Son y\u0131llarda yapay zeka (YZ), t\u0131p alan\u0131nda devrim yaratmaya devam ediyor. \u00d6zellikle karma\u015f\u0131k t\u0131bbi g\u00f6r\u00fcnt\u00fclerin yorumlanmas\u0131nda sa\u011flad\u0131\u011f\u0131 y\u00fcksek kapasite, te\u015fhis s\u00fcre\u00e7lerini \u00e7ok daha h\u0131zl\u0131 ve do\u011fru hale getiriyor. Pediatrik glioma gibi \u00e7ocukluk \u00e7a\u011f\u0131 beyin t\u00fcm\u00f6rlerinde n\u00fcks riskini tahmin etmek ise, g\u00fcn\u00fcm\u00fcz t\u0131bbi uygulamalar\u0131nda halen ciddi zorluklar bar\u0131nd\u0131r\u0131yor. Bu ba\u011flamda Mass General Brigham b\u00fcnyesindeki ara\u015ft\u0131rmac\u0131lar, Boston Children\u2019s Hospital ve Dana-Farber\/Boston Children\u2019s Cancer and Blood Disorders Center ile i\u015f birli\u011fi i\u00e7inde geli\u015ftirdikleri yeni nesil derin \u00f6\u011frenme modelini kullanarak, ard\u0131\u015f\u0131k beyin MR g\u00f6r\u00fcnt\u00fclerini analiz edip glioma n\u00fcks\u00fc riskini \u00f6ng\u00f6rme alan\u0131nda \u00f6nemli bir ad\u0131m att\u0131.<\/p>\n
\u00c7ocuk gliomalar\u0131, cerrahi m\u00fcdahale ile genellikle tedavi edilebilir olsa da, bu t\u00fcm\u00f6rlerdeki heterojen yap\u0131 ve farkl\u0131 n\u00fcks risk profilleri nedeniyle takip s\u00fcre\u00e7leri olduk\u00e7a karma\u015f\u0131kt\u0131r. Tek seferlik g\u00f6r\u00fcnt\u00fcleme sonu\u00e7lar\u0131 n\u00fcks\u00fc \u00f6ng\u00f6rmede yetersiz kalmakta, bu da \u00e7ocuklar ve aileleri i\u00e7in uzun s\u00fcreli, s\u0131k MR takiplerine ba\u011fl\u0131 psikolojik ve lojistik y\u00fcklerin ortaya \u00e7\u0131kmas\u0131na yol a\u00e7maktad\u0131r. Dr. Benjamin Kann liderli\u011findeki ara\u015ft\u0131rma grubu ve ba\u015f yazar Divyanshu Tak, bu durumu de\u011fi\u015ftirmek \u00fczere geli\u015fmi\u015f yapay zeka tekniklerini devreye sokarak ard\u0131\u015f\u0131k post-operatif MR g\u00f6r\u00fcnt\u00fclerinden elde edilen zaman serisi bilgilerini entegre edebilen bir sistem geli\u015ftirdiler.<\/p>\n
Geleneksel medikal g\u00f6r\u00fcnt\u00fcleme yapay zeka modelleri, genellikle tek bir g\u00f6r\u00fcnt\u00fc an\u0131n\u0131 de\u011ferlendirmekle yetinir. Ancak bu yeni y\u00f6ntem, zaman i\u00e7inde s\u0131ralanm\u0131\u015f g\u00f6r\u00fcnt\u00fc verilerini kullanarak beyin dokusundaki ve t\u00fcm\u00f6r mikro\u00e7evresindeki ince de\u011fi\u015fimleri takip etmeye, b\u00f6ylece t\u00fcm\u00f6r\u00fcn davran\u0131\u015f\u0131n\u0131 anlamaya \u00e7al\u0131\u015f\u0131yor. Bu zaman serisi \u00f6\u011frenme (temporal learning) yakla\u015f\u0131m\u0131, t\u0131bbi g\u00f6r\u00fcnt\u00fc analizlerinde hen\u00fcz yayg\u0131n kullan\u0131lmayan ve b\u00fcy\u00fck potansiyel ta\u015f\u0131yan bir makine \u00f6\u011frenmesi stratejisidir. Ara\u015ft\u0131rmac\u0131lar, hastalar\u0131n MR g\u00f6r\u00fcnt\u00fclerini kronolojik s\u0131raya koyarak modelin, ameliyat sonras\u0131 aylar i\u00e7inde ortaya \u00e7\u0131kan n\u00fcanslar\u0131 yakalamas\u0131n\u0131 sa\u011flad\u0131 ve model daha sonra bu de\u011fi\u015fiklikleri klinik n\u00fcks sonu\u00e7lar\u0131yla ili\u015fkilendirdi.<\/p>\n
Pediatrik glioma gibi nadir g\u00f6r\u00fclen kanser t\u00fcrlerinde veri eksikli\u011fi \u00f6nemli bir engel olu\u015fturur. Ara\u015ft\u0131rma ekibi, \u00fclke \u00e7ap\u0131nda \u00e7e\u015fitli kurumlar\u0131n hasta verilerini entegrasyonla bir araya getirerek 715 \u00e7ocuktan yakla\u015f\u0131k 4.000 MR g\u00f6r\u00fcnt\u00fcs\u00fcn\u00fc analiz i\u00e7in kulland\u0131. Bu \u00e7ok merkezli i\u015f birli\u011fi, derin \u00f6\u011frenme algoritmalar\u0131n\u0131n daha g\u00fcvenilir ve genelle\u015ftirilebilir sonu\u00e7lar \u00fcretmesini m\u00fcmk\u00fcn k\u0131ld\u0131. Sadece tek g\u00f6r\u00fcnt\u00fc ile s\u0131n\u0131rl\u0131 kalan modellerin aksine, zaman serisi model bu \u00e7oklu g\u00f6r\u00fcnt\u00fc verilerini birle\u015ftirerek progresyonun foto\u011fraf de\u011fil, filmi \u00e7ekilen bir s\u00fcre\u00e7 oldu\u011funu ortaya koydu.<\/p>\n
Modelin performans\u0131 ise olduk\u00e7a dikkat \u00e7ekiciydi. Hem d\u00fc\u015f\u00fck hem y\u00fcksek dereceli gliomalarda n\u00fcks\u00fc \u00f6ng\u00f6rme do\u011frulu\u011fu, tedaviden sonraki ilk y\u0131l i\u00e7inde %75 ila %89 gibi y\u00fcksek oranlarda ger\u00e7ekle\u015fti. Bu, geleneksel tek g\u00f6r\u00fcnt\u00fc bazl\u0131 tahmin modellerinin yakla\u015f\u0131k %50 olan tesad\u00fcfi ba\u015far\u0131 oran\u0131n\u0131n \u00e7ok \u00fczerinde yer ald\u0131. Ard\u0131\u015f\u0131k g\u00f6r\u00fcnt\u00fclerin say\u0131s\u0131 artt\u0131k\u00e7a tahmin ba\u015far\u0131s\u0131 y\u00fckseldi fakat d\u00f6rt ila alt\u0131 g\u00f6r\u00fcnt\u00fc sonras\u0131nda modelin verimlili\u011fi doygunlu\u011fa ula\u015ft\u0131. Bu, fazla veri y\u00fcklemesinin ek fayda sa\u011flamad\u0131\u011f\u0131n\u0131, optimal d\u0131\u015f s\u0131n\u0131rlar\u0131n bulunabilece\u011fini g\u00f6sterdi.<\/p>\n
Bu geli\u015fme sadece algoritman\u0131n teknik ba\u015far\u0131s\u0131n\u0131 ortaya koymakla kalmad\u0131; ayn\u0131 zamanda klinik uygulamalara da \u00f6nemli katk\u0131lar sa\u011flamay\u0131 hedefliyor. Model, d\u00fc\u015f\u00fck riskli hastalar\u0131n gereksiz g\u00f6r\u00fcnt\u00fclemelerden kaynaklanan maliyet ve stres y\u00fck\u00fcn\u00fc azaltma, y\u00fcksek riskli bireylerin ise erken ve hedefe y\u00f6nelik ek tedavilere y\u00f6nlendirilmesi a\u00e7\u0131s\u0131ndan yeni bir perspektif sunuyor. B\u00f6ylece daha ki\u015fiselle\u015ftirilmi\u015f bir takip ve tedavi stratejisi m\u00fcmk\u00fcn hale geliyor, bu da hastalar\u0131n ya\u015fam kalitesini ve genel hayatta kal\u0131m oranlar\u0131n\u0131 olumlu y\u00f6nde etkileyebilir.<\/p>\n
Yine de ara\u015ft\u0131rmac\u0131lar sonu\u00e7lar\u0131n dikkatli yorumlanmas\u0131 gerekti\u011fini vurguluyor. Modelin farkl\u0131 klinik ortamlarda yeniden test edilerek ge\u00e7erlili\u011finin ve genellenebilirli\u011finin sa\u011flanmas\u0131 elzem. Ayr\u0131ca, yapay zeka destekli prototiplerin klinik s\u00fcre\u00e7lerde uygulanabilmesi i\u00e7in ileriye d\u00f6n\u00fck \u00e7al\u0131\u015fmalara, klinik denemelere ihtiya\u00e7 s\u00f6z konusu. Bu s\u00fcre\u00e7lerde, yapay zekan\u0131n hasta y\u00f6netimini ve tedavi kararlar\u0131n\u0131 somut anlamda iyile\u015ftirip iyile\u015ftirmedi\u011fi netle\u015febilecek.<\/p>\n
Bu \u00e7al\u0131\u015fma, medikal g\u00f6r\u00fcnt\u00fcleme alan\u0131nda sabit g\u00f6r\u00fcnt\u00fclerin \u00f6tesine ge\u00e7erek zaman boyutunu etkin \u015fekilde kullanan bir metodoloji de\u011fi\u015fimine i\u015faret ediyor. Sadece n\u00f6ro-onkoloji de\u011fil, seri g\u00f6r\u00fcnt\u00fc takibinin standart oldu\u011fu kardiyoloji, muskuloskeletal gibi di\u011fer t\u0131p alanlar\u0131nda da benzer yapay zeka uygulamalar\u0131n\u0131n geli\u015ftirilmesi bekleniyor. Zaman serileri sayesinde hastal\u0131klar\u0131n evrimi ve tedaviye yan\u0131t daha ince ayr\u0131nt\u0131larla izlenebiliyor, dolay\u0131s\u0131yla klinik m\u00fcdahaleler \u00e7ok daha hassas ve proaktif hale getirilebiliyor.<\/p>\n
Ara\u015ft\u0131rma ekibi, yapay zeka uzmanlar\u0131, radyologlar, onkologlar ve veri bilimcilerinden olu\u015fan \u00e7ok disiplinli i\u015f birli\u011fiyle bu projeyi hayata ge\u00e7irdi. Ulusal Kanser Enstit\u00fcs\u00fc ve Botha-Chan Low Grade Glioma Consortium gibi kurumlar\u0131n finansal desteklerinin yan\u0131 s\u0131ra, Children\u2019s Brain Tumor Network\u2019\u00fcn veri eri\u015fim olanaklar\u0131 da ara\u015ft\u0131rman\u0131n ba\u015far\u0131ya ula\u015fmas\u0131nda kritik rol oynad\u0131. Buna benzer a\u00e7\u0131k veri i\u015f birliklerinin nadir hastal\u0131k \u00e7al\u0131\u015fmalar\u0131nda giderek \u00f6nem kazand\u0131\u011f\u0131 belirtilebilir.<\/p>\n
Gelecekte, zaman serisi derin \u00f6\u011frenme modellerinin klinik radyoloji platformlar\u0131na entegre edilmesiyle hekimlerin bilgi birikimi ve karar s\u00fcre\u00e7lerinin desteklenmesi hedefleniyor. Ancak bu entegrasyon s\u00fcre\u00e7leri, yaz\u0131l\u0131m uyumlulu\u011fu, klinisyen e\u011fitimi ve reg\u00fclasyon gibi zorluklar\u0131 i\u00e7eriyor. Yine de bu \u00e7al\u0131\u015fmalar, pediatrik n\u00f6ro-onkolojide ki\u015fiye \u00f6zel tedavi ve izlem se\u00e7eneklerini art\u0131rmaya y\u00f6nelik \u00f6nemli bir ad\u0131m\u0131 simgeliyor.<\/p>\n
Dr. Kann\u2019\u0131n s\u00f6zleriyle, yapay zekan\u0131n pe\u015f pe\u015fe \u00e7ekilen g\u00f6r\u00fcnt\u00fcleri analiz ederek yapt\u0131\u011f\u0131 \u00f6ng\u00f6r\u00fcler, t\u0131p alan\u0131nda yeni ufuklar a\u00e7\u0131yor. Pediatrik gliomalar\u0131n \u00f6tesinde, bu yakla\u015f\u0131m hastal\u0131klar\u0131n takibinde ve y\u00f6netiminde devrim yaratma potansiyeline sahip. Disiplinler aras\u0131 i\u015f birli\u011fi ve dikkatli klinik validasyon yakla\u015f\u0131mlar\u0131yla, zaman serisi derin \u00f6\u011frenmenin hasta bak\u0131m\u0131n\u0131 d\u00f6n\u00fc\u015ft\u00fcrmesi yak\u0131n ve umut verici bir gelecek vaad ediyor.<\/p>\n
—<\/p>\n
Ara\u015ft\u0131rma Konusu<\/strong>: People Son y\u0131llarda yapay zeka (YZ), t\u0131p alan\u0131nda devrim yaratmaya devam ediyor. \u00d6zellikle karma\u015f\u0131k t\u0131bbi g\u00f6r\u00fcnt\u00fclerin yorumlanmas\u0131nda sa\u011flad\u0131\u011f\u0131 y\u00fcksek kapasite, te\u015fhis s\u00fcre\u00e7lerini \u00e7ok daha h\u0131zl\u0131 ve do\u011fru hale getiriyor. Pediatrik glioma gibi \u00e7ocukluk \u00e7a\u011f\u0131 beyin t\u00fcm\u00f6rlerinde n\u00fcks riskini tahmin etmek ise, g\u00fcn\u00fcm\u00fcz t\u0131bbi uygulamalar\u0131nda halen ciddi zorluklar bar\u0131nd\u0131r\u0131yor. Bu ba\u011flamda Mass General Brigham b\u00fcnyesindeki ara\u015ft\u0131rmac\u0131lar, Boston…<\/p>\n","protected":false},"author":1,"featured_media":3759,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","_yoast_wpseo_focuskw":"","rank_math_title":"","rank_math_description":"","rank_math_focus_keyword":"","_wpan_schema_json_ld":"","_wpan_ai_seo_metadata":"","_wpan_ai_seo_status":"","_wpan_ai_seo_policy":"","_wpan_ai_seo_faq_block":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[28],"tags":[2927,2928,2930,2929,2926],"tmauthors":[],"class_list":{"0":"post-3758","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-kanser","8":"tag-cocuk-gliomasinda-zaman-serisi-mr-goruntu-analizi","9":"tag-derin-ogrenme-modelleri-pediatrik-beyin-tumorlerinde","10":"tag-pediatrik-beyin-tumorlerinde-makine-ogrenmesi-uygulamalari","11":"tag-post-operatif-mr-takiplerinde-yapay-zeka-kullanimi","12":"tag-yapay-zeka-ile-pediatrik-beyin-tumoru-nuksu-tahmini"},"jetpack_featured_media_url":"https:\/\/haber360.com\/wp-content\/uploads\/2025\/04\/Yapay-Zeka-ile-Pediatrik-Beyin-Tumoru-Nuksu-Tahmini-1745502268.jpg","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/haber360.com\/index.php\/wp-json\/wp\/v2\/posts\/3758","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/haber360.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/haber360.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/haber360.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/haber360.com\/index.php\/wp-json\/wp\/v2\/comments?post=3758"}],"version-history":[{"count":0,"href":"https:\/\/haber360.com\/index.php\/wp-json\/wp\/v2\/posts\/3758\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/haber360.com\/index.php\/wp-json\/wp\/v2\/media\/3759"}],"wp:attachment":[{"href":"https:\/\/haber360.com\/index.php\/wp-json\/wp\/v2\/media?parent=3758"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/haber360.com\/index.php\/wp-json\/wp\/v2\/categories?post=3758"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/haber360.com\/index.php\/wp-json\/wp\/v2\/tags?post=3758"},{"taxonomy":"tmauthors","embeddable":true,"href":"https:\/\/haber360.com\/index.php\/wp-json\/wp\/v2\/tmauthors?post=3758"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nMakale Ba\u015fl\u0131\u011f\u0131<\/strong>: Longitudinal Risk Prediction for Pediatric Glioma with Temporal Deep Learning
\nHaberin Yay\u0131n Tarihi<\/strong>: 24-Apr-2025
\nWeb References<\/strong>: https:\/\/doi.org\/10.1056\/AIoa2400703
\nDoi Referans<\/strong>: Tak, D et al. \u201cLongitudinal risk prediction for pediatric glioma with temporal deep learning.\u201d NEJM AI DOI: 10.1056\/AIoa2400703
\nAnahtar Kelimeler<\/strong>: Gliomas, Neuroimaging, Brain cancer, Cancer research, Deep learning<\/p>\n","protected":false},"excerpt":{"rendered":"