ABOUT THE SPEAKER
Max Little - Applied mathematician
Max Little is a mathematician whose research includes a breakthrough technique to monitor – and potentially screen for – Parkinson's disease through simple voice recordings.

Why you should listen

Max Little is an applied mathematician whose goal is to "see connections between subjects, not boundaries … to see how things are related, not how they are different." He has a background in applied mathematics, statistics, signal processing and computational engineering, and his work has been applied across disciplines like biomedicine, extreme rainfall analysis and forecasting, biophysical signal processing, and hydrogeomorphology and open channel flow measurement. Little is best known for his work on the Parkinson's Voice Initiative, in which he and his team developed a cheap and simple tool that uses precise voice analysis software to detect Parkinson's with 99 percent accuracy. Little is a TEDGlobal 2012 Fellow and a Wellcome Trust-MIT Postdoctoral Research Fellow.

More profile about the speaker
Max Little | Speaker | TED.com
TEDGlobal 2012

Max Little: A test for Parkinson's with a phone call

Max Little: Test za Parkinsonovu bolest putem telefonskog poziva

Filmed:
1,296,740 views

Parkinsonova bolest pogađa 6,3 milijuna ljudi diljem svijeta i uzrokuje slabost i tremor. No, ne postoji nikakav objektivan način za rano otkrivanje. Primijenjeni matematičar i TED Fellow Max Little testira jeftino i jednostavno pomagalo koje je u pokusima uspjelo otkriti Parkinsonovu bolest s točnošću od 99% – i to jednim pozivom od 30 sekundi.
- Applied mathematician
Max Little is a mathematician whose research includes a breakthrough technique to monitor – and potentially screen for – Parkinson's disease through simple voice recordings. Full bio

Double-click the English transcript below to play the video.

00:16
So, well, I do appliedprimijenjen mathmatematika,
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Bavim se primijenjenom matematikom,
00:18
and this is a peculiarosebujan problemproblem
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a problem svojstven
00:20
for anyonebilo tko who does appliedprimijenjen mathmatematika, is that
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svima koji se bave primijenjenom matematikom
00:22
we are like managementupravljanje consultantskonzultanti.
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jest da smo poput savjetnika za poslovanje.
00:24
No one knowszna what the hellpakao we do.
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Nitko ne zna što zapravo radimo.
00:26
So I am going to give you some -- attemptpokušaj todaydanas
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Ja ću vam danas pokušati
00:28
to try and explainobjasniti to you what I do.
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objasniti što radim.
00:30
So, dancingples is one of the mostnajviše humanljudski of activitiesdjelatnost.
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Ples je jedna od najljudskijih aktivnosti.
00:34
We delightzadovoljstvo at balletbalet virtuososvirtuoza and tapslavina dancersplesači
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Oduševljavaju nas baletni virtuozi i plesači stepa
00:37
you will see laterkasnije on.
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koje ćete vidjeti kasnije.
00:38
Now, balletbalet requirestraži an extraordinaryizvanredan levelnivo of expertiseekspertiza
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Balet zahtijeva izvanrednu razinu vještine
00:41
and a highvisok levelnivo of skillvještina,
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i visoku razinu umijeća
00:44
and probablyvjerojatno a levelnivo of initialpočetni suitabilityprikladnost
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i vjerojatno neku razinu početne prikladnosti
00:47
that maysvibanj well have a geneticgenetski componentsastavni dio to it.
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koja bi mogla imati genetski element.
00:48
Now, sadlyNažalost, neurologicalneurološki disordersporemećaji suchtakav as Parkinson'sParkinsonove diseasebolest
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Nažalost, neurološki poremećaji,
kao što je Parkinsonova bolest,
00:52
graduallypostepeno destroyuništiti this extraordinaryizvanredan abilitysposobnost,
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postupno uništavaju ovu izvranrednu sposobnost.
00:54
as it is doing to my friendprijatelj JanJan StriplingMladić, who was
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To čine i mom prijatelju Janu Striplingu, koji je
00:56
a virtuosovirtuoz balletbalet dancerplesač in his time.
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u svoje vrijeme bio virtuozni baletan.
00:59
So great progressnapredak and treatmentliječenje has been madenapravljen over the yearsgodina.
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Velik napredak i tretman
postignuti su tijekom godina.
01:02
HoweverMeđutim, there are 6.3 millionmilijuna people worldwideširom svijeta
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Međutim, 6,3 milijuna ljudi diljem svijeta
01:05
who have the diseasebolest, and they have to liveživjeti with
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ima ovu bolest i mora živjeti
01:09
incurableneizlječivo weaknessslabost, tremortremor, rigiditykrutost
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s neizlječivom slabošću, tremorom, ukočenošću
01:11
and the other symptomssimptomi that go alonguz with the diseasebolest,
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i ostalim simptomima te bolesti.
01:13
so what we need are objectivecilj toolsalat
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Stoga trebamo objektivne instrumente
01:15
to detectotkriti the diseasebolest before it's too latekasno.
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za otkrivanje bolesti dok nije prekasno.
01:19
We need to be ableu stanju to measuremjera progressionprogresija objectivelyobjektivno,
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Moramo moći objektivno mjeriti razvoj bolesti,
01:21
and ultimatelyna kraju, the only way we're going to know
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i konačno, jedini način na koji ćemo znati
01:24
when we actuallyzapravo have a curelijek is when we have
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kad budemo imali lijek jest kad bude postojala
01:26
an objectivecilj measuremjera that can answerodgovor that for sure.
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objektivna mjera koja će na to
moći sa sigurnošću odgovoriti.
01:30
But frustratinglyfrustrirajuće, with Parkinson'sParkinsonove diseasebolest
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Frustrira to što kod Parkinsonove bolesti
01:33
and other movementpokret disordersporemećaji, there are no biomarkersbiomarkeri,
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i ostalih poremećaja kretanja ne postoje biomarkeri,
01:35
so there's no simplejednostavan bloodkrv testtest that you can do,
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stoga nema jednostavnog krvnog nalaza
koji biste mogli napraviti,
01:37
and the bestnajbolje that we have is like
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i najbolje što imamo jest
01:39
this 20-minute-minuta neurologistneurolog testtest.
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20-minutni neurološki test.
01:41
You have to go to the clinicKlinika to do it. It's very, very costlyskup,
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Da biste ga napravili, morate
otići u bolnicu. Veoma je skup,
01:44
and that meanssredstva that, outsideizvan the clinicalklinički trialsispitivanja,
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što znači da se osim u kliničkim ispitivanjima
01:47
it's just never doneučinio. It's never doneučinio.
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nikad ne obavlja. Nikad se ne obavlja.
01:49
But what if patientspacijenti could do this testtest at home?
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No, što kad bi pacijenti mogli
ovaj test obaviti kod kuće?
01:52
Now, that would actuallyzapravo saveuštedjeti on a difficulttežak tripputovanje to the clinicKlinika,
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Time bi se poštedjeli teškog puta do bolnice.
01:54
and what if patientspacijenti could do that testtest themselvesse, right?
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Što kad bi pacijenti mogli sami napraviti taj test?
01:59
No expensiveskup staffosoblje time requiredpotreban.
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Bez skupog osoblja.
02:01
Takes about $300, by the way,
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Inače, testiranje u neurološkoj klinici
02:02
in the neurologist'sNeurolog je clinicKlinika to do it.
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stoji oko 300 dolara.
02:04
So what I want to proposepredložiti to you as an unconventionalnekonvencionalan way
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Stoga vam želim predložiti jedan
nekonvencionalan način
02:07
in whichkoji we can try to achievepostići this,
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na koji to možemo pokušati postići,
02:08
because, you see, in one senseosjećaj, at leastnajmanje,
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zato što smo svi mi u jednu ruku, na kraju krajeva,
02:10
we are all virtuososvirtuoza like my friendprijatelj JanJan StriplingMladić.
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virtuozi poput mog prijatelja Jana Striplinga.
02:13
So here we have a videovideo of the vibratingvibracijski vocalvokalne foldsnabora.
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Ovo je snimka koja prikazuje glasnice koje vibriraju.
02:17
Now, this is healthyzdrav and this is somebodyneko makingizrađivanje speechgovor soundszvukovi,
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Zdrave su i ovdje netko
proizvodi govorne zvukove.
02:20
and we can think of ourselvessebe as vocalvokalne balletbalet dancersplesači,
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Možemo zamisliti sebe
kao vokalne plesače baleta
02:24
because we have to coordinatekoordinirati all of these vocalvokalne organsorgana
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zato što moramo uskladiti
sve ove govorne organe
02:26
when we make soundszvukovi, and we all actuallyzapravo
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kad prozvodimo zvukove.
02:28
have the genesgeni for it. FoxPFoxP2, for exampleprimjer.
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Svi imamo gene za to. Na primjer, gen FoxP2.
02:31
And like balletbalet, it takes an extraordinaryizvanredan levelnivo of trainingtrening.
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Kao i kod baleta, potrebna je
izvanredna razina uvježbanosti.
02:33
I mean, just think how long it takes a childdijete to learnnaučiti to speakgovoriti.
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Samo se sjetite koliko treba
djetetu da nauči govoriti.
02:36
From the soundzvuk, we can actuallyzapravo trackstaza
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Pomoću zvuka zapravo možemo pratiti
02:38
the vocalvokalne foldpreklopiti positionpoložaj as it vibratesvibrira,
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položaj glasnica dok vibriraju,
02:40
and just as the limbsudova are affectedpogođeni in Parkinson'sParkinsonove,
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Parkinson zahvaća glasnice,
02:43
so too are the vocalvokalne organsorgana.
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baš kao i udove.
02:46
So on the bottomdno tracetrag, you can see an exampleprimjer of
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Na donjem ispisu možete vidjeti primjer
02:48
irregularneregularan vocalvokalne foldpreklopiti tremortremor.
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nepravilnog tremora glasnica.
02:49
We see all the sameisti symptomssimptomi.
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Vidimo iste simptome.
02:51
We see vocalvokalne tremortremor, weaknessslabost and rigiditykrutost.
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Vidimo glasovni tremor, slabost i ukočenost.
02:53
The speechgovor actuallyzapravo becomespostaje quietertiši and more breathyBreathy
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Govor zapravo postaje tiši i zadihaniji
02:56
after a while, and that's one of the exampleprimjer symptomssimptomi of it.
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nakon nekog vremena
i to je jedan primjer simptoma.
02:58
So these vocalvokalne effectsefekti can actuallyzapravo be quitedosta subtlefin,
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Ovi govorni učinci mogu biti vrlo suptilni,
03:01
in some casesslučajevi, but with any digitaldigitalni microphonemikrofon,
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u nekim slučajevima,
ali s bilo kojim digitalnim mikrofonom
03:04
and usingkoristeći precisionpreciznost voiceglas analysisanaliza softwaresoftver
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te koristeći precizni program za glasovnu analizu
03:06
in combinationkombinacija with the latestnajnoviji in machinemašina learningučenje,
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u kombinaciji s najnovijim
dostignućima u strojnom učenju,
03:09
whichkoji is very advancednapredan by now,
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koje je veoma napredovalo,
03:10
we can now quantifyizmjeriti exactlytočno where somebodyneko lieslaži
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sad možemo točno izmjeriti gdje se netko nalazi
03:13
on a continuumkontinuum betweenizmeđu healthzdravlje and diseasebolest
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na pravcu između zdravlja i bolesti,
03:16
usingkoristeći voiceglas signalssignali alonesam.
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i to koristeći samo glasovne signale.
03:19
So these voice-basedglas-temeljen teststestovi, how do they stackstog up againstprotiv
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Kakvi su ovi testovi temeljeni na glasu
u usporedbi sa
03:21
expertstručnjak clinicalklinički teststestovi? We'llMi ćemo, they're bothoba non-invasiveNe-invazivne.
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stručnim kliničkim testovima?
Pa, i jedni i drugi su neinvazivni.
03:23
The neurologist'sNeurolog je testtest is non-invasiveNe-invazivne. They bothoba use existingpostojanje infrastructureinfrastruktura.
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Neurološki test je neinvazivan.
Oba koriste postojeću infrastrukturu.
03:27
You don't have to designdizajn a wholečitav newnovi setset of hospitalsbolnice to do it.
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Ne morate graditi nove bolnice da biste to napravili.
03:30
And they're bothoba accuratetočan. Okay, but in additiondodatak,
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I oba su točna. No, uz to,
03:33
voice-basedglas-temeljen teststestovi are non-expertNe-stručnjak.
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testovi utemeljeni na glasu nestručni su.
03:36
That meanssredstva they can be self-administeredsami.
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To znači da ih možete izvesti sami.
03:38
They're high-speedvelike brzine, take about 30 secondssekundi at mostnajviše.
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Vrlo su brzi, potrebno je najviše 30-ak sekundi.
03:40
They're ultra-lowUltra-nisko costcijena, and we all know what happensdogađa se.
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Veoma su jeftini i svi znamo što se događa.
03:43
When something becomespostaje ultra-lowUltra-nisko costcijena,
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Kad nešto postane vrlo jeftino,
03:45
it becomespostaje massivelymasivno scalableskalabilan.
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također postane masovno mjerljivo.
03:47
So here are some amazingnevjerojatan goalsciljevi that I think we can dealdogovor with now.
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Ovo su neki čudesni ciljevi
s kojima se sad možemo nositi.
03:51
We can reducesmanjiti logisticallogističke difficultiespoteškoće with patientspacijenti.
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Možemo smanjiti logističke teškoće s pacijentima.
03:54
No need to go to the clinicKlinika for a routinerutina checkuppregled.
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Nema potrebe za odlaskom
u bolnicu na rutinski pregled.
03:56
We can do high-frequencyvisoke frekvencije monitoringnadgledanje to get objectivecilj datapodaci.
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Možemo provoditi česte nadzore
kako bismo dobili objektivne podatke.
03:58
We can performizvesti low-costniska cijena massmasa recruitmentzapošljavanje for clinicalklinički trialsispitivanja,
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Možemo jeftino i masovno pronalaziti
subjekte za kliničke pokuse
04:02
and we can make population-scaleStanovništvo-skale screeningprobir
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i po prvi put je izvedivo napraviti procjenu
04:04
feasibleizvodljiv for the first time.
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na razini cijelog stanovništva.
04:06
We have the opportunityprilika to startpočetak to searchtraži
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Imamo mogućnost početi tražiti
04:08
for the earlyrano biomarkersbiomarkeri of the diseasebolest before it's too latekasno.
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rane biomarkere bolesti prije nego bude prekasno.
04:12
So, takinguzimanje the first stepskoraci towardsza this todaydanas,
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Kako bismo učinili prvi korak prema tome,
04:15
we're launchingporinuće the Parkinson'sParkinsonove VoiceGlas InitiativeInicijativa.
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danas lansiramo Parkinsonovu glasovnu inicijativu.
04:17
With AculabAculab and PatientsLikeMePatientsLikeMe, we're aimings ciljem
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Zajedno s tvrtkom Aculab
i stranicom PatientsLikeMe
04:19
to recordsnimiti a very largeveliki numberbroj of voicesglasovi worldwideširom svijeta
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ciljamo na snimanje jako velikog broja
glasova diljem svijeta
04:21
to collectprikupiti enoughdovoljno datapodaci to startpočetak to tacklepribor these fourčetiri goalsciljevi.
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kako bismo skupili dovoljno podataka
da se počnemo baviti s ova četiri cilja.
04:24
We have locallokalne numbersbrojevi accessibledostupan to threetri quartersčetvrtine
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Dostupni su nam lokalni telefonski brojevi
04:26
of a billionmilijardi people on the planetplaneta.
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kojima pristup ima 750 milijuna ljudi u svijetu.
04:27
AnyoneBilo tko healthyzdrav or with Parkinson'sParkinsonove can call in, cheaplyjeftino,
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Svi zdravi, ili koji imaju Parkinsona,
mogu jeftino nazvati
04:30
and leavenapustiti recordingssnimke, a fewnekoliko centscenti eachsvaki,
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i ostaviti zapise, koji stoje po nekoliko centa,
04:32
and I'm really happysretan to announceobjaviti that we'veimamo alreadyveć hithit
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i vrlo sam sretan što mogu reći
da smo već dostigli
04:35
sixšest percentposto of our targetcilj just in eightosam hourssati.
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6% od našeg cilja, za samo osam sati.
04:38
Thank you. (ApplausePljesak)
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Hvala. (Pljesak)
04:42
(ApplausePljesak)
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(Pljesak)
04:48
TomTom RiellyRielly: So MaxMax, by takinguzimanje all these samplesuzorci of,
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Tom Rielly: Dakle, Maxe, uzimajući sve ove uzorke od,
04:52
let's say, 10,000 people,
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recimo, 10 000 ljudi,
04:55
you'llvi ćete be ableu stanju to tell who'stko je healthyzdrav and who'stko je not?
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moći ćete reći tko je zdrav, a tko nije?
04:57
What are you going to get out of those samplesuzorci?
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Što ćete dobiti iz tih uzoraka?
04:59
MaxMax Little: Yeah. Yeah. So what will happendogoditi se is that,
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Max Litlle: Da. Dakle, kako to ide.
05:01
duringza vrijeme the call you have to indicatenaznačiti whetherda li or not
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Tijekom poziva morate reći
05:03
you have the diseasebolest or not, you see. TRTR: Right.
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imate li bolest ili ne. TR: Dobro.
05:04
MLML: You see, some people maysvibanj not do it. They maysvibanj not get throughkroz it.
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ML: Vidite, neki ljudi možda to ne učine.
Možda ne prođu kroz to.
05:06
But we'lldobro get a very largeveliki sampleuzorak of datapodaci that is collectedprikupljeni
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Ali dobit ćemo jako velik uzorak podataka prikupljen
05:09
from all differentdrugačiji circumstancesokolnosti, and it's gettinguzimajući it
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iz mnogo različitih okolnosti,
05:13
in differentdrugačiji circumstancesokolnosti that matterstvar because then
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a upravo je to važno, zato što
05:14
we are looking at ironingpeglanja out the confoundingzbunjivanje factorsčimbenici,
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želimo riješiti zbunjujuće faktore
05:18
and looking for the actualstvaran markersoznake of the diseasebolest.
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i tražiti stvarne znakove bolesti.
05:20
TRTR: So you're 86 percentposto accuratetočan right now?
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TR: Dakle, trenutna točnost iznosi 86%?
05:23
MLML: It's much better than that.
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ML: Mnogo je bolje od toga.
05:24
ActuallyZapravo, my studentstudent ThanasisTanasis, I have to plugutikač him,
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Zapravo, moj učenik Thanasis,
moram ga spomenuti
05:25
because he's doneučinio some fantasticfantastičan work,
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zato što je napravio fantastičan posao
05:27
and now he has proveddokazao that it worksdjela over the mobilemobilni telephonetelefon networkmreža as well,
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i dokazao da funkcionira i preko mobilnih mreža,
05:31
whichkoji enablesomogućuje this projectprojekt, and we're gettinguzimajući 99 percentposto accuracytočnost.
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što omogućuje ovaj projekt. Dobili smo točnost od 99%.
05:34
TRTR: Ninety-nine99. Well, that's an improvementpoboljšanje.
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TR: Devedeset devet. To se zove poboljšanje.
05:36
So what that meanssredstva is that people will be ableu stanju to —
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Dakle, to znači da će ljudi moći –
05:38
MLML: (LaughsSmijeh)
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ML: (Smijeh)
05:40
TRTR: People will be ableu stanju to call in from theirnjihov mobilemobilni phonestelefoni
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TR: Ljudi će moći nazvati sa svojih mobitela
05:42
and do this testtest, and people with Parkinson'sParkinsonove could call in,
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i napraviti ovaj test.
I ljudi sa Parkinsonom će moći nazvati,
05:45
recordsnimiti theirnjihov voiceglas, and then theirnjihov doctorliječnik can checkprovjeriti up
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snimiti svoj glas i onda će
njihov liječnik moći provjeriti
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on theirnjihov progressnapredak, see where they're doing in this coursenaravno of the diseasebolest.
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njihov napredak, vidjeti gdje su u tijeku bolesti.
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MLML: AbsolutelyApsolutno.
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ML: Apsolutno.
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TRTR: ThanksHvala so much. MaxMax Little, everybodysvi.
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TR: Hvala puno. Maxe Little, ljudi.
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MLML: ThanksHvala, TomTom. (ApplausePljesak)
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ML: Hvala, Tome. (Pljesak)
Translated by Senzos Osijek
Reviewed by Suzana Barić

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ABOUT THE SPEAKER
Max Little - Applied mathematician
Max Little is a mathematician whose research includes a breakthrough technique to monitor – and potentially screen for – Parkinson's disease through simple voice recordings.

Why you should listen

Max Little is an applied mathematician whose goal is to "see connections between subjects, not boundaries … to see how things are related, not how they are different." He has a background in applied mathematics, statistics, signal processing and computational engineering, and his work has been applied across disciplines like biomedicine, extreme rainfall analysis and forecasting, biophysical signal processing, and hydrogeomorphology and open channel flow measurement. Little is best known for his work on the Parkinson's Voice Initiative, in which he and his team developed a cheap and simple tool that uses precise voice analysis software to detect Parkinson's with 99 percent accuracy. Little is a TEDGlobal 2012 Fellow and a Wellcome Trust-MIT Postdoctoral Research Fellow.

More profile about the speaker
Max Little | Speaker | TED.com