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Indian Origin Researcher part of Team that developed Automated Robotic Drill, will perform Surgery in 2.5 minutes

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Doctors performing surgery on a patient., Wikimedia
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New York, May 1, 2017: A computer-driven automated drill that could perform a type of complex cranial surgery 50 times faster — decreasing the operating time from two hours to 2.5 minutes — has been developed by researchers, including one of the Indian-origin.

A translabyrinthine surgery is performed to expose slow-growing, benign tumours that form around the auditory nerves.

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For such complex surgeries, surgeons typically use hand drills to make intricate openings, adding hours to a procedure and may also increase the risks of loss of facial movement.

However, the new automated machine replaces hand drills to produce fast, clean, and safe cuts, reducing the time the wound is open and the patient is anesthetised, thereby decreasing the incidence of infection, human error, and surgical cost.

“I was interested in developing a low-cost drill that could do a lot of the grunt work to reduce surgeon fatigue,” said A.K. Balaji, Associate Professor at the University of Utah in the US.

The drill, which could play a pivotal role in future surgical procedures like hip implants, was developed from scratch to meet the needs of the neurosurgical unit, as well as developed software that sets a safe cutting path, the researchers said in the paper reported in the journal Neurosurgical Focus.

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First, the patient is imaged using a CT scan to gather bone data and identify the exact location of sensitive structures, such as nerves and major veins and arteries that must be avoided. Surgeons use this information to programme the cutting path of the drill.

In addition, the surgeon can programme safety barriers along the cutting path within 1 mm of sensitive structures.

If the drill gets too close to the facial nerve and irritation is monitored during surgery, the drill automatically turns off. (IANS)

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With the aid of Twitter and AI, researchers to develop flood warning system

In a study, published in the journal Computers & Geosciences, the researchers showed how AI can be used to extract data from Twitter and crowdsourced information from mobile phone apps to build up hyper-resolution monitoring of urban flooding.

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AI can play a key role in future flood warning and monitoring systems
AI can play a key role in future flood warning and monitoring systems

London, Dec 26: Researchers are combining Twitter, citizen science and artificial intelligence (AI) techniques to develop an early-warning system for flood-prone communities in urban areas.

In a study, published in the journal Computers & Geosciences, the researchers showed how AI can be used to extract data from Twitter and crowdsourced information from mobile phone apps to build up hyper-resolution monitoring of urban flooding.

“By combining social media, citizen science and artificial intelligence in urban flooding research, we hope to generate accurate predictions and provide warnings days in advance,” said Roger Wang from University of Dundee in Britain.

Urban flooding is difficult to monitor due to complexities in data collection and processing.

This prevents detailed risk analysis, flooding control and the validation of numerical models.

The research team set about trying to solve this problem by exploring how the latest AI technology can be used to mine social media and apps for the data that users provide.

They found that social media and crowdsourcing can be used to complement datasets based on traditional remote sensing and witness reports.

Applying these methods in case studies, they found them to be genuinely informative and that AI can play a key role in future flood warning and monitoring systems.

“The present recording systems — remote satellite sensors, a local sensor network, witness statements and insurance reports — all have their disadvantages. Therefore, we were forced to think outside the box and one of the things that occurred to us was how Twitter users provide real-time commentary on floods,” Wang said.

“A tweet can be very informative in terms of flooding data. Key words were our first filter, then we used natural language processing to find out more about severity, location and other information,” Wang said.

The researchers applied computer vision techniques to the data collected from MyCoast, a crowdsourcing app, to automatically identify scenes of flooding from the images that users post.

“We found these big data-based flood monitoring approaches can definitely complement the existing means of data collection and demonstrate great promise for improving monitoring and warnings in future,” Wang said.

Twitter data was streamed over a one-month period in 2015, with the filtering keywords of “flood”, “inundation”, “dam”, “dike”, and “levee”. More than 7,500 tweets were analysed over this time.

“We have reached the point of 70 per cent accuracy and we are using the thousands of images available on MyCoast to further improve this,” Wang said.