Tiroid Nodüllerinin Klinik Yönetiminde “TİRONOD Karar-Destek Sistemi”

Year 2025, Volume: 9 Issue: 2, 193 - 204, 31.08.2025

İsmail Terzi , Taner Bayraktaroğlu , Erkan Çetiner

https://doi.org/10.29058/mjwbs.1617806

Abstract

Amaç: Bu makale, tiroid nodüllerinin değerlendirilmesinde hekimlere rehberlik etmek, tanı-tedavi süreçlerini optimize etmek ve klinik
uygulamaya entegrasyonunu sağlamak için oluşturulan TİRONOD-Karar Destek Sistemi'ni incelemek üzere tasarlanmıştır. Sistem, tiroid
nodüllerinin ayırıcı tanısına yenilikçi bir yaklaşım sunarak nihayetinde sağlık hizmetlerinin kalitesini artırmayı amaçlamaktadır.
Gereç ve Yöntemler: TİRONOD karar destek sisteminin mevcut durumu analiz edilecek ve yapay zeka ile makine öğrenmesi tabanlı bir
algoritma kullanılarak klinik süreçlere entegre edilecektir. Sistem; klinik verileri, ultrason görüntüleme bulgularını ve nodül biyopsilerinin
sitoloji sonuçlarını birleştirerek malignite riskini değerlendiren hassas ve öngörücü bir araç olarak tasarlanmıştır.
Bulgular: Sistemin, hasta yönetimi sırasında biyopsi gerekliliğinin belirlenmesine, gereksiz ameliyatların önlenmesine ve maliyet-etkinlik
analizlerinin yapılmasına katkıda bulunması beklenmektedir.
Sonuç: TİRONOD Karar Destek Sistemi, tiroid nodüllerinin ayırıcı tanısında yenilikçi bir yaklaşım sunarak sağlık hizmetlerinin kalitesini
artırmayı amaçlamaktadır. Sistemin hem ulusal hem de uluslararası düzeyde örnek bir model olması beklenmektedir

Keywords

Tiroid bezi , tiroid nodülü , ultrasonografi , İİAB , Bethesda Sistemi , sitoloji , karar destek sistemi , TİRONOD

“TIRONOD Decision-Support System” in the Clinical Management of Thyroid Nodules

Abstract

Aim: This article was designed to examine the TIRONOD-Decision Support System, which was created to guide physicians in the evaluation
of thyroid nodules, optimize diagnosis-treatment processes, and ensure its integration into clinical practice. The system ultimately aims to
increase the quality of healthcare services by offering an innovative approach to the differential diagnosis of thyroid nodules.
Material and Methods: The current status of the TIRONOD decision support system will be analyzed and integrated into clinical processes
with an artificial intelligence and machine learning-based algorithm. The system is designed as a sensitive and predictive tool for malignancy
risk assessment by combining clinical data, ultrasound imaging findings, and cytology results of nodule biopsies.
Results: The system is expected to contribute to determining the necessity of biopsies, preventing unnecessary surgeries, and conducting
cost-effectiveness analyses during patient management
Conclusion: The TIRONOD Decision Support System aims to increase the quality of healthcare services by offering an innovative approach
in the differential diagnosis of thyroid nodules. It is expected to be an exemplary model at both national and international levels.

Keywords

Thyroid gland , thyroidal nodules , ultrasonography , FNA , Bethesda System , cytology , decision support system , TIRONOD

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