- I-AI ithuthukisa ukutholwa kwezithombe futhi inciphisa kakhulu isikhathi sokuhlaziya ukuxilonga.
- Amasu Okufunda Okujulile kanye namaNethiwekhi Emizwa Eguquguqukayo enza kube nokwenzeka ukubona izinto ezingavamile ezingabonakali ngeso lomuntu.
- I-Radiomics ivela njengethuluzi lokubikezela ukwenza ukwelashwa kube ngokwakho kanye nokuqapha ukuqhubeka kwesifo.
- Ubuchwepheshe busebenza njengosekelo oluyisu kudokotela wemisebe, abusoze buthathe indawo yokubhekwa kwabantu.
Uke waphawula ukuthi ezokwelapha zishintshe kakhulu kangakanani emashumini eminyaka amuva nje? Iqiniso liwukuthi, siphila esikhathini esiyingqayizivele, lapho i-Artificial Intelligence (AI) isishintshe khona ekubeni yinto engaphandle kwama-movie esayensi yaba yindoda yodokotela abangochwepheshe. Ikakhulukazi ku-radiology, lapho inani ledatha likhulu kakhulu, lobu buchwepheshe bunikeza amandla adingekayo okwenza ukuxilongwa ngokunemba okwakubonakala kungenakwenzeka ngaphambili.
Lokhu akukhona ngokushintsha uchwepheshe, kodwa kunalokho ngokubanika amathuluzi azenzakalela imisebenzi eyisicefe nephindaphindayo , okuvumela udokotela we-radiation ukuthi agxile kulokho okubaluleke ngempela: isiguli. Kunjengokungathi udokotela uthole umsizi onamandla amakhulu ongaskena izinkulungwane zezithombe ngemizuzwana, ekhomba lapho izinkinga zingase zibe khona futhi asize ekusindiseni izimpilo ngokutholwa kusenesikhathi.
Ukwembula Imiqondo: I-AI, Ukufunda Komshini, kanye Nokufunda Okujulile
Ukuze siqale ingxoxo, sidinga ukucacisa imigomo. Uma sikhuluma nge-AI, sibhekisela ekhonweni lemishini lokwenza imisebenzi evame ukudinga ukuqonda komuntu . Ngaphakathi kwalesi sambulela esibanzi, sine-Machine Learning (ML), edala izinhlelo ezikwazi ukufunda imithetho kusuka kudatha. Umehluko omkhulu lapha ukuthi, ku-ML yendabuko, kusenezinga elithile lokungenelela komuntu ekukhetheni izici zedatha.
Ukufunda Okujulile (DL) kufana nokuvela kwemvelo kwe-AA. Lapha, ukungenelela komuntu kuncane kakhulu , njengoba uhlelo lusebenzisa amanethiwekhi e-neural anezingqimba eziningi ukukhipha izici ezifanele kakhulu ngokwalo. Yingakho lubizwa ngokuthi "okujulile": lapho izingqimba eziningi, kulapho ikhono lokucubungula ulwazi oluyinkimbinkimbi likhula khona. Ku-radiology, lokhu kubaluleke kakhulu, njengoba kuvumela ikhompyutha ukuthi iqonde umehluko phakathi kwephikseli evamile kanye ne- microcalcification esolisayo ngaphandle kokuthi othile ayitshele ngqo ukuthi ibheke kuphi.
Uhambo Lobuchwepheshe kanye Nenethiwekhi Yezinzwa
I-AI ayizange izalwe izolo. Kusukela ngawo-50, sidlule ezigabeni zentshiseko enkulu kanye nalokho okubizwa ngokuthi "ubusika be-AI," lapho ubuchwepheshe bume khona. Kodwa-ke, ukuchuma kwamanje kungenxa yokufika kwamayunithi okucubungula ihluzo anamandla (ama-GPU) kanye nokutholakala kwedatha okungakaze kubonwe. Kusukela ezigabeni ezibalulekile njenge-Deep Blue ku-chess kuya ku-AlphaZero, efunde ukudlala yodwa, umkhondo ukhombisa ukuthi i-AI iguqukele ezimisweni zemithetho eqinile kuya kumamodeli aqonda amaphethini ngokuzimela.
Inhliziyo yezithombe zezokwelapha kukhona amaNethiwekhi Emizwa Okwenziwa. Cabanga ngama-perceptron amancane asebenza ngendlela efanayo nama-neurons ethu ebhayoloji, ecubungula izinto ezishukumisayo ngemisebenzi yokuvuselela. Uma sikhuluma ngokubona okwenziwe , izinkanyezi ziyi-Convolutional Neural Networks (ama-CNN). Ngokungafani namaNethiwekhi akudala, ama-CNN asebenzisa izihlungi ezihlaziya isithombe endaweni, zikwazi ukuhlonza into kungakhathaliseki ukuthi ikuphi isikhundla noma i-engeli, okuyisisekelo sokuhlaziya ama-anatomie abantu ahlukahluka kuye ngesiguli.
Inqubo Yokuqeqesha Nezinselele Zayo
Ukuqeqesha i-AI akulula. Okokuqala, kuchazwa ama-hyperparameter (njengenani lezingqimba), bese kulungiswa isisindo sokuxhumeka kwe-neural. Kumodeli yokufunda egadiwe , inethiwekhi iqhathanisa ukubikezela kwayo nelebula langempela (elinikezwe uchwepheshe) bese izilungisa ngenqubo ebizwa ngokuthi i-backpropagation ukuze kuncishiswe iphutha. Ukuze lokhu kusebenze, idatha ihlukaniswe ngamaqembu amathathu: ukuqeqeshwa, ukuqinisekiswa, kanye nokuhlolwa kokugcina , ukuqinisekisa ukuthi umshini awugcini nje ngokukhumbula izibonelo, kodwa kunalokho uhlanganisa ulwazi.
Kodwa-ke, akuzona zonke izinhlobo zezimbali. Kukhona ingozi yokufaka ngokweqile , lapho i-AI iba umlutha kakhulu kudatha yokuqeqeshwa kangangokuthi yehluleka ngeziguli ezintsha. Inkinga enkulu lapha ukuntuleka kwezithombe ezinelebula efanele, njengoba lokhu kudinga isikhathi esiningi kodokotela bemisebe. Ukuze kunqotshwe lokhu, kusetshenziswa i-Transfer Learning , equkethe ukusebenzisa ukwakheka kanye nesisindo senethiwekhi esivele iqeqeshwe kwenye indawo nokuyivumelanisa nemithi, kusukela esikhundleni esinenzuzo kakhulu.
I-AI ku-Radiology Service Workflow
Ukuqaliswa kwe-AI kwenzeka ezigabeni ezahlukene zokwelapha kwansuku zonke. Kuqala ngokutholwa kwesithombe , lapho ama-algorithms angasheshisa ukuthwebula nokunciphisa izinto ezibangelwa ukunyakaza kwesiguli, okwenza isipiliyoni sibe mnandi kakhulu futhi isithombe sibe bukhali. Ngaphezu kwalokho, i-AI isiza ukuthuthukisa umthamo wemisebe, ilandela isimiso se-ALARA (esiphansi ngangokunokwenzeka), yandisa ukuphepha kwesiguli.
Esigabeni sokuhlaziya, amathuluzi okuhlukanisa azenzakalela ukuhlukaniswa kwezitho noma amathumba, umsebenzi owawenziwa ngesandla futhi wadliwa amahora amaningi. Izinhlelo zokuthola amanxeba zisebenza njengesixwayiso, ziqokomisa izindawo ezisolisayo ukuze udokotela angaphuthelwa lutho. Kukhona futhi ukuhlukaniswa okubalulekile: I-AI ingahlaziya umugqa wezivivinyo futhi ibeke amacala aphuthumayo phezulu ohlwini lodokotela we-radiation, isheshise ukungenelela okungasindisa izimpilo.
Ama-Radiomics kanye Nekusasa Lemithi Eqondene Nomuntu Siqu
Nakuba ukuxilongwa kuhlaba umxhwele, i-radiomics yilapho umlingo we-prognosis uvela khona. Lo mkhakha ukhipha idatha yobuningi kusuka ekubunjweni nasezimo ezingabonakali emehlweni omuntu. Ngokuhlanganisa le datha ne-genomics kanye nokuhlaziywa kwemitholampilo, singakha amamodeli emithi eqondene nawe . Lokhu kusenza sikwazi ukubikezela ukuthi isimila sizosabela yini ekwelashweni okuthile noma amathuba okuphindeka kwesifo, silungise ukwelashwa ngesikhathi sangempela.
Naphezu kwamandla, sisabhekene nokungacaci kwama-algorithms , okubizwa ngokuthi "amabhokisi amnyama," lapho singazi kahle ukuthi i-AI yafika kanjani kuleso siphetho. Izixazululo ezifana ne-Grad-CAM zizama ukukhanyisa lezi zindawo, zibonise ukuthi umshini ugxile kuphi ukunaka kwawo. Ukuhlanganiswa okugcwele manje kuncike ekusebenzisaneni phakathi kwezinhlelo ezivela kubakhiqizi abahlukene kanye nezinhlaka zokuziphatha eziqinisekisa ubumfihlo bedatha yesiguli.
Ukuhlangana kokuhlangenwe nakho kwezokwelapha kwabantu kanye namakhono amakhulu okucubungula idatha kuguqula i-radiology ibe ubuchwepheshe obubikezelayo nobunembile kakhulu. Ngokuzenzakalela kwemisebenzi ephindaphindayo, ikhwalithi yesithombe ethuthukisiwe, kanye nokuhlaziywa kwe-radiomic, uhlelo lwezempilo luya esimweni lapho ukutholwa kusenesikhathi kuyinto evamile futhi ukwelashwa kulungiselelwe ngqo umuntu ngamunye, kuphakamisa ukusebenza kahle kokuxilonga ezingeni elisha lobuhle.
