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Deep-learning based quantitative evaluation of postoperative atelectasis following right upper lobectomy.

Deep-learning based quantitative evaluation of postoperative atelectasis following right upper lobectomy. NPJ digital medicine Kamtam, D. N., Facchi, G. M., Lin, N., Tsai, L. L., Lui, N. S., Elliott, I. A., Liou, D. Z., Backhus, L. M., Berry, M. F., Guo, H. H., Langlotz, C. P., Shrager, J. B. 2026

Abstract

Existing methods of grading atelectasis are typically subjective and not scalable. We aimed to develop an automated, deep learning-based framework to quantify and grade postoperative atelectasis. We retrospectively included all patients who underwent RULobectomy from 2008 to 2023. We trained three nnU-Net v2 segmentation models for preoperative and postoperative lobes and airways with volumetric quantification of the right middle lobe (RML), right lower lobe (RLL), and total lung volume. Atelectasis severity in the RML was independently graded using a 5-point radiological scale (none, minimal, subsegmental, segmental, lobar). The association between volume metrics with atelectasis severity and clinical outcomes was evaluated. 236 patients comprised the study cohort. Median(IQR) RML volume loss progressively increased with higher atelectasis grades, from -4.6?mL (-78.5, 59.0) in grade 0 to -317.8?mL (-440.7, -194.8) in grade 4 atelectasis (p?

View details for DOI 10.1038/s41746-026-02683-6

View details for PubMedID 42062447