The world of genomics has made abrupt strides in the past several years, with the first CRISPR-edited babies being born just a few weeks ago. Using advanced CRISPR technology, Scientist Jiankui He'announced that twin girls with an edited gene that reduces the risk of contracting HIV "came crying into this world as healthy as any other babies a few weeks ago."' The announcement was met with great backlash, sparking'outrage from many researchers and ethicists who say implanting edited embryos to create babies is premature and exposes the children to unnecessary health risks. Opponents also fear the creation of "designer babies," children edited to enhance their intelligence, athleticism or other traits.' CRISPR technology is used in editing human genomes.
A newly developed artificial intelligence (AI) system could help expedite the diagnosis of epileptic conditions such as Dravet syndrome. The AI system was described in a study, titled "A propositional AI system for supporting epilepsy diagnosis based on the 2017 epilepsy classification: Illustrated by Dravet syndrome," in the journal Epilepsy & Behavior. Epilepsy is a broad disease category for many different conditions that involve seizures. Properly diagnosing epileptic conditions can be a challenge, especially given their different causes and symptoms. For example, mutations in the SCN1A gene are the most common cause of Dravet syndrome, but not all people with Dravet syndrome have such mutations, and SCN1A mutations can also be associated with other conditions, such as febrile seizures plus.
Some -omics tools can be more accurate, sensitive or efficient than others. Yet benchmarking is no tell-all. Inflammatory bowel disease (IBD) is a complex genetic disease that is instigated and amplified by the confluence of multiple genetic and environmental variables that perturb the immune–microbiome axis. Here the authors describe IBD as a model disease in the context of leveraging human genetics to dissect interactions in cellular and molecular pathways that regulate homeostasis of the mucosal immune system. Machine learning can tell different types of knot apart just by'looking' at them.
We know OpenTable as the restaurant reservation system, but OpenTable has revolutionized the entire restaurant industry. By compiling a comprehensive database of dates, names, places, check size and so on, OpenTable creates operational advantages for its food and beverage customers. It provides the infrastructure to manage those reservations, assign tables, recognize repeat diners and remember diner preferences. It also allows restaurants to better manage costs by staffing correctly and minimizing food waste.
Deep neural networks (DNNs) show promise in breast cancer screening, but their robustness to input perturbations must be better understood before they can be clinically implemented. There exists extensive literature on this subject in the context of natural images that can potentially be built upon. However, it cannot be assumed that conclusions about robustness will transfer from natural images to mammogram images, due to significant differences between the two image modalities. In order to determine whether conclusions will transfer, we measure the sensitivity of a radiologist-level screening mammogram image classifier to four commonly studied input perturbations that natural image classifiers are sensitive to. We find that mammogram image classifiers are also sensitive to these perturbations, which suggests that we can build on the existing literature. We also perform a detailed analysis on the effects of low-pass filtering, and find that it degrades the visibility of clinically meaningful features called microcalcifications. Since low-pass filtering removes semantically meaningful information that is predictive of breast cancer, we argue that it is undesirable for mammogram image classifiers to be invariant to it. This is in contrast to natural images, where we do not want DNNs to be sensitive to low-pass filtering due to its tendency to remove information that is human-incomprehensible.
The system can be deployed on a smartphone, achieves 91 percent top-10-accuracy in identifying over 215 different genetic syndromes, and has outperformed clinical experts in three separate experiments. The FDNA team's research paper, Identifying facial phenotypes of genetic disorders using deep learning, has been published in Nature Medicine. Using deep learning algorithms and brain-like neural networks, the Face3Gene app can predict congenital and neural developmental disorders in people through the detection of distinctive facial features in photos. Face2Gene builds on a technique the FDNA team introduced last January in the paper DeepGestalt -- Identifying Rare Genetic Syndromes Using Deep Learning. Researchers started by training an AI system to distinguish two conditions which cause distinct facial features -- Cornelia de Langs syndrome and Angelman syndrome -- from other, similar conditions.
To brave the unpredictability of epileptic seizures, three young tunisian entrepreneurs engineered a bracelet for users to monitor their own condition and most crucially, automatically contact their caregivers within seconds of a fit. Since their launch in 2017, Epilert continues to prevent epileptic fatalities and hopes to advance medical treatments by working closely with Tunisian doctors.
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A recent study by IBM Research, together with Sage Bionetworks, Kaiser Permanente Washington Health Research Institute, and the University of Washington School of Medicine, has uncovered how combining machine learning algorithms and assessments by radiologists could improve the overall accuracy of breast cancer screenings. Mammogram screenings, commonly used by radiologists for the early detection of breast cancer, according to IBM researcher Stefan Harrer, frequently rely on a radiologist's expertise to visually identify signs of cancer, which is not always accurate. "Through the current state of human interpretation of mammography images, two things happen: Misdiagnosis in terms of missing the cancer and also diagnosing cancer when it's not there," Harrer told ZDNet. "Both cases are highly undesirable -- you never want to miss a cancer when it's there, but also if you're diagnosing a cancer and it's not there, it creates enormous pressure on patients, on the healthcare system, that could be avoided. "That is exactly where we aim to improve things through the incorporation of AI (artificial intelligence) to decrease the rate of false positives, which is the diagnosis of cancer, and also to decrease missing the cancer when there is one." The research used more than 310,800 de-identified mammograms and clinical data from Kaiser Permanente Washington (KPWA) and the Karolinska Institute (KI) in Sweden. Of the combined datasets, KI contributed around 166,500 examinations from 6,800 women, of which 780 were cancer positive; while the remaining 144,200 examinations were provided by KPWA from 85,500 women, of which 941 were cancer positive. "We had hundreds of thousands of mammograms that were annotated.
Scientists who designed an artificially intelligent robot that helped children with autism boost their learning and social skills hope such technology could one day aid others with the developmental disorder. The study saw seven children with mild to moderate autism take home what is known as a socially assistive robot, named Kiwi, for a month. According to a statement by the University of Southern California where the team is based, the participants from the Los Angeles area were aged between three and seven years old, and played space-themed games with the robot almost daily. As Kiwi was fitted with machine-learning technology, it was able to provide unique feedback and instructions to the children based on their abilities. For instance, if the child got a question wrong Kiwi would give prompts to help them solve it, and tweak the difficulty levels to challenge the child appropriately.