Public articles linked to the same research event.
medRxiv The study presents a sparsely supervised AI framework in which frozen ultrasound foundation model (USFM, ViT-B) embeddings of 768 dimensions per frame are passed to a lightweight bidirectional LSTM temporal head that outputs a 0–1 frame-quality score, trained with Gaussian soft labels that peak at expert-marked key frames and decay smoothly with frame distance (width σ = 5 frames); in subject-level five-fold cross-validation on 18 subjects and 323 ultrasound videos spanning nine controlled acquisition sweep types (about 45,500 frames), it selected a usable frame in 82.2% of sweeps containing key frames with a mean minimum distance of 3.07 frames, exceeding the strongest training-free baseline (USFM feature cosine similarity, 49.2%) and a hard-label model (71.9%).
The study presents a sparsely supervised AI framework in which frozen ultrasound foundation model (USFM, ViT-B) embeddings of 768 dimensions per frame are passed to a lightweight bidirectional LSTM temporal head that outputs a 0–1 frame-quality score, trained with Gaussian soft labels that peak at expert-marked key frames and decay smoothly with frame distance (width σ = 5 frames); in subject-level five-fold cross-validation on 18 subjects and 323 ultrasound videos spanning nine controlled acquisition sweep types (about 45,500 frames), it selected a usable frame in 82.2% of sweeps containing key frames with a mean minimum distance of 3.07 frames, exceeding the strongest training-free baseline (USFM feature cosine similarity, 49.2%) and a hard-label model (71.9%).
The study presents a sparsely supervised AI framework in which frozen ultrasound foundation model (USFM, ViT-B) embeddings of 768 dimensions per frame are passed to a lightweight bidirectional LSTM temporal head that outputs a 0–1 frame-quality score, trained with Gaussian soft labels that peak at expert-marked key frames and decay smoothly with frame distance (width σ = 5 frames); in subject-level five-fold cross-validation on 18 subjects and 323 ultrasound videos spanning nine controlled acquisition sweep types (about 45,500 frames), it selected a usable frame in 82.2% of sweeps containing key frames with a mean minimum distance of 3.07 frames, exceeding the strongest training-free baseline (USFM feature cosine similarity, 49.2%) and a hard-label model (71.9%).
The study presents a sparsely supervised AI framework in which frozen ultrasound foundation model (USFM, ViT-B) embeddings of 768 dimensions per frame are passed to a lightweight bidirectional LSTM temporal head that outputs a 0–1 frame-quality score, trained with Gaussian soft labels that peak at expert-marked key frames and decay smoothly with frame distance (width σ = 5 frames); in subject-level five-fold cross-validation on 18 subjects and 323 ultrasound videos spanning nine controlled acquisition sweep types (about 45,500 frames), it selected a usable frame in 82.2% of sweeps containing key frames with a mean minimum distance of 3.07 frames, exceeding the strongest training-free baseline (USFM feature cosine similarity, 49.2%) and a hard-label model (71.9%).