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Health Care Technology

Wearable Brain-Machine Interface Turns Intentions into Actions - ELE Times


A multi-institutional, international team of researchers at the Georgia Institute of Technology combined wireless soft scalp electronics and virtual reality in a BMI system that allows the user to imagine an action and wirelessly control a wheelchair or robotic arm. The major advantage of this system to the user, compared to what currently exists, is that it is soft and comfortable to wear, and doesn't have any wires. BMI systems are a rehabilitation technology that analyzes a person's brain signals and translates that neural activity into commands, turning intentions into actions. The most common non-invasive method for acquiring those signals is ElectroEncephaloGraphy, EEG, which typically requires a cumbersome electrode skull cap and a tangled web of wires. These devices generally rely heavily on gels and pastes to help maintain skin contact, require extensive set-up times, are generally inconvenient and uncomfortable to use.

Mind and Matter: Modeling the Human Brain With Machine Learning - Neuroscience News


Summary: Researchers created a new human brain model using machine learning-based optimization of required user profile information. We all like to think that we know ourselves best, but, given that our brain activity is largely governed by our subconscious mind, it is probably our brain that knows us better! While this is only a hypothesis, researchers from Japan have already proposed a content recommendation system that assumes this to be true. Essentially, such a system makes use of its user's brain signals (acquired using, say, an MRI scan) when exposed to particular content and eventually, by exploring various users and contents, builds up a general model of brain activity. "Once we obtain the'ultimate' brain model, we should be able to perfectly estimate the brain activity of a person exposed to a specific content," says Prof. Ryoichi Shinkuma from Shibaura Institute of Technology, Japan, who was a part of the team that came up with the idea.

Landmark Detection in Cardiac MRI Using a Convolutional Neural Network


"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. To develop a convolutional neural network (CNN) solution for landmark detection in cardiac MRI. This retrospective study included cine, late-gadolinium enhancement (LGE), and T1 mapping scans from two hospitals.

How AI And Digital Identity Verification Can Secure The Telehealth Age


… and when keeping that information secure, said Adam Silverman, M.D., chief medical officer for healthcare artificial intelligence (AI) service Syllable.

Liquid metal sensors and AI could help prosthetic hands to 'feel'


Each fingertip has more than 3,000 touch receptors, which largely respond to pressure. Humans rely heavily on sensation in their fingertips when manipulating an object. The lack of this sensation presents a unique challenge for individuals with upper limb amputations. While there are several high-tech, dexterous prosthetics available today--they all lack the sensation of'touch'. The absence of this sensory feedback results in objects inadvertently being dropped or crushed by a prosthetic hand.

Get started with the Redox Amazon HealthLake Connector


Amazon HealthLake is a new, HIPAA-eligible service designed to store, transform, query, and analyze health data at scale. You can bring your healthcare data into Amazon HealthLake using Fast Healthcare Interoperability Resources (FHIR) R4 APIs. If you don't have your data in FHIR R4, Amazon has collaborated with industry experts to build Amazon HealthLake connectors to help you with custom file and HL7 to FHIR R4 mappings. This post highlights one of those partners, Redox, and their Amazon HealthLake Connector. Developers at over 300 companies use the Redox platform to exchange data with more than 1,700 healthcare provider organizations.

Severely paralyzed man communicates using brain signals sent to his vocal tract


A severely paralyzed man has been able to communicate using a new type of technology that translates signals from his brain to his vocal tract directly into words that appear on a screen. Developed by researchers at UC San Francisco, the technique is a more natural way for people with speech loss to communicate than other methods we've seen to date. So far, neuroprosthetic technology has only allowed paralyzed users to type out just one letter at a time, a process that can be slow and laborious. It also tapped parts of the brain that control the arm or hand, a system that's not necessarily intuitive for the subject. The USCF system, however, uses an implant that's placed directly on the part of the brain dedicated to speech.

AI for Navigation in Robotic Assisted Surgeries by RSIP Vision


Every Robotic Assisted Surgeries (RAS) requires some level of navigation. While in open surgery the target is viewed directly, minimally invasive RAS views come from inside the body cavity, with a restricted field-of-view (FOV). Also, the surgeon's hands are occupied with the tools, whereas the camera is controlled by an assistant, adding another complication to the procedure – requiring perfect collaboration between them. Another challenge arises from anatomical and physiological differences between patients which make it difficult to accurately position surgical tools and recognize target organs. In gastroscopies or colonoscopies, the singular wide-angle view is often difficult to interpret, and objective navigational aid can be beneficial.

Which companies are leading the way for artificial intelligence in the medical sector? - Verdict Medical Devices


Koninklijke Philips NV and Medtronic Plc are leading the way for artificial intelligence investment among top medical companies according to our analysis of a range of GlobalData data. Artificial intelligence has become one of the key themes in the medical sector of late, with companies hiring for increasingly more roles, making more deals, registering more patents and mentioning it more often in company filings. These themes, of which artificial intelligence is one, are best thought of as "any issue that keeps a CEO awake at night", and by tracking and combining them, it becomes possible to ascertain which companies are leading the way on specific issues and which are dragging their heels. According to GlobalData analysis, Koninklijke Philips NV is one of the artificial intelligence leaders in a list of high-revenue companies in the medical industry, having advertised for 578 positions in artificial intelligence, made nine deals related to the field, filed 113 patents and mentioned artificial intelligence five times in company filings between January 2020 and June 2021. Our analysis classified 13 companies as Most Valuable Players – or MVPs – due to their high number of new jobs, deals, patents and company filings mentions in the field of artificial intelligence.

The tenured engineers of 2021


The School of Engineering has announced that MIT has granted tenure to eight members of its faculty in the departments of Chemical Engineering, Electrical Engineering and Computer Science, Materials Science and Engineering, Mechanical Engineering, and Nuclear Science and Engineering. "This year's newly tenured faculty are truly inspiring," says Anantha Chandrakasan, dean of the School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. "Their work as educators and scholars has shown an incredible commitment to teaching and research -- they have each had a tremendous impact in their fields and within School of Engineering community." This year's newly tenured associate professors are: Mohammad Alizadeh, in the Department of Electrical Engineering and Computer Science and the MIT Computer Science and Artificial Intelligence Laboratory, focuses his research in the areas of computer networks and systems. His research aims to improve the performance, robustness, and ease of management of future networks and cloud computing systems.