ReviewHepatology international2019
Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology.
Review in Hepatology international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 38 citations in OpenAlex.
- Comparison of breast ultrasound image classification accuracy between convolutional neural networks and human experts using multicenter external validation cohort data.Journal of medical ultrasonics (2001) · 2026Article
- Advancements in Artificial Intelligence-Enhanced Imaging Diagnostics for the Management of Liver Disease-Applications and Challenges in Personalized Care.Bioengineering (Basel, Switzerland) · 2024Review
- Current status and future perspectives of contrast-enhanced ultrasound diagnosis of breast lesions.Journal of medical ultrasonics (2001) · 2024Review
- Artifact reduction in photoacoustic images by generating virtual dense array sensor from hemispheric sparse array sensor using deep learning.Journal of medical ultrasonics (2001) · 2024Article
- Computerized Diagnosis of Liver Tumors From CT Scans Using a Deep Neural Network Approach.IEEE journal of biomedical and health informatics · 2023Article
- A study on the optimal condition of ground truth area for liver tumor detection in ultrasound images using deep learning.Journal of medical ultrasonics (2001) · 2023Article
- Improving artificial intelligence pipeline for liver malignancy diagnosis using ultrasound images and video frames.Briefings in bioinformatics · 2023Article
- Artificial intelligence models for the diagnosis and management of liver diseases.Ultrasonography (Seoul, Korea) · 2023Article
- Quantitative analysis of artificial intelligence on liver cancer: A bibliometric analysis.Frontiers in oncology · 2023Article
- Application of endoscopic ultrasonography for detecting esophageal lesions based on convolutional neural network.World journal of gastroenterology · 2022Article
- Artificial intelligence (AI) models for the ultrasonographic diagnosis of liver tumors and comparison of diagnostic accuracies between AI and human experts.Journal of gastroenterology · 2022Article
- Ultrasound of Fetal Cardiac Function Changes in Pregnancy-Induced Hypertension Syndrome.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- Research progress and hotspot of the artificial intelligence application in the ultrasound during 2011-2021: A bibliometric analysis.Frontiers in public health · 2022Article
- A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology.Diagnostics (Basel, Switzerland) · 2021Review
- The overview of the deep learning integrated into the medical imaging of liver: a review.Hepatology international · 2021Review
- Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images.PloS one · 2021Article
- Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor.Frontiers in oncology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
An ultrasound (US) examination is a common noninvasive technique widely applied for diagnosis of a variety of diseases. Based on the rapid development of US equipment, many US images have been accumulated and are now available and ready for the preparation of a database for the development of computer-aided US diagnosis with deep learning technology. On the contrary, because of the unique characteristics of the US image, there could be some issues that need to be resolved for the establishment of computer-aided diagnosis (CAD) system in this field. For example, compared to the other modalities, the quality of a US image is, currently, highly operator dependent; the conditions of examination should also directly affect the quality of US images. So far, these factors have hampered the application of deep learning-based technology in the field of US diagnosis. However, the development of CAD and US technologies will contribute to an increase in diagnostic quality, facilitate the development of remote medicine, and reduce the costs in the national health care through the early diagnosis of diseases. From this point of view, it may have a large enough potential to induce a paradigm shift in the field of US imaging and diagnosis of liver diseases.
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.