{"id":3086,"date":"2026-10-01T10:11:17","date_gmt":"2026-10-01T10:11:17","guid":{"rendered":"https:\/\/www.innothyrogen.eu\/uncategorized-sr\/kako-masinsko-ucenje-pomaze-u-predvidjanju-bolesti-stitne-zlezde\/"},"modified":"2026-10-01T10:12:45","modified_gmt":"2026-10-01T10:12:45","slug":"kako-masinsko-ucenje-pomaze-u-predvidjanju-bolesti-stitne-zlezde","status":"publish","type":"post","link":"https:\/\/www.innothyrogen.eu\/sr\/%d0%b1%d0%bb%d0%be%d0%b3\/kako-masinsko-ucenje-pomaze-u-predvidjanju-bolesti-stitne-zlezde\/","title":{"rendered":"Kako ma\u0161insko u\u010denje poma\u017ee u predvi\u0111anju bolesti \u0161titne \u017elezde"},"content":{"rendered":"\n

Za\u0161to je ma\u0161insko u\u010denje va\u017eno za predvi\u0111anje bolesti \u0161titne \u017elezde<\/a><\/h2>\n\n

Poreme\u0107aji \u0161titne \u017elezde, uklju\u010duju\u0107i hipotireozu, hipertireozu i maligne bolesti \u0161titne \u017elezde, spadaju me\u0111u naj\u010de\u0161\u0107a metaboli\u010dka i endokrina stanja \u0161irom sveta. Tradicionalna dijagnoza se oslanja na klini\u010dke simptome, fizi\u010dki pregled i biohemijske analize. Ove analize mere nivoe tiroidnih hormona, kao \u0161to su tireostimuli\u0161u\u0107i hormon (TSH), slobodni T3 i slobodni T4, kao i tiroidna antitela (TPOAb). Pored toga, dijagnoza koristi ultrazvu\u010dne preglede i biopsije aspiracijom tankom iglom. Iako su utvr\u0111eni putevi korisni, standardne klini\u010dke grani\u010dne vrednosti ponekad mogu da previde suptilne disfunkcije ili ne uspeju da obuhvate slo\u017eene individualne fiziolo\u0161ke varijacije. Ma\u0161insko u\u010denje (ML) postaje sve va\u017enije u predvi\u0111anju bolesti \u0161titne \u017elezde jer obra\u0111uje slo\u017eene dijagnosti\u010dke varijable i smanjuje dijagnosti\u010dke gre\u0161ke. <\/a><\/p>\n\n

Modeli nadgledanog i nenadgledanog u\u010denja<\/h2>\n\n

Svi modeli ma\u0161inskog u\u010denja mogu se podeliti u dve kategorije: modeli nadgledanog i nenadgledanog u\u010denja, u zavisnosti od podataka sa kojima rade. Nadgledani modeli, kao \u0161to su logisti\u010dka regresija, stabla odlu\u010divanja i neuronske mre\u017ee, rade sa ozna\u010denim podacima koji imaju eksplicitno definisane ishode. Na primer, svaki uzorak ima unapred definisanu ciljnu oznaku kao \u0161to je \u201ehipotireoidni\u201c ili \u201eeutireoidni\u201c. Ovi modeli generi\u0161u predvi\u0111anja na novim neozna\u010denim podacima, koji se obi\u010dno nazivaju validacioni uzorci. U tu svrhu koriste obu\u010dene vrednosti predvi\u0111anja iz skupa za obuku ozna\u010denih podataka. <\/a><\/p>\n\n

Nenadgledani modeli, kao \u0161to je K-means klasterizacija, rade sa neozna\u010denim podacima kako bi otkrili skrivene obrasce ili osnovne strukturne distribucije bez unapred definisanih ciljnih oznaka.<\/p>\n\n

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

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

Logisti\u010dka regresija (LR) je osnovni statisti\u010dki model i model ma\u0161inskog u\u010denja. Ona mapira ulazne karakteristike u ocenu verovatno\u0107e koriste\u0107i sigmoidnu funkciju. Za predvi\u0111anje bolesti \u0161titne \u017elezde, \u010desto slu\u017ei kao mo\u0107an osnovni model, posti\u017eu\u0107i visoku ta\u010dnost klasifikacije na strukturiranim skupovima podataka o \u0161titnoj \u017elezdi. Po\u0161to model daje eksplicitne regresione koeficijente, medicinski istra\u017eiva\u010di ih mogu direktno prevesti u odnose \u0161ansi. Ovi odnosi kvantifikuju koliko promena jedne jedinice u biomarkeru, kao \u0161to je TSH, menja rizik od poreme\u0107aja \u0161titne \u017elezde. <\/a><\/p>\n\n

Me\u0111utim, LR se oslanja na ru\u010dno unapred modelovane odnose izme\u0111u ishoda i prediktora, \u0161to \u010desto zahteva ru\u010dni in\u017eenjering karakteristika i slo\u017eenu normalizaciju podataka. \u010cesto se de\u0161ava da u kartonu pacijenta nedostaju laboratorijske vrednosti, kao \u0161to je neizmeren test slobodnog T3. Zbog toga se podacima koji nedostaju mora upravljati unapred putem tehnika imputacije. <\/p>\n\n

Stabla odlu\u010divanja i ekstremni gradijentni busting<\/h2>\n\n

Stabla odlu\u010divanja su neparametarski modeli nadgledanog u\u010denja koji rekurzivno dele podatke na osnovu pragova karakteristika. Na primer, ovi pragovi mogu biti specifi\u010dne grani\u010dne vrednosti za nivoe TSH ili slobodnog T4. Ovi modeli efikasno hvataju nelinearne odnose i interakcije izme\u0111u razli\u010ditih klini\u010dkih parametara, kao \u0161to su starost, pol i hormonski profili. Za razliku od LR modela, oni ne zahtevaju slo\u017eenu normalizaciju podataka. <\/a><\/p>\n\n

XGBoost je napredni okvir ansambla sa gradijentnim bustingom koji gradi sekvencijalna stabla odlu\u010divanja, gde svako novo stablo ispravlja preostale gre\u0161ke prethodnih. XGBoost se dosledno rangira me\u0111u algoritmima sa najboljim performansama za tabelarne medicinske skupove podataka minimiziranjem pristrasnosti i varijanse, spre\u010davanjem preprilago\u0111avanja na \u0161umnim klini\u010dkim skupovima podataka i efikasnim rukovanjem nedostaju\u0107im vrednostima. <\/a><\/p>\n\n

Neuronske mre\u017ee za predvi\u0111anje bolesti \u0161titne \u017elezde<\/h2>\n\n

Nedavni napredak u dubokom u\u010denju dramati\u010dno je pobolj\u0161ao na\u010din na koji neuronske mre\u017ee obra\u0111uju podatke za predvi\u0111anje i klasifikaciju bolesti \u0161titne \u017elezde. Na primer, one obra\u0111uju slikovne podatke, kao \u0161to su ultrazvu\u010dni snimci, i numeri\u010dke podatke, kao \u0161to su starost, pol i nivoi TSH. <\/p>\n\n

Medicinsko snimanje, posebno ultrasonografija \u0161titne \u017elezde, oslanja se na konvolucione neuronske mre\u017ee (CNN, prikazane na slici 1) i specijalizovane arhitekture dubokog u\u010denja kako bi se razlikovali benigni i maligni \u010dvorovi \u0161titne \u017elezde. Istra\u017eiva\u010di \u010desto koriste standardne ili ponovo obu\u010dene osnovne CNN mre\u017ee, kao \u0161to je ResNet, putem transfernog u\u010denja kako bi procenili radiomi\u010dke karakteristike kao \u0161to su oblik \u010dvora, ivice, ehogenost i kalcifikacije. <\/p>\n\n

Zajedni\u010dki prediktivni modeli, kao \u0161to su hibridni modeli i modeli fuzije radiomike i klini\u010dkih podataka, spajaju numeri\u010dke metrike pacijenata sa kvantitativnim radiomi\u010dkim karakteristikama slike. Spajanje ovih tokova podataka poma\u017ee u optimizaciji dijagnosti\u010dkih tokova rada i minimizira potrebu za invazivnim biopsijama aspiracijom tankom iglom na benignim \u010dvorovima. <\/p>\n\n

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

Slika 1.<\/strong> Tok rada za klasifikaciju \u010dvorova \u0161titne \u017elezde pomo\u0107u konvolucionih neuronskih mre\u017ea sa ResNet18 i ekstrakcijom karakteristika. Indikatori evaluacije se koriste za procenu efekta klasifikacije modela. Pored toga, kori\u0161\u0107ena je toplotna mapa za vizuelizaciju rezultata predvi\u0111anja, pomo\u0107u koje su autori izdvojili i analizirali istaknute oblasti. Prilago\u0111eno prema J. Yang i sar. 2022 [5].<\/em><\/p>\n\n

Kako ML mo\u017ee promeniti klini\u010dku praksu<\/h2>\n\n

U predvi\u0111anju bolesti \u0161titne \u017elezde, evaluacija performansi osigurava visoke standarde validacije i pouzdanost pre primene. Na primer, evaluacija koristi metrike kao \u0161to su AUC, F1 skor, preciznost i odziv. Pored toga, ekstrakcija karakteristika izoluje klju\u010dne klini\u010dke varijable i radiomi\u010dke biomarkere kako bi se rano uo\u010dile suptilne fiziolo\u0161ke varijacije. <\/a><\/p>\n\n

Pristrasnost i obja\u0161njivost u predvi\u0111anju bolesti \u0161titne \u017elezde<\/h2>\n\n

Klju\u010dni potencijalni rizici povezani sa skoro svakim naprednim ML modelom uklju\u010duju pristrasnost algoritma usled nereprezentativnih skupova podataka za obuku. Pored toga, priroda \u201ecrne kutije\u201c ote\u017eava klini\u010dku interpretaciju. Da bismo ispravili pristrasnost, moramo da izgradimo skupove za obuku koji istinski predstavljaju razli\u010dite grupe pacijenata. Tako\u0111e moramo da koristimo pametna prilago\u0111avanja podataka kako bismo spre\u010dili pojavu zdravstvenih dispariteta. <\/p>\n\n

Re\u0161avanje problema \u201ecrne kutije\u201c uklju\u010duje integraciju okvira za obja\u0161njivu ve\u0161ta\u010dku inteligenciju, kao \u0161to je SHapley Additive exPlanations (SHAP, prikazan na slici 2). Ovi okviri, zauzvrat, matemati\u010dki otkrivaju kako specifi\u010dne karakteristike uti\u010du na predvi\u0111anje. Odr\u017eavanje ovih alata bezbednim, pravednim i pouzdanim svodi se na redovne provere performansi modela. Pored toga, timovi sa razli\u010ditim perspektivama treba da vode njihovu upotrebu. Kona\u010dno, lekari moraju ostati direktno uklju\u010deni u nadzor svake odluke pre nego \u0161to ve\u0161ta\u010dka inteligencija u\u0111e u svakodnevni klini\u010dki rad. <\/p>\n\n

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

Slika 2:<\/strong> Primer SHAP vrednosti va\u017enih karakteristika koje uti\u010du na izlaz modela. Velike pozitivne SHAP vrednosti<\/em> ukazuju na to da specifi\u010dna vrednost karakteristike pomera predvi\u0111anje modela ka pozitivnom ishodu (kao \u0161to je ve\u0107i rizik od bolesti \u0161titne \u017elezde), dok velike negativne SHAP vrednosti pomeraju predvi\u0111anje ka negativnom ishodu (kao \u0161to je manji rizik od bolesti \u0161titne \u017elezde ili normalno eutireoidno stanje). Preuzeto od C. J.<\/em> Ejiyi i sar. 2026 [6]<\/em><\/em><\/p>\n\n

Autor: Lucia Buljac (Univerzitet u Splitu, Prirodno-matemati\u010dki fakultet)<\/strong><\/p>\n\n

Klju\u010dne reference<\/h2>\n\n
    \n
  1. Alkhazzar, H., & Moslem, B. (2024). Thyroid Disease Prediction Using Machine Learning Model: Decision Tree. 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). Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques. 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). Development and preliminary validation of a machine learning system for thyroid dysfunction diagnosis based on routine laboratory tests. Communications Medicine<\/em>, 2<\/em>, Article 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). Using Deep Convolutional Neural Networks for Enhanced Ultrasonographic Image Diagnosis of Differentiated Thyroid Cancer. 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). Ultrasound Image Classification of Thyroid Nodules Based on Deep Learning. 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. A robust framework for clinically interpretable hypothyroidism diagnosis with Polynomial-SHAP and DeepSeqNet. 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 ma\u0161insko u\u010denje va\u017eno za predvi\u0111anje bolesti \u0161titne \u017elezde Poreme\u0107aji \u0161titne \u017elezde, uklju\u010duju\u0107i hipotireozu, hipertireozu i maligne bolesti \u0161titne \u017elezde, spadaju me\u0111u naj\u010de\u0161\u0107a metaboli\u010dka i endokrina stanja \u0161irom sveta. Tradicionalna dijagnoza se oslanja na klini\u010dke simptome, fizi\u010dki pregled i biohemijske analize. Ove analize mere nivoe tiroidnih hormona, kao \u0161to su tireostimuli\u0161u\u0107i hormon (TSH), slobodni […]<\/p>\n","protected":false},"author":1,"featured_media":3087,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[76],"tags":[50,48,49,52,51],"class_list":["post-3086","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-76","tag-eufunded","tag-horizoneurope","tag-innothyrogen","tag-personalisedmedicine","tag-thyroidhealth"],"yoast_head":"\nKako ma\u0161insko u\u010denje poma\u017ee u predvi\u0111anju bolesti \u0161titne \u017elezde - InnoThyroGen EU projekat<\/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\/sr\/%d0%b1%d0%bb%d0%be%d0%b3\/kako-masinsko-ucenje-pomaze-u-predvidjanju-bolesti-stitne-zlezde\/\" \/>\n<meta property=\"og:locale\" content=\"sr_RS\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Kako ma\u0161insko u\u010denje poma\u017ee u predvi\u0111anju bolesti \u0161titne \u017elezde - InnoThyroGen EU projekat\" \/>\n<meta property=\"og:description\" content=\"Za\u0161to je ma\u0161insko u\u010denje va\u017eno za predvi\u0111anje bolesti \u0161titne \u017elezde Poreme\u0107aji \u0161titne \u017elezde, uklju\u010duju\u0107i hipotireozu, hipertireozu i maligne bolesti \u0161titne \u017elezde, spadaju me\u0111u naj\u010de\u0161\u0107a metaboli\u010dka i endokrina stanja \u0161irom sveta. Tradicionalna dijagnoza se oslanja na klini\u010dke simptome, fizi\u010dki pregled i biohemijske analize. Ove analize mere nivoe tiroidnih hormona, kao \u0161to su tireostimuli\u0161u\u0107i hormon (TSH), slobodni […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.innothyrogen.eu\/sr\/%d0%b1%d0%bb%d0%be%d0%b3\/kako-masinsko-ucenje-pomaze-u-predvidjanju-bolesti-stitne-zlezde\/\" \/>\n<meta property=\"og:site_name\" content=\"InnoThyroGen EU projekat\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/profile.php?id=61571994011521\" \/>\n<meta property=\"article:published_time\" content=\"2026-10-01T10:11:17+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-10-01T10:12:45+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.innothyrogen.eu\/wp-content\/uploads\/2026\/10\/1884.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta property=\"og:image:height\" content=\"667\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta 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