Radyoloji Yapay Zeka Çalışmalarında Veri Hazırlama Aşamaları
Özet
Tıbbi görüntüleme alanında yapay zeka (YZ) çalışmaları eksponansiyel bir hızla artmakta olup, bu teknolojilerin rutin klinik pratiğimize entegrasyonu artık kaçınılmaz bir gerçektir. Literatürdeki teorik performans başarılarının klinik sahaya güvenle aktarılabilmesi ve başarılı yapay zeka modellerinin geliştirilebilmesi için son derece ciddi, katı ve titiz bir metodolojik veri hazırlığı aşaması gerekmektedir. Çünkü modelin mimari karmaşıklığından ziyade, verinin kalitesi başarının temel belirleyicisidir ("Çöp girerse, çöp çıkar - GIGO" prensibi). Veri toplanması, anonimleştirme, matematiksel ön işleme, uyumlandırma ve etiketleme gibi aşamalar dışarıdan bakıldığında salt teknik ve bilgisayar mühendisliğine ait süreçler gibi görünse de; algoritmaya sunulan klinik "Referans Standart"ın oluşturulması ve verinin tıbbi doğruluğunun sağlanması doğrudan radyoloji uzmanının sorumluluğundadır. Bu nedenle, radyologların veri hazırlık sürecinin bizzat merkezinde bulunması klinik ve etik bir zorunluluktur. İlerleyen süreçte hastanelerde rutin kullanıma geçecek olan YZ modellerinin tanısal güvenilirliğini, dış dünya genellenebilirliğini ve olası sistematik yanlılıklarını denetleyebilmek; ancak hekimlerin bu veri hazırlama metodolojilerine, teknik parametrelere ve veri sızıntısı gibi metodolojik hatalara hakim olmasıyla mümkündür.
Referanslar
Diaz O, Kushibar K, Osuala R, Linardos A, Garrucho L, Igual L, et al. Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools. Phys Med. 2021;83:25–37. doi:10.1016/j.ejmp.2021.02.007
Najjar R. Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics. 2023;13(17):2760. doi:10.3390/diagnostics13172760
Willemink MJ, Koszek WA, Hardell C, Wu J, Fleischmann D, Harvey H, et al. Preparing Medical Imaging Data for Machine Learning. Radiology. 2020;295(1):4–15. doi:10.1148/radiol.2020192224
Kelly BS, Judge C, Bollard SM, Clifford SM, Healy GM, Aziz A, et al. Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE). Eur Radiol. 2022;32(11):7998–8007. doi:10.1007/s00330-022-08784-6
Langlotz CP. The Future of AI and Informatics in Radiology: 10 Predictions. Radiology. 2023;309(1):e231114. doi:10.1148/radiol.231114
Obuchowicz R, Lasek J, Wodziński M, Piórkowski A, Strzelecki M, Nurzynska K. Artificial Intelligence-Empowered Radiology—Current Status and Critical Review. Diagnostics. 2025;15(3):282. doi:10.3390/diagnostics15030282
Kohli M, Prevedello LM, Filice RW, Geis JR. Implementing Machine Learning in Radiology Practice and Research. Am J Roentgenol. 2017;208(4):754–60. doi:10.2214/AJR.16.17224
Galbusera F, Cina A. Image annotation and curation in radiology: an overview for machine learning practitioners. Eur Radiol Exp. 2024;8(1):11. doi:10.1186/s41747-023-00408-y
Kulkarni V, Gawali M, Kharat A. Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice. JMIR Med Inform. 2021;9(9):e28776. doi:10.2196/28776
Gichoya JW, Thomas K, Celi LA, Safdar N, Banerjee I, Banja JD, et al. AI pitfalls and what not to do: mitigating bias in AI. Br J Radiol. 2023;96(1150):20230023. doi:10.1259/bjr.20230023
Rockall AG, Shelmerdine SC, Chen M. AI and ML in radiology: Making progress. Clin Radiol. 2023;78(2):81–2. doi:10.1016/j.crad.2022.10.010
Masoudi S, Harmon SA, Mehralivand S, Walker SM, Raviprakash H, Bagci U, et al. Quick guide on radiology image pre-processing for deep learning applications in prostate cancer research. J Med Imaging. 2021;8(01). doi:10.1117/1.JMI.8.1.010901
Rouzrokh P, Khosravi B, Faghani S, Moassefi M, Vera Garcia DV, Singh Y, et al. Mitigating Bias in Radiology Machine Learning: 1. Data Handling. Radiol Artif Intell. 2022;4(5):e210290. doi:10.1148/ryai.210290
Tejani AS, Klontzas ME, Gatti AA, Mongan JT, Moy L, Park SH, et al. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update. Radiol Artif Intell. 2024;6(4):e240300. doi:10.1148/ryai.240300
Banerjee I, Bhattacharjee K, Burns JL, Trivedi H, Purkayastha S, Seyyed-Kalantari L, et al. “Shortcuts” Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation. J Am Coll Radiol. 2023;20(9):842–51. doi:10.1016/j.jacr.2023.06.025
Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012;30(9):1323–41. doi:10.1016/j.mri.2012.05.001
Candemir S, Nguyen XV, Folio LR, Prevedello LM. Training Strategies for Radiology Deep Learning Models in Data-limited Scenarios. Radiol Artif Intell. 2021;3(6):e210014. doi:10.1148/ryai.2021210014
Nakaura T, Higaki T, Awai K, Ikeda O, Yamashita Y. A primer for understanding radiology articles about machine learning and deep learning. Diagn Interv Imaging. 2020;101(12):765–70. doi:10.1016/j.diii.2020.10.001
Tejani AS, Retson TA, Moy L, Cook TS. Detecting Common Sources of AI Bias: Questions to Ask When Procuring an AI Solution. Radiology. 2023;307(3):e230580. doi:10.1148/radiol.230580
Tampu IE, Eklund A, Haj-Hosseini N. Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images. Sci Data. 2022;9(1):580. doi:10.1038/s41597-022-01618-6
Wang L. Breast Ultrasound AI Under Dataset Shift: A Patient-Leakage-Aware Benchmark. Diagnostics. 2026;16(10):1537. doi:10.3390/diagnostics16101537
Varoquaux G, Cheplygina V. Machine learning for medical imaging: methodological failures and recommendations for the future. Npj Digit Med. 2022;5(1):48. doi:10.1038/s41746-022-00592-y
Chae A, Yao MS, Sagreiya H, Goldberg AD, Chatterjee N, MacLean MT, et al. Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology. Radiology. 2024;310(1):e223170. doi:10.1148/radiol.223170
Sounderajah V, Guni A, Liu X, Collins GS, Karthikesalingam A, Markar SR, et al. The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence. Nat Med. 2025;31(10):3283–9. doi:10.1038/s41591-025-03953-8
Guo B, Lu D, Szumel G, Gui R, Wang T, Konz N, et al. The Impact of Scanner Domain Shift on Deep Learning Performance in Medical Imaging: an Experimental Study [Internet]. arXiv; 2024 [cited 2026 Jun 18]. Available from: http://arxiv.org/abs/2409.04368 doi:10.48550/arXiv.2409.04368
Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378. doi:10.1136/bmj-2023-078378
Nikolic B. Racial Bias Exacerbated through AI: An Example Using Chest Radiograph Models. Radiology. 2023;309(2):e232666. doi:10.1148/radiol.232666
Kompa B, Snoek J, Beam AL. Second opinion needed: communicating uncertainty in medical machine learning. Npj Digit Med. 2021;4(1):4. doi:10.1038/s41746-020-00367-3
Langlotz CP. Will Artificial Intelligence Replace Radiologists? Radiol Artif Intell. 2019;1(3):e190058. doi:10.1148/ryai.2019190058
