Airport security-style technology could help doctors decide on stroke treatment

A new computer program could help doctors predict which patients might suffer potentially fatal side-effects from a key stroke treatment.

The program, which assesses brain scans using pattern recognition software similar to that used in airport security and passport control, has been developed by researchers at Imperial College London. Results of a pilot study funded by the Wellcome Trust, which used the software are published in the journal Neuroimage Clinical.

Stroke affects over 15 million people each year worldwide. Ischemic strokes are the most common and these occur when small clots interrupt the blood supply to the brain. The most effective treatment is called intravenous thrombolysis, which injects a chemical into the blood vessels to break up or ‘bust’ the clots, allowing blood to flow again.

However, because intravenous thombolysis effectively thins the blood, it can cause harmful side effects in about six per cent of patients, who suffer bleeding within the skull. This often worsens the disability and can cause death.

Clinicians attempt to identify patients most at risk of bleeding on the basis of several signs assessed from brain scans. However, these signs can often be very subtle and human judgements about their presence and severity tend to lack accuracy and reliability.

In the new study, researchers trained a computer program to recognise patterns in the brain scans that represent signs such as brain-thinning or diffuse small-vessel narrowing, in order to predict the likelihood of bleeding. They then pitted the automated pattern recognition software against radiologists’ ratings of the scans. The computer program predicted the occurrence of bleeding with 74 per cent accuracy compared to 63 per cent for the standard prognostic approach.

Dr Paul Bentley from the Department of Medicine, lead author of the study, said: “For each patient that doctors see, they have to weigh up whether the benefits of a treatment will outweigh the risks of side effects. Intravenous thrombolysis carries the risk of very severe side effects for a small proportion of patients, so having the best possible information on which to base our decisions is vital. Our new study is a pilot but it suggests that ultimately doctors might be able to use our pattern recognition software, alongside existing methods, in order to make more accurate assessments about who is most at risk and treat them accordingly. We are now planning to carry out a much larger study to more fully assess its potential.”

The research team conducted a retrospective analysis of computerized tomography (CT) scans from 116 patients. These are scans that use x-rays to produce ‘virtual slices’ of the brain. All the patients had suffered ischemic strokes and undergone intravenous thrombolysis in Charing Cross Hospital. In the sample the researchers included scans from 16 patients who had subsequently developed serious bleeding within the brain.

Without knowing the outcomes of the treatment, three independent experts examined the scans and used standard prognostic tools to predict whether patients would develop bleeding after treatment.

In parallel the computer program directly assessed and classified the patterns of the brain scans to produce its own predictions.

Researchers evaluated the performance of both approaches by comparing their predictions of bleeding with the actual experiences of the patients.

Using a statistical test the research showed the computer program predicted the occurrence of bleeding with 74 per cent accuracy compared to 63 per cent for the standard prognostic approach. 

The researchers also gave the computer a series of ‘identity parades’ by asking the software to choose which patient out of ten scans went on to suffer bleeding. The computer correctly identified the patient 56 per cent of the time while the standard approach was correct 31 per cent of the time.

The researchers are keen to explore whether their software could also be used to identify stroke patients who might be helped by intravenous thrombolysis who are not currently offered this treatment. At present only about 20 per cent of patients with strokes are treated using intravenous thrombolysis, as doctors usually exclude those with particularly severe strokes or patients who have suffered the stroke more than four and half hours before arriving at hospital. The researchers believe that their software has the potential to help doctors to identify which of those patients are at low risk of suffering side effects and hence might benefit from treatment.

Artificial intelligence lie detector

Wrongly accused and imprisoned for a crime you didn’t commit. It sounds like the plot to a generic crime thriller. However, this scenario does happen from time to time in the UK. From the Birmingham Six, falsely imprisoned for sixteen years, to the more recent case of Barri White, who was wrongly jailed for the murder of his girlfriend Rachel Manning, these situations can seem to the public like a tragic miscarriage of the criminal justice system.

However, what if you could stop these miscarriages of justice from happening? Imperial alumnus Dr James O’Shea, who graduated with a Bachelor of Science in Chemistry in 1976, has built a lie detector device called the ‘Silent Talker’ that he believes could help to improve criminal investigations.

While lie detector tests of any sort are not currently admissible evidence in British courts, Dr O’Shea believes Silent Talker could be an invaluable tool in helping law enforcement to focus their investigations.

Dr O’Shea says: “An original member of my team who helped to develop the Silent Talker was very close to the area where one of the attacks by Yorkshire Ripper took place. She took an interest in the case and found that the Ripper had been interviewed and passed over several times by the police. If the police had Silent Talker back then, it may have helped them to determine that they needed to spend a little more time on this guy, and investigate his background more closely.”

Artificially intelligent

The Silent Talker consists of a digital video camera that is hooked up to a computer. It runs a series of programs called artificial neural networks. These are computational models that take their design from animals’ central nervous systems, acting like an autonomous ‘brain’ for the device.

The computer programming in the artificial brain is a type of artificial intelligence called machine learning. It enables Silent Talker to learn and recognise patterns in data so that it can constantly adapt and reprogram itself during an interview. This enables Silent Talker to build up an overall profile of the subject to identify when someone is lying or telling the truth.

But how does it know when someone is lying? The inventors of the device claim it’s written all over your face. The camera records the subject in an interview and the artificial brain identifies non-verbal ‘micro-gestures’ on people’s faces. These are unconscious responses that Silent Talker picks up on to determine if the interviewee is lying.

Examples of micro-gestures include signs of stress, mental strain and what psychologists call ‘duping delight’. This refers to the unconscious flash of a smile at the pleasure and thrill of getting away with telling a lie. Dr O’Shea says these ‘tells’ are extremely fine-grained and exceedingly difficult for the interviewee to have any control over.

Coming to an interview near you

Dr O’Shea says the uses for such a device are numerous.

“One can imagine a near-future scenario in which your prospective employers are wearing Google Glasses, where every micro-gesture that ‘leaks’ from your face is a response that flashes by their eyes as ‘true’ or ‘false’ in real-time.”

While it does use the latest in computational techniques, Dr O’Shea says Silent Talker is not infallible. In tests to classify the micro-gestures as deceptive or non-deceptive, the Silent Talker has achieved an accuracy rate of 87 per cent.

However, this has not stopped prospective clients from clamouring for the device. Dr O’Shea and his colleagues have already been approached by security services about whether Silent Talker could be used to determine if people approaching a military checkpoint could be suicide bombers so that they can be eliminated before blowing up their target. The team’s answer has been a loud and emphatic ‘no’.

“In an ethical sense, such decisions should not be taken by a machine,” says Dr O’Shea.

Researchers demonstrate information processing using a light-based chip inspired by our brain

In a recent paper in Nature Communications, researchers from Ghent University report on a novel paradigm to do optical information processing on a chip, using techniques inspired by the way our brain works.

Neural networks have been employed in the past to solve pattern recognition problems like speech recognition or image recognition, but so far, these bio-inspired techniques have been implemented mostly in software on a traditional computer. What UGent researchers have done is implemented a small (16 nodes) neural network directly in hardware, using a silicon photonics chip. Such a chip is fabricated using the same technology as traditional computer chips, but uses light rather than electricity as the information carrier. This approach has many benefits including the potential for extremely high speeds and low power consumption.

The UGent researchers have experimentally shown that the same chip can be used for a large variety of tasks, like arbitrary calculations with memory on a bit stream or header recognition (an operation relevant in telecom networks: the header is an address indicating where the data needs to be sent). Additionally, simulations have shown that the same chip can perform a limited form of speech recognition, by recognising individual spoken digits (“one”, “two”, …).