Effectiveness and clinical impact of using deep learning for first-trimester fetal ultrasound image quality auditing

Abstract Background Regular auditing of ultrasound images is required to maintain quality; however, manual auditing is time-consuming and can be inconsistent. We therefore aimed to develop and validate an artificial intelligence-based image quality audit (AI-IQA) system to audit images from the four...

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Xehetasun bibliografikoak
Egile Nagusiak:	Xiaoyan Cao, Binghan Li, Yongsong Zhou, Yan Cao, Xin Yang, Xindi Hu, Chaoyu Chen, Shaokao Zhu, Hengli Lin, Tao Wang, Yuling Yan, Tao Tan, Lin Wang, Dong Ni
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	BMC 2025-03-01
Saila:	BMC Pregnancy and Childbirth
Gaiak:	Prenatal ultrasound First-trimester scanning Image quality control Artificial intelligence Deep learning
Sarrera elektronikoa:	https://doi.org/10.1186/s12884-025-07485-4
Etiketak:	Etiketa erantsi Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!

Effectiveness and clinical impact of using deep learning for first-trimester fetal ultrasound image quality auditing

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