ArticleMedical physics2025
End-to-end deep learning approach for uterine artery-ovarian artery anastomosis detection from digital subtraction angiography sequences.
Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundUterine artery embolization (UAE) is a vital treatment modality for various gynecological conditions. However, complications may arise from the accidental migration of embolic agents into non-target tissues through the uterine artery-ovarian artery anastomosis (UOA). The key to addressing this issue lies in the timely detection of UOA, which is a challenging task. PURPOSE: This study aimed to develop an artificial intelligence model to detect UOA during these procedures.
methods990 uterine artery angiography sequences from 610 female patients who underwent UAE were retrospectively reviewed and were divided into training, validation, internal testing, and external testing cohorts. An end-to-end artificial intelligence model called UOA Detector (UOA-D) was proposed. The detection performance was evaluated using metrics such as the area under the receiver's operating characteristic curve (AUC), average precision (AP), average precision at intersection over union (IoU) threshold 0.5 (AP_0.5), sensitivity, specificity, accuracy, and F1 score. Moreover, the impact of UOA-D on enhancing the detection capabilities of six interventional radiologists, in terms of both performance and detection time, was examined across the internal and external testing sets.
resultsUOA-D manifested an AUC of 0.831 and 0.829 within the internal and external testing sets, with AP of 0.541 and 0.509, and AP_0.5 of 0.773 and 0.768, respectively. In the delineation of UOA-positive cases, UOA-D demonstrated sensitivity, specificity, accuracy, and F1 score of 0.978, 0.951, 0.961, and 0.978 in the internal testing set, and 0.969, 0.945, 0.959, and 0.962 in the external set, respectively. UOA-D significantly outperformed junior interventional radiologists and matched the performance of senior interventional radiologists in detecting UOA. The UOA-D-assisted strategy significantly improved the performance of junior radiologists and substantially shortened the detection time, demonstrating its potential clinical benefits.
conclusionsUOA-D demonstrated superior efficacy in detecting UOA through digital subtraction angiography (DSA) sequences, offering considerable advantages for clinical application.
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.