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 […]
Join ApplySci at the 12th Wearable Tech + Digital Health + Neurotech Boston conference on November 14, 2019 at Harvard Medical School featuring talks by Brad Ringeisen, DARPA – Joe Wang, UCSD – Carlos Pena, FDA – George Church, Harvard – Diane Chan, MIT – Giovanni Traverso, Harvard | Brigham & Womens – Anupam Goel, UnitedHealthcare – Nathan Intrator, […]
MIT CSAIL professor Regina Barzilay and Harvard/MGH professor Constance Lehman have developed a deep learning model that can predict breast cancer, from a mammogram, up to five years in the future. The model learned subtle breast tissue patterns that lead to malignant tumors from mammograms and known outcomes of 90,000 MGH patients. The goal is […]
Xia Huimin and Guangzhou Women and Children’s Medical Center researchers used AI to read 1.36 million pediatric health records, and diagnosed disease as accurately as doctors, according to a recent study. Common childhood diseases were detected after processing symptoms, medical history and other clinical data from this massive sample. The goal is the diagnosis of complex […]
In a recent study, Jae Ho Sohn and UCSD colleagues used an AI to analyze glucose-monitoring PET scans to detect early-stage Alzheimer’s disease six years before diagnosis. The algorithm was trained on PET scans from patients who were eventually diagnosed with Alzheimer’s disease, MCI, or no disorder. It was able to identify 92% of patients who […]