are not well defined in the previous AI-based detection. As the performance of AI algorithms can be highly dependent on the population used in the training data sets.
And these training data sets are created by annotating the medical images of breast infected with cancer available in various formats like X-rays, CT Scan and MRI. A huge amount of such labeled data is used to train the AI algorithms.
There are many companies providing healthcare training data with annotated medical imaging to train the AI and ML models with accuracy.
And with the availability of such data, detection of various types of other common cancer through AI will become possible benefiting the humans saving their life from such maladies.
Also Read: How Can Artificial Intelligence Benefit Humans?
Artificial Intelligence in Robotics: How AI is Used in Robotics?
Robots were the first-known automated type machines people got to know. There was a time when robots were developed for performing specific tasks, yes such machines were earlier developed without any artificial intelligence (AI) to perform only repetitive tasks.
But now the scenarios are different, AI in getting integrated into robots to develop the advanced level of robotics that can perform multiple tasks, and also learn new things with a better perception of the environment. AI in robotics helps robots perform the crucial tasks with a human-like vision to detect or recognize the various objects.
Nowadays, robots are developed through machine learning training. A huge amount of datasets is used to train the computer vision model, so that robotics can recognize the various objects and carry out the actions accordingly with right results.
And, further, day-by-day, with more quality and precise machine learning processes, robotics performance is getting improved. So, right here we are discussing the machine learning in robotics and types of datasets used to train the AI model developed for robots.
How AI is Used in Robotics?
The AI in robotics not only helps to learn the model to perform certain tasks but also makes machines more intelligent to act in different scenarios. There are various functions integrated into robots like computer vision, motion control, grasping the objects, and training data to understand physical and logistical data patterns and act accordingly.
And to understand the scenarios or recognize the various objects, labeled training data is used to train the AI model through machine learning algorithms. Here, image annotation plays a key role in creating a huge amount of datasets helping the robotics to recognize and grasp different types of objects or perform the desired action in the right manner making AI successful in the robotics.
Application of Sensors in Robotics
The sensor helps the robots to sense the surroundings or perceive the visuals of the environment. Just like five key sensors of human beings, combinations of various sensing technologies are used in the robotics. From motion sensors to computer vision for object detection, there are multiple sensors providing a sensing technology into changing and uncontrolled environments making the AI possible in the robotics.
Uses of Types of Sensors in Robotics:
- Time-of-flight (ToF) Optical Sensors
- Temperature and Humidity Sensors
- Ultrasonic Sensors
- Vibration Sensors
- Millimeter-wave Sensors
Nowadays a wide range of increasingly more sophisticated and accurate similar sensors, combined with systems that can fuse all of this sensor data together is empowering robots to have increasingly good perception and awareness for the right actions in real-life.
Application of Machine Learning in Robotics
Basically, machine learning is the process of training an AI model to make it intelligent enough to perform specific tasks or some varied actions. And to feed the ML algorithms, a set of data is used at a large scale to make sure AI models like robotics can perform precisely. As much as training data will be used to train the model, the accuracy would be at the best level.
In robotics, it is trained to recognize the objects, with the capability to grasp or hold the same object and ability to move from one location to another location. Machine learning mainly helps to recognize the wide-ranging objects visible in different shapes, sizes and various scenarios.
And the machine learning process keeping running if robots detect new objects, it can make the new category to detect such objects if visible again in the near future. However, there are different disciplines of teaching a robot through machine learning. And deep learning is also used to train such models with high-quality training data for a more precise machine learning process.
APPLICATION OF AI IN ROBOTICS
AI in robotics makes such machines more efficient with self-learning ability to recognize the new objects. However, currently, robotics are used at the industrial purpose and in various other fields to perform the various actions with the desired accuracy at higher efficiency, and better than humans.
Video: Most Advance AI Robots
From handling the carton boxes at warehouses, robotics is performing the unbelievable actions making certain tasks easier. Right here we will discuss the application of AI robotics in various fields with types of training data used to train such AI models.
Robotics in Healthcare
Robotics in healthcare are now playing a big role in providing an automated solution to medicine and other divisions in the industry. AI companies are now using big data and other useful data from the healthcare industry to train robots for different purposes.
From medical supplies, to sanitization, disinfection and performing the remote surgeries, AI in robotics making such machines become more intelligent learned from the data and performs various crucial tasks without the help of humans.
Robotics in Agriculture
In the agriculture sector, automation is helping farmers to improve crop yield and boost productivity. And robotics is playing a big role in the cultivation and harvesting the crops with precise detection of plants, vegetables, fruits, and other unwanted floras. In agriculture AI robots can perform the fruits or vegetable plucking, spraying the pesticides, and monitor the health conditions of plants.
Robotics in Automotive
The automobile industry moved to the automation that leads to fully-automated assembly lines to assemble the vehicles. Except for a few important tasks, there are many processes performed by robotics to develop cars reducing the cost of manufacturing. Usually, robotics is specially trained to perform certain actions with better accuracy and efficiency.
Robotics at Warehouses
Warehouse needs manpower to manage the huge amount of inventory kept by mainly eCommerce companies to deliver the products to their customers or move from location to another location. Robotics is trained to handle such inventories with the capability to carefully carry from one place to another place reducing the human workforce in performing such repetitive tasks.
Robotics at Supply Chain
Just like inventory handling at warehouses, Robotics at logistics and supply chain plays a crucial role in moving the items transported by the logistic companies. AI model for robotics gets trained through computer vision technology to detect various objects. Such robotics can pick the boxes and kept at the desired place or load and unload the same from the vehicle at faster speed with accuracy.
Training Data for Robotics
As you already know a huge amount of training data is required to develop such robots. And such data contains the images of annotated objects that help machine learning algorithms learn and recognize the similar objects when visible in the real-life.
And to generate a huge amount of such training data, image annotation techniques are used to annotate the different objects to make them recognizable to machines. And Anolytics provides the one-stop data annotation solution to AI companies to render high-quality training data sets for machine learning-based model development.
Artificial Intelligence in High-Quality Embryo Selection for 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.
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.
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.
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.
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.
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
How Artificial Intelligence Can Predict Health Risk of Pregnancy?
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).
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.
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.
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.
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.
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.
Source: Health Analytics
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