ArticleUltrasonography (Seoul, Korea)2026
Direct deep learning analysis of three-dimensional automated breast ultrasound videos with reading mode optimization for breast cancer diagnosis.
Article in Ultrasonography (Seoul, Korea), 2026. 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.
- MMBCFNet: multi modal hybrid deep learning framework for breast cancer detection using MRI, mammography, and ultrasound images.Journal of ultrasound · 2026Article
- BI-RADS v2025 for breast ultrasound: key updates and Asian perspectives.Ultrasonography (Seoul, Korea) · 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
9 authors.
Funding
Abstract
purposeThis study aimed to develop and evaluate a deep learning model that directly analyzes three-dimensional automated breast ultrasound videos (DL-3DABUV) to assist breast cancer diagnosis, and to examine the optimal reading mode for clinical implementation.
methodsThis retrospective study included 547 patients (285 benign, 262 malignant), who were randomly assigned to a training set (n=437) and a test set (n=110). The DL-3DABUV model, built using ResNet50 and multi-instance learning, was trained by directly analyzing videos without image selection or manual annotation. Six radiologists (three experienced and three novice) evaluated the test set under three modes: independent-reading (without DL-3DABUV), second-reading (without prior knowledge of DL-3DABUV results), and concurrent-reading (after viewing DL-3DABUV results). The diagnostic performance of DL-3DABUV, experienced radiologists, and novice radiologists was compared. Reading times across the three modes were also assessed.
resultsCompared to experienced radiologists in independent reading, DL-3DABUV showed no significant differences in area under the receiver operating characteristic curve (AUC) (0.82 vs. 0.83), sensitivity (82.1% vs. 81.6%), or specificity (81.5% vs. 88.3%) (all P>0.05). DL-3DABUV exhibited higher AUC and specificity than novice radiologists in independent-reading (0.82 vs. 0.68, P<0.001; 81.5% vs. 57.4%, P<0.001). However, novice performance reached parity with DL-3DABUV in both second-reading and concurrent-reading. No significant differences in diagnostic performance were observed between second-reading and concurrent-reading. Concurrent reading significantly reduced reading time by 33.6 seconds compared with second-reading (P<0.001).
conclusionDL-3DABUV achieves diagnostic performance comparable to experienced radiologists and enhances diagnostic accuracy for novices. Concurrent reading provides a more efficient workflow by reducing reading time while maintaining diagnostic performance.
Indexed as
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.