ReviewSensors (Basel, Switzerland)2022
Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- High-Frequency Ultrasound-Guided Treatment of a Head and Neck Lymphatic Malformation.Healthcare (Basel, Switzerland) · 2026Article
- Quantum-edge synergy for dynamic acoustic compensation in ultrasound safety: a narrative review of tissue heterogeneity challenges.Frontiers in medical technology · 2026Review
- Research progress in computer-aided diagnosis systems for lung cancer.NPJ digital medicine · 2025Review
- The usefulness of automated high frequency ultrasound image analysis in atopic dermatitis staging.Scientific reports · 2025Article
- Automatic Assessment of AK Stage Based on Dermatoscopic and HFUS Imaging-A Preliminary Study.Journal of clinical medicine · 2024Article
- FPGA Implementation of Image Registration Using Accelerated CNN.Sensors (Basel, Switzerland) · 2023Article
- AI-Assisted Ultrasound for the Early Diagnosis of Antibody-Negative Autoimmune Thyroiditis.Journal of multidisciplinary healthcare · 2023Review
- Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Over the last few decades, computer-aided diagnosis systems have become a part of clinical practice. They have the potential to assist clinicians in daily diagnostic tasks. The image processing techniques are fast, repeatable, and robust, which helps physicians to detect, classify, segment, and measure various structures. The recent rapid development of computer methods for high-frequency ultrasound image analysis opens up new diagnostic paths in dermatology, allergology, cosmetology, and aesthetic medicine. This paper, being the first in this area, presents a research overview of high-frequency ultrasound image processing techniques, which have the potential to be a part of computer-aided diagnosis systems. The reviewed methods are categorized concerning the application, utilized ultrasound device, and image data-processing type. We present the bridge between diagnostic needs and already developed solutions and discuss their limitations and future directions in high-frequency ultrasound image analysis. A search was conducted of the technical literature from 2005 to September 2022, and in total, 31 studies describing image processing methods were reviewed. The quantitative and qualitative analysis included 39 algorithms, which were selected as the most effective in this field. They were completed by 20 medical papers and define the needs and opportunities for high-frequency ultrasound application and CAD development.
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.