DEEP TECH HEALTH + NEUROTECH
Yu Takagi, Shinji Nishimoto and Osaka University colleagues have published a study which demonstrates that AI can read brain scans and re-create largely realistic versions of images a person has seen. Future applications could include enabling communication of people with paralysis, recording dreams, and understanding animal perception, among others. Additional training was used on the existing text-to-image generative […]
Google’s deep learning technology detected tuberculosis with similar accuracy to radiologists in a Radiology study. 165,174 chest radiographs from 22,284 patients in four countries were scanned. In detecting active tuberculosis, its sensitivity was higher (88 percent versus 75 percent) and its specificity was noninferior (79 percent versus 84 percent) compared to nine radiologists. Costs were […]
MIT’s Dina Katabi has developed a non-contact, neural network-based system to detect Parkinson’s disease while a person is sleeping. By assessing nocturnal breathing patterns, the series of algorithms detects, and tracks the progression of, the disease — every night, at home. A device in the bedroom emits radio signals, analyzes their reflections off the surrounding […]
The mammography screening paradigm has not changed since the 1960s. Breast screening AI company Vara, with Essen University and Memorial Sloan Kettering hospitals, published a study showing that radiologists assisted by AI are better able to screen for breast cancer. The hope is that AI systems could detect cancers that doctors miss, provide better care […]
DeepCOVID-XR is a Northwestern University developed algorithm that automatically detects the signs of COVID-19 on a basic X-ray of the lungs. The system is able to detect COVID-19 in X-rays 10 times faster than thoracic radiologists and 1% to 6% more accurately. The developers said the AI could be used to rapidly screen patients at […]
Connie Lehman — Professor, Radiology, Harvard Medical School; Chief of Breast Imaging, radiology, Massachusetts General Hospital; Co-director of AVON Breast Center, Radiology, Massachusetts General Hospital; and Director, Breast Imaging Research Center, Radiology, Massachusetts General Hospital spoke at ApplySci’s recent conference at Harvard Medical School. Click to listen to her talk on using AI to discover breast cancer 5 years […]
UT Dallas researchers Mohsen Jafarzadeh, Yonas Tadesse, and colleagues are using AI to control prosthetic hands with raw EMG signals. The real-time convolutional neural network, which does not require preprocessing, results in faster and more accurate data classification and faster hand movements. User data re-trains the system to personalize actions. Join ApplySci at the 12th Wearable Tech […]
Sebastiano Massaro at the University of Surrey, Mihaela Porumb and Leandro Pecchia at the University of Warwick, and Ernesto Iadanza at the University of Florence have developed an advanced signal processing and machine learning method to identify congestive heart failure with 100% accuracy through analysis of one raw ECG heartbeat. The CNN model was trained and […]
Fujitsu, GE Healthcare, Macquarie University and Macquarie Medical Imaging are using AI to detect and monitor brain aneurysms on scans faster and more efficiently. Fujitsu will use AI to analyze brain images generated by GE’s Revolution C scanner and an algorithm that detect abnormalities and aneurysms. The algorithm will be capable of highlighting an arterial ring […]
CompCyst is a proof-of-concept study, led by Anne Marie Lennon at Johns Hopkins, which uses AI to more accurately determine which pancreatic cysts will become cancerous. The test evaluates molecular and clinical markers in cyst fluids, and could significantly improve detection rates vs. current clinical and imaging tests. In the study, the researchers evaluated molecular profiles, […]