Computed tomography of interstitial lung disease in systemic sclerosis: dataset and deep learning model for pulmonary lesion segmentation
Deep learning-based automated segmentation can help quantify SSc-ILD lesions in HRCT images and provide clinically relevant information. The model's performance is comparable to previous studies, and the predicted lesion volumes correlate significantly with PFT metrics. This approach shows promise for aiding in SSc-ILD diagnosis, monitoring, and clinical decision-making, although further validation with larger datasets is needed.
Authors
- Farhad Gharibdoost (ORCID: https://orcid.org/0000-0002-0324-2609)
- Hoda Kavosi (ORCID: https://orcid.org/0000-0003-4762-6943)
- Aida Mohamadi (ORCID: https://orcid.org/0000-0002-6921-0108)
- Amir Mohammad Vahdani (ORCID: https://orcid.org/0000-0002-6196-1329)
- Sepehr Nayebirad
- Leila Aghaghazvini
- Elaheh Karimi (ORCID: https://orcid.org/0000-0002-7273-544X)
- Shayan Forghani
- Mehrnaz Asadi
- Zahra Tamartash
- Yousef Vojgani
Institutions
- Shariati Hospital (IR)
- Tehran University of Medical Sciences (IR)
Publication Details
- Journal
- Reumatismo
- Published
- 2026-02-25
- DOI
- https://doi.org/10.4081/reumatismo.2026.1920
- Primary Topic
- Systemic Sclerosis and Related Diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.00