ArticleNPJ digital medicine2025
On the public dissemination and open sourcing of ultrasound resources, datasets and deep learning models.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Breast Ultrasound AI Under Dataset Shift: A Patient-Leakage-Aware Benchmark.Diagnostics (Basel, Switzerland) · 2026Article
- Bridging the gap: methodological challenges and innovations in systematic reviews of machine learning models in healthcare.Frontiers in artificial intelligence · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Ultrasound data is relatively under-utilized in machine learning applied to medical imaging research when compared to other imaging modalities. Towards rectifying this, this paper (and the associated webpage) catalogs and assesses the usability of publicly available ultrasound datasets and models. Datasets were categorized and ranked using an original dataset quality score, SonoDQS. The models were scored using our model quality score, SonoMQS. We identified 72 public ultrasound datasets covering different anatomies and collected in different parts of the world. We identified 56 open-source models trained on ultrasound data. Most open-source models were trained on datasets that are or were made publicly available. A plurality of the datasets are of similar quality, corresponding to bronze (fifth tier) in the SonoDQS ranking. There are a few publicly available datasets of fetal content (5) and prostate anatomy (4) in spite of the wide use of ultrasound in these clinical areas, acknowledging a notable gap.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.