One key way is with diagnostic and imaging tools, like MRIs and CT and PET scans. Algorithms can be trained, for instance, to accurately measure all of the lymph nodes from a cancer patient's CT scan to see if they're changing size. It's a huge job that algorithms can do much more quickly than humans. The clinician can then take the results and decide whether a therapeutic regime is working or needs to be adjusted or changed. We now also use machine-learning tools in stroke detection and classification.
Since ancient times, humans have been interested in finding systematic approaches to reasoning and logical thinking. Now, we want to make machines "think" like us and endow them with the reasoning ability that, unfortunately, we don't quite understand ourselves. But, why do we need machines that can deconstruct truths and validate reasons like we do? One of our most recent AI-related posts discusses the story of an AI system that can detect skin cancer more accurately than dermatologists. No doubt, this is a big deal in that an early diagnosis is one of the most effective methods for providing successful cancer treatments.
During the heydays of pulp sci-fi, Robert A. Heinlein penned a now forgotten short novel titled Waldo. The parable broadly speculated on how robotics and automation would eventually come to shape the lives and the landscape of the future. Almost a century later, Heinlein's work reads like a prophesy, foretelling the 21st century's rapid march towards adopting machines to do men's work. Look around and you'll find myriad examples. Robots are putting together cars on the assembly line and acting as companions for the disabled.
A new study finds that freshmen from 19 colleges in eight countries report symptoms consistent with a diagnosable psychological disorder. "While effective care is important, the number of students who need treatment for these disorders far exceeds the resources of most counseling centers, resulting in a substantial unmet need for mental health treatment among college students," said lead author Randy P. Auerbach, Ph.D., of Columbia University. "Considering that students are a key population for determining the economic success of a country, colleges must take a greater urgency in addressing this issue." For the study, Auerbach and his research team analyzed data from the World Health Organization's World Mental Health International College Student Initiative.
Prostate cancer is expected to be the leading source of new cancer for men and the second most frequent cause of death after lung cancer. It is also cancer that is very hard to detect, and small lesions can comprise just a fraction of 1% of the tissue surface. To help solve the problem, researchers from Cornell University and the Memorial Sloan Kettering Cancer Center, a cancer treatment and research institution in New York City, developed a deep learning-based approach that more accurately detects cancer. Using the center's biopsy dataset, the team developed a state-of-the-art system that can be considered clinically relevant, the researchers said. "Until recently, studies relied on datasets in the order of few hundreds of slides which are not enough to train a model that can work at scale in the clinic.
When I was in high school in the early 2000s, I spent a week of my summer vacation shadowing a pathologist at the local hospital. Every day in his basement office was basically the same; he'd focus his microscope on a slide of tissue, squinting for minutes at a time, methodically making notes about the shape of the cells, their size, their surroundings. When he had enough data points he'd make the phone call: "Squamous cell carcinoma." For decades, doctors have relied on the well-trained eyes of human pathologists to give their patients a cancer diagnosis. Now, researchers are teaching machines to do that time-intensive work in as little as a few seconds.
IMAGE: The image shows how an AI tool analyzes a slice of cancerous tissue to create a map that tells apart two lung cancer types, with squamous cell carcinoma in red,... view more A new computer program can analyze images of patients' lung tumors, specify cancer types, and even identify altered genes driving abnormal cell growth, a new study shows. Led by researchers at NYU School of Medicine and published online in Nature Medicine, the study found that a type of artificial intelligence (AI), or "machine learning" program, could distinguish with 97 percent accuracy between adenocarcinoma and squamous cell carcinoma--two lung cancer types that experienced pathologists at times struggle to parse without confirmatory tests. The AI tool was also able to determine whether abnormal versions of 6 genes linked to lung cancer--including EGFR, KRAS, and TP53--were present in cells, with an accuracy that ranged from 73 to 86 percent depending on the gene. Such genetic changes or mutations often cause the abnormal growth seen in cancer, but can also change a cell's shape and interactions with its surroundings, providing visual clues for automated analysis. Determining which genes are changed in each tumor has become vital with the increased use of targeted therapies that work only against cancer cells with specific mutations, researchers say.
SK Telecom has launched a Wi-Fi offering that supports up to 4.8Gbps in data transfer speed, the company has announced. The Wi-Fi product, dubbed T Wi-Fi AX, is based on the 802.11.ax standard and is almost four times faster than 802.11ac Wave 1, which was commercialised in 2013 and supports speeds of up to 1.3Gbps. Currently, commercially available smartphones do not have an 802.11.ax chip, so it will be restricted to a speed increase of up to 1Gbps. Flagship handsets are to be launched next year with better hardware, and eventually the 802.11.ax chip, which will then get the full speed boost, SK Telecom said.
A team of experts from IIT-Kharagpur (IIT-Kgp) and Tata Medical Centre (TMC), Kolkata, has devised a computer-assisted model they say can automatically grade breast cancer aggressiveness, even in remote settings, providing fresh impetus to AI-based medical technology in India. It also seeks to reduce human error in identifying breast cancer of various levels of aggressiveness to assist in distinguishing normal and low and higher risk malignant tumours. To do that, the team tapped into deep learning, a form of AI concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. "The idea is to assess and identify the cancer that's of high risk. This software allows accurate identification of the aggressive cancers anywhere, even in the remotest part of the country, allowing faster referral and quicker treatment for patients, irrespective of their geographical location," Sanjoy Chatterjee, senior clinical oncologist at TMC, told IANS.
It's obvious that it takes years to train doctors, especially those who handle serious and complicated medical issues – pathologists, cardiologists, dermatologists and the rest, that's why there's always a shortage of these lifesaving experts. Thanks to artificial intelligence because now, machines can be trained to help fill that shortage. In fact, already, we have AI tools that can diagnose pneumonia, fungi, depression and certain eye infections -- all with an average accuracy rate of over 92 percent. And you know what, the list is expanding further! Chinese researchers have managed to develop a new system that diagnoses prostate cancer, as accurately as pathologists do.