Concussion fears for NRL stars

Consistent concussions causing health problems for players in their life after football is a ‘‘scary’’ prospect for the current crop of NRL stars, according to Roosters co-captain Jake Friend.

Head knocks in the NRL have been thrown into the spotlight this week after Andrew Johns revealed his recently diagnosed frontal lobe epilepsy may have been brought on by concussions suffered during his career.

Johns has since said he doesn’t blame his glittering NRL career for the health problems he has suffered in retirement.

But that admission from the game’s eighth Immortal doesn’t stop the prospect of potential longterm health effects from playing on the mind of the current crop of NRL players.

Friend was sporting a pair of battle scars on his face suffered during the fervent loss to the Rabbitohs when he was asked about concussions.

‘‘It’s a scary thing and a bit of an unknown thing,’’ the Roosters hooker said. ‘‘We’ve seen in the NFL, the stuff that has happened over there. But everyone is different as well. You don’t know how serious each individual head knock has been and the other factors away from footy.

‘‘For me, it’s something I’m wary of as a player and each individual player has a responsibility on themselves, as well as all the

Eels clubs in the NRL. They have all the right things in place for it but it comes down to the individual as well.’’

Friend’s belief that the onus must be placed on the individual player – not just the NRL or club doctors – is worth noting.

Where former NFL players have heaped blame on the league – and won hundreds of millions in court settlements – Friend believes the NRL is now taking the right precautions on the concussion front compared to when he made his NRL debut in 2008.

Those precautions include the introduction of a HIA, a concussion ‘‘spotter’’ in the Bunker and the addition of a second doctor on the sideline during matches in case two head knocks happen within minutes of one another.

Combined, they justify comments from NRL head of football P W D L F A PD Pts Graham Annesley which claimed the code is world class in its monitoring of head injuries.

But the majority of those precautions have only been introduced in recent years, another ‘‘scary’’ thought for Friend.

‘‘Even [since] when I started it is a lot more stringent, the testing,’’ he said. ‘‘We have to go off and do a HIA, all that sort of stuff now. There was none of that eight or nine years ago and I guess that is scary because it has only just come in. You never know the effects of prior head knocks or footy and what that has done to it.’’

It’s the All Blacks as you have never seen them before. Historic photos of New Zealand rugby players have been shown in a completely new light, literally, thanks to the work of Andrew Jones – a Londonbased Kiwi working in the field of artificial intelligence.

Using an algorithm to colourise old black and white photos, Jones put some pictures of former All Blacks to the test – with the results rather striking.

Originally from Matamata, Jones studied at Victoria University in Wellington and has been in London for the past decade, freelancing in analytics and data science, recently working with companies like Amazon and Sony PlayStation.

It was after reading an article on image colourisation that he thought it looked interesting and that he’d try to apply it to old All Blacks photos, as he’d not seen anyone specifically attempt that before.

Jones explained that, to a computer, an image is essentially just a bunch of pixels. In a black and white image the computer registers those pixels as a number that equates to its brightness. That number ranges anywhere from zero (completely black) to 255 (completely white), while anywhere in between those two numbers is some shade of grey.

A black and white image only has a single colour channel – grey. But in the case of a colour image, there are three colour channels representing every pixel – red, green and blue. These three channels have their own value from zero to 255, and the combination of the three values represents any one of around 16.7 million possible colours.

The problem to solve for this task, Jones said, was for each image pixel, how would he get the computer to learn what the numbers for red, green and blue channels should be, based on only knowing the numbers for the grey channel. If the computer can learn that relationship, it can be provided with any black and white image and then apply what it’s learned, with the output being its best guess at the colour

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version.

To do this in practice, Jones explained that a type of model that is very popular in artificial intelligence is used, called a neural network. It’s a mathematical model built to try and mimic how our own brains learn.

In this case, a particular type of neural network is used, called a ‘convolutional neural network’, which deals well with learning about images because it scans different parts of the image to identify different features to ‘understand’ the various parts of the picture. While we might see an eye, nose, or chin, for instance, the neural network deals with very abstract features that humans wouldn’t necessarily understand.

In order for the neural network to learn this relationship, a large number of colour images are found and for every one of those, a black and white version is also created, via any basic photo software.

The neural network is then asked to go through every picture and try to best learn how to match the black and white version to its respective colour version based on the types of features it’s discovered. This phase, Jones said, was called ‘training’, as for all the images he’s not only provided the neural network with the black and white ones, but also the ‘correct answer’.

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