Keywords:
Artificial Intelligence, CT, Computer Applications-General, Outcomes
Authors:
J. Pan, K.-H. Nenning, S. Röhrich, N. Sverzellati, V. Poletti, J. Hofmanninger, A. Makropoulos, H. Prosch, G. Langs
DOI:
10.26044/ecr2023/C-14403
Conclusion
In conclusion, our study demonstrated that deep learning algorithms can be used to accurately quantify and analyze disease patterns in CT scans that are associated with individual outcomes of patients with IPF. Our findings suggest that both the static and dynamic pattern load profiles have significant correlations with survival, with honeycombing and consolidation patterns showing a strong association with patient outcomes. However, it is important to note that our study had some limitations, such as a small sample size. Therefore, further research with larger sample sizes is needed to confirm our findings and determine the clinical significance of these results. Overall, our study highlights the potential of using advanced deep learning algorithms to better understand and monitor the progression of IPF, potentially leading to more effective diagnosis and treatment of this challenging condition.