ReviewBiosensors2023
Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis.
Review in Biosensors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 53 citations in OpenAlex.
- Combined Use of Microwave Sensing Technologies and Artificial Intelligence for Biomedical Monitoring and Imaging.Biosensors · 2026Review
- Glioma chemotherapeutic resistance is tied to membrane electrophysiological properties and glycosylation.Bioengineering & translational medicine · 2026Article
- A hybrid bio inspired neural model based on Ropalidia Marginata behavior for multi disease classification.Scientific reports · 2025Article
- Recent Advances and Unaddressed Challenges in Biomimetic Olfactory- and Taste-Based Biosensors: Moving Towards Integrated, AI-Powered, and Market-Ready Sensing Systems.Sensors (Basel, Switzerland) · 2025Review
- Artificial Intelligence-Assisted Nanosensors for Clinical Diagnostics: Current Advances and Future Prospects.Biosensors · 2025Review
- Accuracy Enhancement in Refractive Index Sensing via Full-Spectrum Machine Learning Modeling.Biosensors · 2025Article
- Machine Learning Enabled Multidimensional Data Utilization Through Multi-Resonance Architecture: A Pathway to Enhanced Accuracy in Biosensing.ACS omega · 2025Article
- Improving the Accuracy of a Wearable Uroflowmeter for Incontinence Monitoring Under Dynamic Conditions: Leveraging Machine Learning Methods.Biosensors · 2025Article
- Quantifying and Controlling DNA Probe Density on the Surface of Silicon Nitride Optical Waveguides.Langmuir : the ACS journal of surfaces and colloids · 2025Article
- Biosensors, Artificial Intelligence Biosensors, False Results and Novel Future Perspectives.Diagnostics (Basel, Switzerland) · 2025Review
- Graphene metasurfaces biosensor for COVID-19 detection in the infra-red regime.Scientific reports · 2025Article
- Recent Advances in SAW Sensors for Detection of Cancer Biomarkers.Biosensors · 2025Review
- Article
- Microfluidic Assays for CD4 T Lymphocyte Counting: A Review.Biosensors · 2025Review
- AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring.Biosensors · 2024Review
- Integrating optical and electrical sensing with machine learning for advanced particle characterization.Biomedical microdevices · 2024Article
- Advancements of paper-based sensors for antibiotic-resistant bacterial species identification.Npj biosensing · 2024Review
- Advances in Microbial Diagnostics: Machine Learning and Nanotechnology for Zoonotic Disease Control.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
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 1 institution in 1 country.
Funding
Abstract
Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.
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.