ultrasound

AI that translates ultrasound videos to fill data gaps

Kari Jaaskelainen

06 Nov 2025 — 1 min read

Ultrasound comes in different "flavors" (grayscale and color flow Doppler), but clinical datasets rarely have both for every view. A new study trains a generative AI to "translate" between them—creating realistic videos to fill the gaps.

Trained on 54,975 videos and tested on 8,368, the model produces synthetic clips that look and behave like the real thing. Doctors could not reliably tell them apart (~54% accuracy, i.e., chance), and AI models for classification and segmentation performed similarly on real vs synthetic data (F1 ~0.9; Dice 0.97). Even though it was trained on heart scans, it generalized to other ultrasound domains.

High visual similarity to ground truth (SSIM ~0.91).
Can help balance imbalanced datasets and reduce missing data.
Boosts the value of existing, retrospective imaging.

Why it matters: more complete datasets can mean stronger, fairer medical AI—without extra scanning. Paper: http://arxiv.org/abs/2511.03255v1

Paper: http://arxiv.org/abs/2511.03255v1

Register: https://www.AiFeta.com

ultrasound AI medicalimaging deepLearning generativeAI healthcare cardiology radiology dataaugmentation

Automating GDPR Compliance: A Roadmap for Companies and Law Firms

GDPR compliance is more than checkboxes. A new roadmap from the Privatech project shows how automation and machine learning can help companies and law firms assess—and even generate—privacy compliance. * Shift the focus to data processors’ real workflows: drafting policies, mapping data uses, documenting decisions. * Break compliance into machine-ready

FPGAs for Faster, Leaner Deep Learning: A Review of CNN Accelerators

Deep learning drives image search, robots, and medical scans. Most systems lean on CPUs and GPUs. This review asks: what if we run convolutional neural networks (CNNs) on FPGAs—reconfigurable chips you can tailor to the model? * Why FPGAs: custom dataflows, low latency, and strong energy efficiency—great for cameras,

Dynamic-K: Recommendations That Know When to Stop

Most apps show a fixed number of “top” items—say 10 movies or 20 products—assuming there are always enough good options. But that’s not always true: sometimes there are few relevant items, or some users are extra picky. The result? Filler recommendations. Dynamic-K flips the script. Instead of

Teaching chatbots to stop contradicting themselves (DECODE)

Teaching chatbots to stop contradicting themselves Ever had a bot say one thing, then the opposite a few turns later? This study introduces DECODE—a new task and dataset for spotting contradictions in everyday conversations, drawn from both human-human and human-bot chats. * New data beats existing natural language inference (NLI)

Read more

Automating GDPR Compliance: A Roadmap for Companies and Law Firms

FPGAs for Faster, Leaner Deep Learning: A Review of CNN Accelerators

Dynamic-K: Recommendations That Know When to Stop

Teaching chatbots to stop contradicting themselves (DECODE)