ReviewDiagnostics (Basel, Switzerland)2025
Machine Learning in Microwave Medical Imaging and Lesion Detection.
Review in Diagnostics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Machine Learning Classification of Axillary Lymph Nodes Using Microwave Signals.Sensors (Basel, Switzerland) · 2026Article
- Microwave technologies for biomedical diagnosis and therapy: advances, challenges, and perspectives.Mikrochimica acta · 2026Review
- A Multi-Input Neural Network for Microwave Hemorrhagic Stroke Identification Using Multimodal Data.Brain sciences · 2026Article
- Tumor Detection and Characterization Using Microwave Imaging Technique-An Experimental Calibration Approach.Sensors (Basel, Switzerland) · 2026Article
- Breast density classification using frequency-based features in microwave imaging.Scientific reports · 2025Article
- Conformal On-Body Antenna System Integrated with Deep Learning for Non-Invasive Breast Cancer Detection.Sensors (Basel, Switzerland) · 2025Article
- Machine learning-based DNA microarray analysis for disease detection using the MICRO-AI framework.Science progressArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) techniques have attracted many microwave researchers and engineers for their potential to improve performance in microwave- and millimeter-wave-based medical applications. This paper reviews ML algorithms, data acquisition, training techniques, and applications that have emerged in recent years. It also reviews state-of-the-art ML techniques applied for the detection of various organ diseases with microwave signals, achieving more successful results than using traditional methods alone, such as a higher diagnosis accuracy or spatial resolution and significantly improved efficiency. Challenges and the outlook of using ML in future microwave medical applications are also discussed.
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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.