ArticleComputers in biology and medicine2025
Novel Lobe-based Transformer model (LobTe) to predict emphysema progression in Alpha-1 Antitrypsin Deficiency.
Article in Computers in biology and medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of Artificial Intelligence in Alpha-1 Antitrypsin Deficiency: A Systematic Review from a Respiratory Medicine Perspective.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Preclinical models of COPD: a state of the art review.The European respiratory journal · 2026Review
- Seeing is believing-on the utility of CT in phenotyping chronic obstructive pulmonary disease.The British journal of radiology · 2026Review
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2 authors.
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Abstract
Emphysema, marked by irreversible lung tissue destruction, poses challenges in progression prediction due to its heterogeneity. Early detection is particularly critical for patients with Alpha-1 Antitrypsin Deficiency (AATD), a genetic disorder reducing ATT protein levels. Heterozygous carriers (PiMS and PiMZ) have variable AAT levels thus complicating their prognosis. This study introduces a novel prognostic model, the Lobe-based Transformer encoder (LobTe), designed to predict the annual change in lung density (ΔALD [g/L-yr]) using CT scans. Utilizing a global self-attention mechanism, LobTe specifically analyzes lobar tissue destruction to forecast disease progression. In parallel, we developed and compared a second model utilizing an LSTM architecture that implements a local subject-specific attention mechanism. Our methodology was validated on a cohort of 2,019 participants from the COPDGene study. The LobTe model demonstrated a small root mean squared error (RMSE=1.73 g/L-yr) and a notable correlation coefficient (ρ=0.61), explaining over 35% of the variability in ΔALD (R
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