Artificial intelligence has transformed our world drastically. And this fact is more than evident by just taking a look around. Every device or technology that is surrounding us is becoming in ways powered by artificial intelligence. Be it our smartphone, mobile applications, software, websites, smartwatches or IT services like healthcare. Artificial intelligence is penetrating every other industry and impacting it in several ways.

AI making lives easier

With the goal of making things much simpler and uncomplicated, AI along with machine learning is helping industries become more efficient and fastidious. Some of the brightest concepts of artificial intelligence that were just blossoming ideas a while back are now hardcore realities.

While this is helping organizations and institutions advance their goals in their fields, it is ultimately aiding the end customers to get better facilities, products, and services. But, as many people as you will find telling the good tales of artificial intelligence, so will you find those who are skeptical about it.

Even though artificial intelligence-powered programs and tools are helping businesses automate tasks, improve efficiencies, reduce costs and most importantly reduce the burden of an employee’s shoulders, there are people who fear it might take away jobs and reduce the aspect of human touch from a product or service.

But, as soon as the people realize that AI exists to aid them and not replace them, it would be easier to deploy the actual benefits to the customers. Take the healthcare industry as an example. The healthcare sector is a classic example where the cutting edge technology of artificial intelligence is out to the best at use.

Artificially intelligent healthcare

To begin with, it is helping the medical staff and institutions like hospitals and clinics across the world manager patient and staff more efficiently. They have a real-time hospital management system, which can basically predict the number of patients on a particular day. With historic and current statistics in the picture, AI can make this possible and help realize the hospital whether they are prepared for an upcoming situation or not.

Similarly, knowing the footfall for a particular day can help manage the staff and required tools and experts in the hospital. Better management of these would ensure a better allocation of resources in the hospital, along with better facilities for the patient.

And that’s just one aspect of artificial intelligence in the field of healthcare. Another is the diagnosis of diseases where artificial intelligence is providing to be far more comprehensive and efficient than the existing traditional methods.

Especially talking about rare lethal diseases like breast cancer, where diagnosis plays one of the most important roles. Not only does the part of diagnosis end up utilizing a lot of money but it also yields less accurate results.

Constance Lehman, MD, Ph.D., chief of the breast imaging division at Massachusetts General Hospital (MGH) and a professor of radiology at Harvard Medical School highlights the fact that our current tools to predict the future risks are simply not accurate in healthcare.

For breast cancer, the emphasis is still on late-stage diagnosis and professionals are still not screening it as comprehensively as they should. Even the patients who do get mammograms at recommended intervals are not receiving adequate and uniform care from the radiologists. 

However, connecting human intelligence to the power of clinical expertise and unparalleled data processing capabilities of machine learning like deep learning and advanced neural networks is presenting an altogether new frontier for precise and personalized medicine, diagnostics along with treatment. And once this is accomplished, it won’t be just for rare cancers but also for one in a million genetic diseases.

Moreover, using artificial intelligence and its subsidiaries to expand the healthcare industry’s capabilities for more effective screenings, reduced pain points in the care process, along with augmented clinical decision making in healthcare can help the industry save millions of dollars every year and enhance the impact of the patient’s voice in their treatment. 

Talking about breast cancer, the point is that human interpretation of images is highly subjective. Moreover, institutions don’t have enough people to read these images, and even if they are found it is difficult to maintain the highest standard of reading.

According to recent research by the MIT Technology Review and GE Healthcare, it was discovered that some radiologists designate less than 10 percent of breast tissue as dense, while other radiologists will label more than 80 percent of the mammograms in the same manner.

With deep learning in the picture, full-resolution mammograms can be utilized to accurately predict the likelihood of a woman developing breast cancer. The best part is that it is accurate across all races. The research further indicates that an algorithm trained on more than 70,000 images consistently outperformed the commonly used risk model in the industry, in spite of the fact that there was no additional patient data provided to it.

Another concrete example of the study is the research by Google, where its AI has been able to detect breast cancer better than humans. The model was able to successfully spot cancer in de-identified screening mammograms, with fewer false positives and also fewer false negatives than experts.

The study published in the Nature magazine scanned data from more than 76,000 women in the UK and 15,000 women in the US. The best part of the model was that it was able to more effectively screen for breast cancer using less information than human doctors, relying solely on X-ray images, while medical experts had access to patient’s histories and mamograms. 

Conclusion

Rare diseases like breast cancers require more comprehensive diagnosis and treatment than any other condition. The current medical challenges are huge in terms of diagnosis, treatment, and costs, but with AI in the picture not only can the level of treatment be enhanced but also many precious lives can be saved.

The post Breast cancer prediction: How Artificial Intelligence can save lives? appeared first on Artificial Intelligence.

In a ray of hope for those who have to go for breast cancer screening and even for healthy women who get false alarms during digital mammography, an Artificial Intelligence (AI)-based Google model has left radiologists behind in spotting breast cancer by just scanning the X-ray results.

Reading mammograms is a difficult task, even for experts, and can often result in both false positives and false negatives.

In turn, these inaccuracies can lead to delays in detection and treatment, unnecessary stress for patients and a higher workload for radiologists who are already in short supply, Google said in a blog post on Wednesday.

Google’s AI model spotted breast cancer in de-identified screening mammograms (where identifiable information has been removed) with greater accuracy, fewer false positives and fewer false negatives than experts.

“This sets the stage for future applications where the model could potentially support radiologists performing breast cancer screenings,” said Shravya Shetty, Technical Lead, Google Health.

Digital mammography or X-ray imaging of the breast, is the most common method to screen for breast cancer, with over 42 million exams performed each year in the US and the UK combined.

“But despite the wide usage of digital mammography, spotting and diagnosing breast cancer early remains a challenge,” said Daniel Tse, Product Manager, Google Health.

Together with colleagues at DeepMind, Cancer Research UK Imperial Centre, Northwestern University and Royal Surrey County Hospital, Google set out to see if AI could support radiologists to spot the signs of breast cancer more accurately.

The findings, published in the journal Nature, showed that AI could improve the detection of breast cancer.

Google AI model was trained and tuned on a representative data set comprised of de-identified mammograms from more than 76,000 women in the UK and more than 15,000 women in the US, 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 UK and over 3,000 women in the US.

“In this evaluation, our system produced a 5.7% reduction of false positives in the US, and a 1.2% reduction in the UK. It produced a 9.4% reduction in false negatives in the US, and a 2.7% reduction in the UK,” informed Google.

The researchers then trained the AI model only on the data from the women in the UK and then evaluated it on the data set from women in the US.

In this separate experiment, there was a 3.5% reduction in false positives and an 8.1% reduction in false negatives, “showing the model’s potential to generalize to new clinical settings while still performing at a higher level than experts”.

Notably, when making its decisions, the model received less information than human experts did.

The human experts (in line with routine practice) had access to patient histories and prior mammograms, while the model only processed the most recent anonymized mammogram with no extra information.

Despite working from these X-ray images alone, the model surpassed individual experts in accurately identifying breast cancer.

This work, said Google, is the latest strand of its research looking into detection and diagnosis of breast cancer, not just within the scope of radiology, but also pathology.

“We’re looking forward to working with our partners in the coming years to translate our machine learning research into tools that benefit clinicians and patients,” said the tech giant.

The post Google AI model beats humans in detecting breast cancer appeared first on Artificial Intelligence.

CHICAGO—A Google artificial intelligence system proved as good as expert radiologists at detecting which women had breast cancer based on screening mammograms and showed promise at reducing errors, researchers in the United States and Britain reported.

The study, published in the journal Nature on Wednesday, is the latest to show that artificial intelligence (AI) has the potential to improve the accuracy of screening for breast cancer, which affects one in eight women globally.

Radiologists miss about 20 percent of breast cancers in mammograms, the American Cancer Society says, and half of all women who get the screenings for over 10 years have a false-positive result.

The findings of the study, developed with Alphabet Inc’s DeepMind AI unit, which merged with Google Health in September, represent a significant advance in the potential for the early detection of breast cancer, Mozziyar Etemadi, one of its co-authors from Northwestern Medicine in Chicago, said.

The team, which included researchers at Imperial College London and Britain’s National Health Service, trained the system to identify breast cancers on tens of thousands of mammograms.

They then compared the system’s performance with the actual results from a set of 25,856 mammograms in the United Kingdom and 3,097 from the United States.

The study showed the AI system could identify cancers with a similar degree of accuracy to expert radiologists while reducing the number of false-positive results by 5.7 percent in the United States-based group and by 1.2 percent in the British-based group.

It also cut the number of false negatives, where tests are wrongly classified as normal, by 9.4 percent in the United States group, and by 2.7 percent in the British group.

These differences reflect how mammograms are read. In the United States, only one radiologist reads the results, and the tests are done every one to two years. In Britain, the tests are done every three years, and two radiologists read each. When they disagree, a third is consulted.

Subtle Cues

In a separate test, the group pitted the AI system against six radiologists and found it outperformed them at accurately detecting breast cancers.

Connie Lehman, chief of the breast imaging department at Harvard’s Massachusetts General Hospital, said the results are in line with findings from several groups using AI to improve cancer detection in mammograms, including her own work.

The notion of using computers to improve cancer diagnostics is decades old, and computer-aided detection (CAD) systems are commonplace in mammography clinics. Yet, CAD programs have not improved performance in clinical practice.

The issue, Lehman said, is that current CAD programs were trained to identify things human radiologists can see, whereas, with AI, computers learn to spot cancers based on the actual results of thousands of mammograms.

This has the potential to “exceed human capacity to identify subtle cues that the human eye and brain aren’t able to perceive,” Lehman added.

Although computers have not been “super helpful” so far, “what we’ve shown at least in tens of thousands of mammograms is the tool can actually make a very well-informed decision,” Etemadi said.

The study has some limitations. Most of the tests were done using the same type of imaging equipment, and the U.S. group contained a lot of patients with confirmed breast cancers.

Crucially, the team has yet to show the tool improves patient care, said Dr. Lisa Watanabe, chief medical officer of CureMetrix, whose AI mammogram program won United States approval last year.

“AI software is only helpful if it actually moves the dial for the radiologist,” she said.

Etemadi agreed that those studies are needed, as is regulatory approval, a process that could take several years.

The post Study Finds Google AI System Could Improve Breast Cancer Detection appeared first on Artificial Intelligence.

June 12, 2019 – Using deep learning technology, researchers from Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital (MGH) were able to predict women’s future risk of breast cancer development more accurately than when they used traditional methods, according to a study published in Radiology.

Current models use factors like genetics and family history to predict risk, but these tools often fall short. Breast density is an independent risk factor for predicting breast cancer risk, but it’s based on subjective assessment that can vary among radiologists.

Researchers developed a deep learning model that could standardize and automate breast density measurements.

“There’s much more information in a mammogram than just the four categories of breast density,” said study lead author Adam Yala, PhD candidate at MIT in Cambridge, Mass. “By using the deep learning model, we learn subtle cues that are indicative of future cancer.”

The team compared three different risk assessment models. The first used traditional risk factors, and the second used deep learning that evaluated the mammogram alone. The third was a hybrid method that used both the mammogram and traditional risk factors into the deep learning model.

Researchers trained and tested the models on nearly 90,000 screening mammograms from about 40,000 women and found that both deep learning models performed with greater accuracy than the traditional model.

When using the deep learning models to predict women’s risk based on breast density, the team found that patients with non-dense breasts and model-assessed high risk had 3.9 times the cancer incidence of patients with dense breasts and model-assessed low risk. These advantages held across different subgroups of women.

“Unlike traditional models, our deep learning model performs equally well across diverse races, ages and family histories,” said Regina Barzilay, PhD, an AI expert and professor at MIT. “Until now, African-American women were at a distinct disadvantage in having accurate risk assessment of future breast cancer. Our AI model has changed that.”

At MGH, clinicians are already using artificial intelligence to assist with breast density measurements. Researchers are tracking its performance in the clinic and working to refine how they communicate risk information to women and their primary care physicians.

“A missing element to support more effective, more personalized screening programs has been risk assessment tools that are easy to implement and that work across the full diversity of women whom we serve,” said Constance Lehman, MD, PhD, chief of breast imaging at MGH and professor of radiology at Harvard Medical School.

“We are thrilled with our results and eager to work closely with our health care systems, our providers and, most importantly, our patients to incorporate this discovery into improved outcomes for all women.”

Deep learning has proven itself to be a reliable support tool for cancer care. In 2018, a team at Google developed a deep learning tool that could detect metastasized breast cancer with 99 percent accuracy.

Researchers at Case Western Reserve University also built a model that achieved 100 percent accuracy when identifying invasive forms of breast cancer in pathology images.

“If the network can tell which patients have cancer and which do not, this technology can serve as triage for the pathologist, freeing their time to concentrate on the cancer patients,” Anant Madabushi, a biomedical engineering professor at Case Western Reserve and co-author of the study, said at the time.

“To put this in perspective, the machine could do the analysis during ‘off hours,’ possibly running the analysis during the night and providing the results ready for review by the pathologist when she/he were to come into the office in the morning.”

The research from MGH and MIT builds on these efforts, and further shows the potential for deep learning to transform cancer care and diagnosis.

“There’s a very large amount of information in a full-resolution mammogram that breast cancer risk models have not been able to use until recently,” Yala said. “Using deep learning, we can learn to leverage that information directly from the data and create models that are significantly more accurate across diverse populations.”

The post Deep Learning Predicts Women’s Future Risk of Breast Cancer appeared first on Artificial Intelligence.

Every year 40,000 women die from breast cancer in the U.S. alone. When cancers are found early, they can often be cured. Mammograms are the best test available, but they’re still imperfect and often result in false positive results that can lead to unnecessary biopsies and surgeries.

One common cause of false positives are so-called “high-risk” lesions that appear suspicious on mammograms and have abnormal cells when tested by needle biopsy. In this case, the patient typically undergoes surgery to have the lesion removed; however, the lesions turn out to be benign at surgery 90 percent of the time. This means that every year thousands of women go through painful, expensive, scar-inducing surgeries that weren’t even necessary.

How, then, can unnecessary surgeries be eliminated while still maintaining the important role of mammography in cancer detection? Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts General Hospital, and Harvard Medical School believe that the answer is to turn to artificial intelligence (AI).

As a first project to apply AI to improving detection and diagnosis, the teams collaborated to develop an AI system that uses machine learning to predict if a high-risk lesion identified on needle biopsy after a mammogram will upgrade to cancer at surgery.

When tested on 335 high-risk lesions, the model correctly diagnosed 97 percent of the breast cancers as malignant and reduced the number of benign surgeries by more than 30 percent compared to existing approaches.

“Because diagnostic tools are so inexact, there is an understandable tendency for doctors to over-screen for breast cancer,” says Regina Barzilay, MIT’s Delta Electronics Professor of Electrical Engineering and Computer Science and a breast cancer survivor herself. “When there’s this much uncertainty in data, machine learning is exactly the tool that we need to improve detection and prevent over-treatment.”

Trained on information about more than 600 existing high-risk lesions, the model looks for patterns among many different data elements that include demographics, family history, past biopsies, and pathology reports.

“To our knowledge, this is the first study to apply machine learning to the task of distinguishing high-risk lesions that need surgery from those that don’t,” says collaborator Constance Lehman, professor at Harvard Medical School and chief of the Breast Imaging Division at MGH’s Department of Radiology. “We believe this could support women to make more informed decisions about their treatment, and that we could provide more targeted approaches to health care in general.”

A recent MacArthur “genius grant” recipient, Barzilay is a co-author of a new journal article describing the results, co-written with Lehman and Manisha Bahl of MGH, as well as CSAIL graduate students Nicholas Locascio, Adam Yedidia, and Lili Yu. The article was published today in the medical journal Radiology.

How it works

When a mammogram detects a suspicious lesion, a needle biopsy is performed to determine if it is cancer. Roughly 70 percent of the lesions are benign, 20 percent are malignant, and 10 percent are high-risk lesions.

Doctors manage high-risk lesions in different ways. Some do surgery in all cases, while others perform surgery only for lesions that have higher cancer rates, such as “atypical ductal hyperplasia” (ADH) or a “lobular carcinoma in situ” (LCIS).

The first approach requires that the patient undergo a painful, time-consuming, and expensive surgery that is usually unnecessary; the second approach is imprecise and could result in missing cancers in high-risk lesions other than ADH and LCIS.

“The vast majority of patients with high-risk lesions do not have cancer, and we’re trying to find the few that do,” says Bahl, a fellow doctor at MGH’s Department of Radiology. “In a scenario like this there’s always a risk that when you try to increase the number of cancers you can identify, you’ll also increase the number of false positives you find.”

Using a method known as a “random-forest classifier,” the team’s model resulted in fewer unnecessary surgeries compared to the strategy of always doing surgery, while also being able to diagnose more cancerous lesions than the strategy of only doing surgery on traditional “high-risk lesions.” (Specifically, the new model diagnosed 97 percent of cancers compared to 79 percent.)

“This work highlights an example of using cutting-edge machine learning technology to avoid unnecessary surgery,” says Marc Kohli, director of clinical informatics in the Department of Radiology and Biomedical Imaging at the University of California at San Francisco. “This is the first step toward the medical community embracing machine learning as a way to identify patterns and trends that are otherwise invisible to humans.”

Lehman says that MGH radiologists will begin incorporating the model into their clinical practice over the next year.

“In the past we might have recommended that all high-risk lesions be surgically excised,” Lehman says. “But now, if the model determines that the lesion has a very low chance of being cancerous in a specific patient, we can have a more informed discussion with our patient about her options. It may be reasonable for some patients to have their lesions followed with imaging rather than surgically excised.”

The team says that they are still working to further hone the model.

“In future work we hope to incorporate the actual images from the mammograms and images of the pathology slides, as well as more extensive patient information from medical records,” says Bahl.

Moving forward, the model could also easily be tweaked to be applied to other kinds of cancer and even other diseases entirely.

“A model like this will work anytime you have lots of different factors that correlate with a specific outcome,” says Barzilay. “It hopefully will enable us to start to go beyond a one-size-fits-all approach to medical diagnosis.”

The post Using artificial intelligence to improve early breast cancer detection appeared first on Artificial Intelligence.