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otolaryngology


Better hearing through artificial intelligence

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Hearing loss happens to many of us. The US National Institutes of Health, for example, estimates that one in eight Americans aged 12 years and older has hearing loss in both ears. Twenty-five per cent of adults aged between 65 and 75, and half of those aged 75 and older, experience disabling hearing loss. According to NIH's National Institute on Deafness and Other Communication Disorders, 28.8 million US adults could benefit from using hearing aids. Those who could benefit aren't necessarily rushing to achieve better hearing.


Intelligent Pneumonia Identification from Chest X-Rays: A Systematic Literature Review

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Chest radiography is an important diagnostic tool for chest-related diseases. Medical imaging research is currently embracing the automatic detection techniques used in computer vision. Over the past decade, Deep Learning techniques have shown an enormous breakthrough in the field of medical diagnostics. Various automated systems have been proposed for the rapid detection of pneumonia on chest x-rays images Although such detection algorithms are many and varied, they have not been summarized into a review that would assist practitioners in selecting the best methods from a real-time perspective, perceiving the available datasets, and understanding the currently achieved results in this domain. After summarizing the topic, the review analyzes the usability, goodness factors, and computational complexities of the algorithms that implement these techniques.


What Cybercrime Would Look Like in 2020

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From US real estate giant inadvertently leaking 900 million records to Danish hearing aid manufacturer Demant being a victim to a 95 million US dollars hack –cybercriminals ran rampant in the last year. In the USA alone, there were ransomware attacks against 621 government agencies, schools and healthcare providers in the first nine months of 2019. Cybercrime also became much more sophisticated in the year. And this is a trend that will continue in 2020 and beyond. While the classic phishing method –where a login page tricks a user into giving their information – is still very much popular, the use of Artificial Intelligence by malicious parties is an emerging threat that cannot be ignored.


These AI-Powered Digital Health Devices Debut At CES 2020

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In a 2018 Accenture Digital Health report, 75 percent of respondents said technology played an essential role in managing their health. When it comes to artificial intelligence (AI) powered digital health and wearable devices, 72 percent said they're willing to share their wearable data with their health insurance plan. The report also found that when AI and robotics consumer interest surpassed the choices available today for virtual care. At the Consumer Electronics Show (CES) 2020 in Las Vegas, January 7-10, 2020, AI-powered digital health devices will be prevalent. For people with hearing loss or who are visually impaired, machine learning in digital health devices can open new possibilities to hear conversations more clearly or see the world around them.


These AI-Powered Digital Health Devices Debut At CES 2020

#artificialintelligence

In a 2018 Accenture Digital Health report, 75 percent of respondents said technology played an essential role in managing their health. When it comes to artificial intelligence (AI) powered digital health and wearable devices, 72 percent said they're willing to share their wearable data with their health insurance plan. The report also found that when AI and robotics consumer interest surpassed the choices available today for virtual care. At the Consumer Electronics Show (CES) 2020 in Las Vegas, January 7-10, 2020, AI-powered digital health devices will be prevalent. For people with hearing loss or who are visually impaired, machine learning in digital health devices can open new possibilities to hear conversations more clearly or see the world around them.


Researchers develop AI that reads lips from video footage

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But even state-of-the-art systems struggle to overcome ambiguities in lip movements, preventing their performance from surpassing that of audio-based speech recognition. In pursuit of a more performant system, researchers at Alibaba, Zhejiang University, and the Stevens Institute of Technology devised a method dubbed Lip by Speech (LIBS), which uses features extracted from speech recognizers to serve as complementary clues. They say it manages industry-leading accuracy on two benchmarks, besting the baseline by a margin of 7.66% and 2.75% in character error rate. LIBS and other solutions like it could help those hard of hearing to follow videos that lack subtitles. It's estimated that 466 million people in the world suffer from disabling hearing loss, or about 5% of the world's population.


Making the Subjective Objective: Machine Learning and Rhinoplasty

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A total of 100 patients ultimately met full inclusion criteria. The average post-surgical follow up for this cohort was 29 weeks (median, 14 weeks; range, 12-64 weeks). Patients ranged from 16 to 72 years old (mean, 32.75 years; median, 28.00 years; standard deviation, 12.79 years). The ranking CNN algorithm on average estimated patients preoperatively to be 0.03 years older than their actual age. The correlation coefficient between actual age and predicted preoperative age was r 0.91.



Deep Compressed Pneumonia Detection for Low-Power Embedded Devices

arXiv.org Machine Learning

Deep neural networks (DNNs) have been expanded into medical fields and triggered the revolution of some medical applications by extracting complex features and achieving high accuracy and performance, etc. On the contrast, the large-scale network brings high requirements of both memory storage and computation resource, especially for portable medical devices and other embedded systems. In this work, we first train a DNN for pneumonia detection using the dataset provided by RSNA Pneumonia Detection Challenge [4]. To overcome hardware limitation for implementing large-scale networks, we develop a systematic structured weight pruning method with filter sparsity, column sparsity and combined sparsity. Experiments show that we can achieve up to 36x compression ratio compared to the original model with 106 layers, while maintaining no accuracy degradation. We evaluate the proposed methods on an embedded low-power device, Jetson TX2, and achieve low power usage and high energy efficiency. Keywords: Pneumonia detection · YOLO · structured weight pruning.


AI takes 10 seconds to diagnose pneumonia on chest x-rays

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The system--CheXpert--was developed using 188,000 chest images by researchers at Stanford University and fine-tuned by a team at Intermountain Medical Center to identify suspected pneumonia. If implemented in an emergency department, physicians may be able to treat patients sooner--vital to those suffering from pneumonia.