ArticleJournal of healthcare engineering2021
Diagnostic Value of SonoVue Contrast-Enhanced Ultrasonography in Nipple Discharge Based on Artificial Intelligence.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. 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, 1 citations in OpenAlex.
- Retracted: Diagnostic Value of SonoVue Contrast-Enhanced Ultrasonography in Nipple Discharge Based on Artificial Intelligence.Journal of healthcare engineering · 2023Article
Corrections and comments
- Retraction · 2023-11-29Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper aims to explore the application value of SonoVue contrast-enhanced ultrasonography based on deep unsupervised learning (DNS) in the diagnosis of nipple discharge. In this paper, a new model (ODNS) is proposed based on the unsupervised learning model and stack self-coding network. The ultrasonic images of 1,725 patients with breast lesions in the shared database are used as the test data of the model. The differences in accuracy (Acc), recall (RE), sensitivity (Sen), and running time between the two models before and after optimization and other algorithms are compared. A total of 48 female patients with nipple discharge are enrolled. The differences in SE, specificity (SP), positive predictive value (PPV), and negative predictive value (NPV) of conventional ultrasound and contrast-enhanced ultrasonography are analyzed based on pathological examination results. The results showed that when the number of network layers is 5, the classification accuracies of DNS and ODNS model data reached the highest values, which were 91.45% and 98.64%, respectively.
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.