AbVLM-Q: intelligent quality assessment for abdominal ultrasound standard planes via vision-language modeling.

ABSTRACT: Abdominal ultrasound is non-invasive and efficient, yet acquiring standard planes remains challenging due to operator dependency and procedural complexity. We propose AbVLM-Q, a vision-language framework for automated quality assessment of abdominal ultrasound standard planes. In this study, we assembled a multi-center dataset comprising 7,766 abdominal ultrasound scans, which were randomly divided into training (70%), validation (15%), and testing (15%) subsets. The proposed method, AbVLM-Q, was developed using a three-step approach: (1) hierarchical prompting that incorporates spatially aware querying and sequential reasoning; (2) a quantifiable scoring mechanism based on multi-level clinical penalty criteria; and (3) LoRA (Low-Rank Adaptation)-based fine-tuning of a pretrained vision-la

SUBMITTER: Wang B 

PROVIDER: S-EPMC12374393 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature