An ultra-sensitive robotic'finger' that could help detect breast cancer is being developed by scientists. Experts have created a device with a sophisticated sense of touch that can take patient pulses and check for abnormal lumps. The technology could make it easier for doctors to detect diseases such as breast cancer early on, when they are more treatable. And it may also help patients feel at ease during physical examinations that can seem uncomfortable and invasive, the researchers said. While rigid robotic fingers already exist, experts have raised concerns that these devices might not be up to the delicate tasks required in a doctor's office setting.

An algorithm developed by researchers from Helmholtz Munich, the Technical University of Munich (TUM) and its University Hospital rechts der Isar, the University Hospital Bonn (UKB) and the University of Bonn is able to learn independently across different medical institutions. The key feature is that it is self-learning, meaning it does not require extensive, time-consuming findings or markings by radiologists in the MRI images. This federated algorithm was trained on more than 1,500 MRI scans of healthy study participants from four institutions while maintaining data privacy. The algorithm then was used to analyze more than 500 patient MRI scans to detect diseases such as multiple sclerosis, vascular disease, and various forms of brain tumors that the algorithm had never seen before. This opens up new possibilities for developing efficient AI-based federated algorithms that learn autonomously while protecting privacy.

Diseases like breast and skin cancer can be detected with close to 100% accuracy with the help of deep learning. In simple terms, artificial intelligence (AI) is the ability of a digital computer (or computer-controlled robot) to perform certain tasks with intelligence. AI tends to mimic human intelligence in that it relies on the ability to reason, learn from experience, and make decisions. Learning, reasoning, and problem-solving are the building blocks of artificial and human intelligence. Artificial intelligence has branches or categories such as machine learning and deep learning, which both involve the imitation of human intelligence.

A large international collaboration, led by A/Prof Xiu Ying Wang and Prof Manuel Graeber of the University of Sydney, has developed an innovative, advanced artificial intelligence (AI) application, PathoFusion, that could be used for the examination of routine tissue samples in order to identify indications of cancer. The research melds contributions from computer scientists, neuropathologists, neuosurgeons, medical oncologists and medical imaging scientists. ANSTO's Prof Richard Banati, a Professor of Medical Radiation Sciences/Medical Imaging, who studies the brain's innate immune system using advanced medical imaging techniques, is a co-author on the paper published in the journal, Cancers. "The idea behind PathoFusion was to create a novel advanced deep learning model to recognize malignant features and immune response markers, independent of human intervention, and map them simultaneously in a digital image," explained Banati. Scientists specifically designed a bifocal deep learning framework which is analogous to how a microscopist works in histopathology image analysis.

By Sumit Pandey Taoyuan City (Taiwan), Sep 20 (UNI) Even as the world awaits a COVID vaccine, Artificial intelligence (AI) can be used for detecting pneumonia caused by the pandemic which has claimed nearly a million lives globally. The dataset commonly used for this work is open source chest X-ray images from Kaggle or other open-source websites. Some of these models have reported an accuracy even greater than 98 percent, experts have said. The experts while calling for integrating the AI systems into the medical practice, said it would build a mutually-beneficial relationship between AI and Medicine. In future AI would offer greater efficiency or cost-effectiveness and Doctors (or Medical Staff) would offer AI the essential medical exposure of complex cases.

Less than one percent of available studies on the effectiveness of artificial intelligence (AI) in detecting diseases is supported by high-quality data, according to new research. A comprehensive review of scientific literature led by University of Birmingham and University Hospitals Birmingham NHS Foundation Trust found that only a handful could be considered robust enough to back up their claims. It suggested that many studies were biased in favour of machine-learning and tended to over-hype the ability of computer algorithms when comparing them to those of human healthcare professionals. It consequently found that AI was able to detect diseases from medical images with a similar level of accuracy as healthcare professionals – contrary to several studies that have suggested AI can greatly outstrip human diagnosis. The study concluded that, while machine learning held promise to aid clinical diagnosis, its true potential remained uncertain, and called for higher standards of research and reporting to improve future evaluations.

Facial recognition technology, traditionally both marketed and feared as a way to exponentially enhance surveillance capabilities, may one day become integral for producing pork. According to a report from the New York Times, major Chinese tech companies like Alibaba and JD.com are developing artificial intelligence tools to detect disease and keep track of individual pigs using facial recognition. China hopes the technology will make large farms more manageable, allowing it to consolidate and close smaller facilities. The government claims that the move would cut down on pollution. Pig facial recognition would be invaluable to precision livestock farming, an animal husbandry practice most common in European countries and China that uses tracking technologies to maximize efficiency.

Scientists are working on developing an AI system that can analyze your breath and detect diseases. Researcher Dr. Andrea Soltoggio and her team are developing an artificial intelligence system that could save tons of lives down the road: an AI system that could detect diseases, even in its early stages, including terminal illnesses. And it could all be as simple as having the AI system analyze your breath, according to the Smithsonian. A person's breath contains hundreds of volatile organic compounds, some of which can indicate certain diseases in people. Machines called gas-chromatography mass-spectrometers, also known as GC-MS, can be used to detect these compounds. The idea, then, is to use these GC-MS machines to figure out if someone has compounds in their breath that could be indicative of serious illnesses.

When did you last visit your primary care physician? During your appointment, the doctor placed a stethoscope over your chest, listening for whispers of abnormality in your heart beat. But most heart arrhythmias occur sporadically. Picture a world where your heart can be monitored continuously using a device you could purchase at a Best Buy or Target. Algorithms transform the raw data coming from your watch into diagnoses, and your doctor will be notified when a problem is detected.

Though further developments are underway, Google said on April 27 that it has successfully developed new deep learning algorithms that can detect and diagnose diabetic retinopathy, an eye disease which can lead to blindness, as well as locate breast cancer. Lily Peng, product manager of the medical imaging team at Google Research, shared how the US tech giant is using deep learning to train machines to analyze medical images and automatically detect pathological cues, be it swollen blood vessels in the eye or cancerous tumors, during a video conference with the South Korean media hosted by Google Korea. Based on the workings of the human brain, deep learning uses large artificial neural networks -- layers of interconnected nodes -- that rearrange themselves as new information comes in, allowing computers to self-learn without the need for human programming. "Artificial neural networks have been around since the 1960s. But now with more powerful computing power, we can build more layers into the system to handle more complicated tasks with high accuracy," Peng said. "In deep learning, the feature engineering is handled by the computer itself.