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Deep learning detection and classification of fungal and non-fungal calcifications on paranasal sinus CT imaging.

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Title: Deep learning detection and classification of fungal and non-fungal calcifications on paranasal sinus CT imaging.
Authors: Yang Z; Department of Computer Engineering, Soonchunhyang University, Asan, Republic of Korea.; Biomedical research center, Korea University Guro Hospital, Seoul, Republic of Korea., Choi I; Biomedical research center, Korea University Guro Hospital, Seoul, Republic of Korea.; Seers Technology Co. Ltd., Gyeonggi-do, Republic of Korea., Yun H; Biomedical research center, Korea University Guro Hospital, Seoul, Republic of Korea.; Korea University College of Medicine, Seoul, Republic of Korea., Kim S; Biomedical research center, Korea University Guro Hospital, Seoul, Republic of Korea.; Department of Electrical and Computer Engineering, College of Engineering, Seoul National University, Seoul, Republic of Korea., Jung HN; Korea University College of Medicine, Seoul, Republic of Korea.; Department of Radiology, Korea University Guro Hospital, Seoul, Republic of Korea., Suh S; Korea University College of Medicine, Seoul, Republic of Korea.; Department of Radiology, Korea University Guro Hospital, Seoul, Republic of Korea., Kim BK; Korea University College of Medicine, Seoul, Republic of Korea.; Department of Radiology, Korea University Anam Hospital, Seoul, Republic of Korea., Kim B; Korea University College of Medicine, Seoul, Republic of Korea.; Department of Radiology, Korea University Anam Hospital, Seoul, Republic of Korea., You SH; Korea University College of Medicine, Seoul, Republic of Korea.; Department of Radiology, Korea University Anam Hospital, Seoul, Republic of Korea., Ryoo I; Korea University College of Medicine, Seoul, Republic of Korea.; Department of Radiology, Korea University Guro Hospital, Seoul, Republic of Korea.
Source: PloS one [PLoS One] 2026 Jan 20; Vol. 21 (1), pp. e0340832. Date of Electronic Publication: 2026 Jan 20 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1932-6203
DOI:10.1371/journal.pone.0340832