論文種別	原著(症例報告除く)
言語種別	英語
査読の有無	査読あり
表題	Deep learning for pediatric synovial recess distension detection in hemophilia: synthetic image augmentation with styleGAN2-ADA.
掲載誌名	正式名：International journal of computer assisted radiology and surgery 略　 称：Int J Comput Assist Radiol Surg ISSNコード：18616429/18616410
掲載区分	国外
巻・号・頁	pp.Online ahead of print
著者・共著者	Noushin Jafarpisheh, Johannes Roth, Boris Hugle, Mauro Mendez, Karan Chahal, Azusa Nagao, Pascal N Tyrrell
発行年月	2026/04
概要	PURPOSE:Pediatric musculoskeletal ultrasound (MSKUS) datasets are scarce, especially for rare, sex-linked conditions such as hemophilia. Models trained on adult data have limited generalizability due to anatomical differences. We aimed to improve pediatric synovial recess distension (SRD) classification by augmenting real data with synthetic images.METHODS:We developed a tailored augmentation framework using conditional StyleGAN2-ADA to generate age- and diagnosis-specific synthetic ultrasound images (0-8, 9-13, 14-18 years; SRD-positive/negative). Our two-stage quality control pipeline (distance-based filtering and Support Vector Machine (SVM) confidence weighting) using task-specific EfficientNet-B4 embeddings ensured anatomical plausibility. The dataset-2,499 real pediatric and adult knee ultrasound images and 21,550 quality-controlled synthetic images-was used to fine-tune an EfficientNet-B4 classifier, evaluated on an independent pediatric test set of 278 images across four ablation configurations. A separate 3-class age classifier validated anatomical feature preservation. Statistical comparisons used McNemar's test, per-fold sign tests, and bootstrap confidence intervals.RESULTS:Our proposed model improved accuracy over the adult baseline by + 17.3 pp for ages 0-8 (85.3% vs. 68.0%, p < 0.001) and + 6.1 pp for ages 14-18 (89.9% vs. 83.8%, p = 0.033), with consistent gains across all five cross-validation folds (sign test p = 0.031). An independent age classifier confirmed that quality-controlled synthetic images preserved age-specific anatomical features (macro accuracy 0.844 vs. 0.644 real-only).CONCLUSION:Conditional StyleGAN2-ADA with two-stage quality control improved pediatric SRD classification and preserved age-specific anatomical relevance, supporting accurate, age-aware AI tools for rare pediatric conditions.
DOI	10.1007/s11548-026-03638-8
PMID	41986781