@ARTICLE{Becker2025:i8652,
	author="{Becker, Jarosław and Kolendo, Grzegorz and Becker, Aneta and Kawczyński, Sebastian}",
	title="{Artificial Intelligence–Assisted Diagnosis of Pulmonary Pathological Changes in Magnetic Resonance Imaging}",
	year="2025",
	type="artykuł naukowy / referat",
	language="en",
	journal="{Procedia Computer Science}",
	issn="1877-0509",
	number="270",
	pages="5270--5279",
	doi="10.1016/j.procs.2025.09.654",
	url="https://www.sciencedirect.com/science/article/pii/S1877050925033277",
	keywords="Artificial Intelligence (AI); Deep Learning (DL); Pulmonary MRI; Medical Image Segmentation; Hybrid U-Net Architecture",
	abstract="{This article presents a scientific contribution from Phase II of the “COVID FAST” Project, which aimed to develop an artificial intelligence (AI) system to support the diagnosis of post-COVID-19 pulmonary complications based on magnetic resonance imaging (MRI). The study reports the development of a hybrid deep neural network (DNN) model for the automatic segmentation of pathological changes in the lungs. The model integrates U-Net architectures with both multi-label and logits-based outputs, enhancing detection performance – particularly for rare and morphologically diverse pathological lesions. The model achieved high accuracy in identifying pulmonary abnormalities, with mean IoU values exceeding 90% for four relatively small and infrequent lesion types (e.g., adhesions/fibrosis); between 91% and 93.6% of test cases in each of these categories reached IoU > 70%. For the more common lesion type – ground-glass opacity – the target performance level was not achieved (62.8% of test cases with IoU > 70%; mean IoU = 77%). A detailed annotation analysis revealed low inter-observer agreement for ground-glass opacity (59.1% of cases with IoU > 70%), whereas the AI model achieved slightly higher consistency (62.8%). Furthermore, the analysis of false positive test cases (15%) for ground-glass opacity indicated that the model frequently identified lesions that had been overlooked by experts in the reference images. The study results highlight the potential of AI-assisted analysis in lung MRI interpretation and provide a strong rationale for continued research within the Project, as well as for the model’s future clinical applications in healthcare.}",
}