{"id":3083,"date":"2026-10-01T10:11:17","date_gmt":"2026-10-01T10:11:17","guid":{"rendered":"https:\/\/www.innothyrogen.eu\/uncategorized-hr\/kako-strojno-ucenje-pomaze-u-predvidanju-bolesti-stitnjace\/"},"modified":"2026-10-01T10:12:45","modified_gmt":"2026-10-01T10:12:45","slug":"kako-strojno-ucenje-pomaze-u-predvidanju-bolesti-stitnjace","status":"publish","type":"post","link":"https:\/\/www.innothyrogen.eu\/hr\/blog\/kako-strojno-ucenje-pomaze-u-predvidanju-bolesti-stitnjace\/","title":{"rendered":"Kako strojno u\u010denje poma\u017ee u predvi\u0111anju bolesti \u0161titnja\u010de"},"content":{"rendered":"\n

Za\u0161to je strojno u\u010denje va\u017eno za predvi\u0111anje bolesti \u0161titnja\u010de<\/a><\/h2>\n\n

Poreme\u0107aji \u0161titnja\u010de, uklju\u010duju\u0107i hipotireozu, hipertireozu i maligne bolesti \u0161titnja\u010de, spadaju me\u0111u naj\u010de\u0161\u0107a metaboli\u010dka i endokrina stanja diljem svijeta. Tradicionalna dijagnoza oslanja se na klini\u010dke simptome, fizi\u010dki pregled i biokemijske analize. Ove analize mjere razine hormona \u0161titnja\u010de, kao \u0161to su tireostimuliraju\u0107i hormon (TSH), slobodni T3 i slobodni T4, te protutijela \u0161titnja\u010de (TPOAb). Osim toga, dijagnoza koristi ultrazvu\u010dne preglede i biopsije tankom iglom. Iako su uspostavljeni protokoli korisni, standardne klini\u010dke grani\u010dne vrijednosti ponekad mogu propustiti suptilne disfunkcije ili ne uspjeti uhvatiti slo\u017eene individualne fiziolo\u0161ke varijacije. Strojno u\u010denje (ML) postalo je sve va\u017enije u predvi\u0111anju bolesti \u0161titnja\u010de rje\u0161avanjem slo\u017eenih dijagnosti\u010dkih varijabli i smanjenjem dijagnosti\u010dkih pogre\u0161aka. <\/a><\/p>\n\n

Nadzirani i nenadzirani modeli u\u010denja<\/h2>\n\n

Svi modeli strojnog u\u010denja mogu se podijeliti u dvije kategorije: nadzirane i nenadzirane modele u\u010denja, ovisno o podacima s kojima rade. Nadzirani modeli, kao \u0161to su logisti\u010dka regresija, stabla odlu\u010divanja i neuronske mre\u017ee, rade s ozna\u010denim podacima koji sadr\u017ee eksplicitno definirane ishode. Na primjer, svaki uzorak ima unaprijed definiranu ciljnu oznaku poput \u201ehipotireoidan\u201c ili \u201eeutireoidan\u201c. Ovi modeli generiraju predvi\u0111anja na novim neozna\u010denim podacima, obi\u010dno nazvanim validacijskim uzorcima. U tu svrhu koriste obu\u010dene prediktivne vrijednosti iz skupa za obuku ozna\u010denih podataka. <\/a><\/p>\n\n

Nenadzirani modeli, kao \u0161to je K-means klasteriranje, rade s neozna\u010denim podacima kako bi otkrili skrivene obrasce ili temeljne strukturne distribucije bez unaprijed definiranih ciljnih oznaka.<\/p>\n\n

\"\"<\/figure>\n\n

Modeli logisti\u010dke regresije<\/h2>\n\n

Logisti\u010dka regresija (LR) je temeljni statisti\u010dki model i model strojnog u\u010denja. Ona preslikava ulazne zna\u010dajke u vjerojatnosni rezultat pomo\u0107u sigmoidne funkcije. Za predvi\u0111anje bolesti \u0161titnja\u010de \u010desto slu\u017ei kao sna\u017ean osnovni model, posti\u017eu\u0107i visoku to\u010dnost klasifikacije na strukturiranim skupovima podataka o \u0161titnja\u010di. Budu\u0107i da model daje eksplicitne regresijske koeficijente, medicinski istra\u017eiva\u010di ih mogu izravno prevesti u omjere izgleda (odds ratios). Ti omjeri kvantificiraju koliko promjena jedne jedinice u biomarkeru, poput TSH-a, mijenja rizik od poreme\u0107aja \u0161titnja\u010de. <\/a><\/p>\n\n

Me\u0111utim, LR se oslanja na ru\u010dno unaprijed modelirane odnose izme\u0111u ishoda i prediktora, \u010desto zahtijevaju\u0107i ru\u010dni in\u017eenjering zna\u010dajki i slo\u017eenu normalizaciju podataka. \u010cesto, medicinski karton pacijenta ima nedostaju\u0107e laboratorijske vrijednosti, kao \u0161to je neizmjereni test slobodnog T3. Stoga se podaci koji nedostaju moraju prethodno obraditi tehnikama imputacije. <\/p>\n\n

Stabla odlu\u010divanja i ekstremno poja\u010davanje gradijenta<\/h2>\n\n

Stabla odlu\u010divanja su neparametarski nadzirani modeli u\u010denja koji rekurzivno dijele podatke na temelju pragova zna\u010dajki. Na primjer, ti pragovi mogu biti specifi\u010dne grani\u010dne vrijednosti za razine TSH-a ili slobodnog T4. Ovi modeli u\u010dinkovito bilje\u017ee nelinearne odnose i interakcije izme\u0111u razli\u010ditih klini\u010dkih parametara, kao \u0161to su dob, spol i hormonski profili. Za razliku od LR modela, ne zahtijevaju slo\u017eenu normalizaciju podataka. <\/a><\/p>\n\n

XGBoost je napredni okvir za ansambl poja\u010davanja gradijenta koji gradi sekvencijalna stabla odlu\u010divanja, gdje svako novo stablo ispravlja preostale pogre\u0161ke prethodnih. XGBoost se dosljedno svrstava me\u0111u najbolje algoritme za tabli\u010dne medicinske skupove podataka minimiziranjem pristranosti i varijance, sprje\u010davanjem prekomjernog prilago\u0111avanja na bu\u010dnim klini\u010dkim skupovima podataka i u\u010dinkovitim rukovanjem nedostaju\u0107im vrijednostima. <\/a><\/p>\n\n

Neuronske mre\u017ee za predvi\u0111anje bolesti \u0161titnja\u010de<\/h2>\n\n

Nedavna napredovanja u dubokom u\u010denju dramati\u010dno su pobolj\u0161ala na\u010din na koji neuronske mre\u017ee obra\u0111uju podatke za predvi\u0111anje i klasifikaciju bolesti \u0161titnja\u010de. Na primjer, obra\u0111uju slikovne podatke, poput ultrazvu\u010dnih snimaka, i numeri\u010dke podatke, poput dobi, spola i razine TSH-a. <\/p>\n\n

Medicinsko snimanje, posebno ultrazvuk \u0161titnja\u010de, oslanja se na konvolucijske neuronske mre\u017ee (CNN, prikazane na Slici 1) i specijalizirane arhitekture dubokog u\u010denja za razlikovanje benignih i malignih \u010dvorova \u0161titnja\u010de. Istra\u017eiva\u010di \u010desto koriste standardne ili ponovno obu\u010dene osnovne CNN-ove, poput ResNeta, putem prijenosnog u\u010denja za procjenu radiomijskih zna\u010dajki kao \u0161to su oblik \u010dvora, rubovi, ehogenost i kalcifikacije. <\/p>\n\n

Zajedni\u010dki prediktivni modeli, kao \u0161to su hibridni i fuzijski modeli radiomike i klini\u010dkih podataka, spajaju numeri\u010dke metrike pacijenata s kvantitativnim radiomijskim zna\u010dajkama slike. Spajanje ovih tokova podataka poma\u017ee optimizirati dijagnosti\u010dke radne procese i minimizira potrebu za invazivnim biopsijama tankom iglom na benignim \u010dvorovima. <\/p>\n\n

\"\"<\/figure>\n\n

Slika 1.<\/strong> Radni proces za klasifikaciju \u010dvorova \u0161titnja\u010de pomo\u0107u konvolucijskih neuronskih mre\u017ea s ResNet18 i ekstrakcijom zna\u010dajki. Indikatori evaluacije koriste se za procjenu u\u010dinka klasifikacije modela. Osim toga, kori\u0161tena je toplinska karta za vizualizaciju rezultata predvi\u0111anja, pomo\u0107u koje su autori ekstrahirali i analizirali istaknuta podru\u010dja. Prilago\u0111eno iz J. Yang et al 2022 [5].<\/em><\/p>\n\n

Kako strojno u\u010denje mo\u017ee promijeniti klini\u010dku praksu<\/h2>\n\n

U predvi\u0111anju bolesti \u0161titnja\u010de, evaluacija performansi osigurava visoke standarde validacije i pouzdanost prije implementacije. Na primjer, evaluacija koristi metrike poput AUC-a, F1 rezultata, preciznosti i odziva. Osim toga, ekstrakcija zna\u010dajki izolira bitne klini\u010dke varijable i radiomijske biomarkere kako bi se rano uhvatile suptilne fiziolo\u0161ke varijacije. <\/a><\/p>\n\n

Pristranost i obja\u0161njivost u predvi\u0111anju bolesti \u0161titnja\u010de<\/h2>\n\n

Klju\u010dni potencijalni rizici povezani s gotovo svakim naprednim ML modelom uklju\u010duju algoritamsku pristranost iz nereprezentativnih skupova podataka za obuku. Osim toga, priroda \u201ecrne kutije\u201c ote\u017eava klini\u010dku interpretaciju. Da bismo ispravili pristranost, moramo izgraditi skupove za obuku koji istinski predstavljaju raznolike skupine pacijenata. Tako\u0111er moramo koristiti pametne prilagodbe podataka kako bismo sprije\u010dili pojavu zdravstvenih nejednakosti. <\/p>\n\n

Rje\u0161avanje problema \u201ecrne kutije\u201c uklju\u010duje integraciju okvira obja\u0161njive umjetne inteligencije, kao \u0161to su SHapley Additive exPlanations (SHAP, prikazano na Slici 2). Ovi okviri, zauzvrat, matemati\u010dki izla\u017eu kako specifi\u010dne zna\u010dajke pokre\u0107u predvi\u0111anje. Odr\u017eavanje ovih alata sigurnima, po\u0161tenima i pouzdanima svodi se na redovite provjere performansi modela. Osim toga, timovi s razli\u010ditim perspektivama moraju voditi njihovu upotrebu. Kona\u010dno, lije\u010dnici moraju ostati izravno uklju\u010deni kako bi nadzirali svaku odluku prije nego \u0161to umjetna inteligencija u\u0111e u svakodnevni klini\u010dki rad. <\/p>\n\n

\"\"<\/figure>\n\n

Slika 2:<\/strong> Primjer SHAP vrijednosti va\u017enih zna\u010dajki koje utje\u010du na izlaz modela. Velike pozitivne SHAP vrijednosti<\/em> ukazuju na to da specifi\u010dna vrijednost zna\u010dajke gura predvi\u0111anje modela prema pozitivnom ishodu (kao \u0161to je ve\u0107i rizik od bolesti \u0161titnja\u010de), dok velike negativne SHAP vrijednosti guraju predvi\u0111anje prema negativnom ishodu (kao \u0161to je manji rizik od bolesti \u0161titnja\u010de ili normalno eutireoidno stanje). Preuzeto iz C. J.<\/em> Ejiyi et al 2026 [6]<\/em><\/em><\/p>\n\n

Autor: Lucia Buljac (Sveu\u010dili\u0161te u Splitu, Prirodoslovno-matemati\u010dki fakultet)<\/strong><\/p>\n\n

Klju\u010dne reference<\/h2>\n\n
    \n
  1. Alkhazzar, H., & Moslem, B. (2024). Predvi\u0111anje bolesti \u0161titnja\u010de pomo\u0107u modela strojnog u\u010denja: Stablo odlu\u010divanja. EDRAAK<\/em>, 2024<\/em>, 84\u2013100. https:\/\/doi.org\/10.70470\/edraak\/2024\/011<\/a> <\/li>\n\n\n\n
  2. Chaganti, R., Rustam, F., De La Torre D\u00edez, I., Maz\u00f3n, J. L. V., Rodr\u00edguez, C. L., & Ashraf, I. (2022). Predvi\u0111anje bolesti \u0161titnja\u010de pomo\u0107u selektivnih zna\u010dajki i tehnika strojnog u\u010denja. Cancers<\/em>, 14<\/em>(16), 3914. https:\/\/doi.org\/10.3390\/cancers14163914<\/a> <\/li>\n\n\n\n
  3. Hu, M., Asami, C., Iwakura, H., Nakajima, Y., Sema, R., Kikuchi, T., Miyata, T., Sakamaki, K., Kudo, T., Yamada, M., Akamizu, T., & Sakakibara, Y. (2022). Razvoj i preliminarna validacija sustava strojnog u\u010denja za dijagnozu disfunkcije \u0161titnja\u010de na temelju rutinskih laboratorijskih testova. Communications Medicine<\/em>, 2<\/em>, \u010clanak 71. https:\/\/doi.org\/10.1038\/s43856-022-00071-1<\/a> <\/li>\n\n\n\n
  4. Chan, W.-K., Sun, J.-H., Liou, M.-J., Li, Y.-R., Chou, W.-Y., Liu, F.-H., Chen, S.-T., & Peng, S.-J. (2021). Kori\u0161tenje dubokih konvolucijskih neuronskih mre\u017ea za pobolj\u0161anu ultrazvu\u010dnu dijagnozu diferenciranog karcinoma \u0161titnja\u010de. Biomedicines<\/em>, 9<\/em>(12), 1771. https:\/\/doi.org\/10.3390\/biomedicines9121771<\/a> Citirano: 38<\/li>\n\n\n\n
  5. Yang, J., Shi, X., Wang, B., Qiu, W., Tian, G., Wang, X., Wang, P., & Yang, J. (2022). Klasifikacija ultrazvu\u010dnih slika \u010dvorova \u0161titnja\u010de na temelju dubokog u\u010denja. Frontiers in Oncology<\/em>, 12<\/em>, 905955. https:\/\/doi.org\/10.3389\/fonc.2022.905955<\/a> <\/li>\n\n\n\n
  6. Ejiyi, C.J., Eze, F.O., Hossin, M.A. et al. Robustan okvir za klini\u010dki interpretibilnu dijagnozu hipotireoze s polinomskim SHAP-om i DeepSeqNet-om. J Big Data 13, 63 (2026). https:\/\/doi.org\/10.1186\/s40537-025-01332-1<\/a><\/a><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    Za\u0161to je strojno u\u010denje va\u017eno za predvi\u0111anje bolesti \u0161titnja\u010de Poreme\u0107aji \u0161titnja\u010de, uklju\u010duju\u0107i hipotireozu, hipertireozu i maligne bolesti \u0161titnja\u010de, spadaju me\u0111u naj\u010de\u0161\u0107a metaboli\u010dka i endokrina stanja diljem svijeta. Tradicionalna dijagnoza oslanja se na klini\u010dke simptome, fizi\u010dki pregled i biokemijske analize. Ove analize mjere razine hormona \u0161titnja\u010de, kao \u0161to su tireostimuliraju\u0107i hormon (TSH), slobodni T3 i slobodni […]<\/p>\n","protected":false},"author":1,"featured_media":3084,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[75],"tags":[43,41,42,45,44],"class_list":["post-3083","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-eufunded","tag-horizoneurope","tag-innothyrogen","tag-personalisedmedicine","tag-thyroidhealth"],"yoast_head":"\nKako strojno u\u010denje poma\u017ee u predvi\u0111anju bolesti \u0161titnja\u010de - InnoThyroGen EU projekt<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.innothyrogen.eu\/hr\/blog\/kako-strojno-ucenje-pomaze-u-predvidanju-bolesti-stitnjace\/\" \/>\n<meta property=\"og:locale\" content=\"hr_HR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Kako strojno u\u010denje poma\u017ee u predvi\u0111anju bolesti \u0161titnja\u010de - InnoThyroGen EU projekt\" \/>\n<meta property=\"og:description\" content=\"Za\u0161to je strojno u\u010denje va\u017eno za predvi\u0111anje bolesti \u0161titnja\u010de Poreme\u0107aji \u0161titnja\u010de, uklju\u010duju\u0107i hipotireozu, hipertireozu i maligne bolesti \u0161titnja\u010de, spadaju me\u0111u naj\u010de\u0161\u0107a metaboli\u010dka i endokrina stanja diljem svijeta. 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