ReviewSensors (Basel, Switzerland)2025
Artificial Intelligence and Machine Learning in Optical Fiber Sensors: A Review.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Physics-Guided CNN Detection of Crack-Associated Events from Embedded Fiber Bragg Grating Sensors.Sensors (Basel, Switzerland) · 2026Article
- Review of Optical Fiber and Integrated Photonic Sensors for Industry and Smart Manufacturing: Technologies, Applications, Structural Health Monitoring and AI-Enabled Sensing.Sensors (Basel, Switzerland) · 2026Review
- Magnetometry for Agriculture and Animal Systems: From Classical Sensors to Quantum-Enabled Biosensing.Biosensors · 2026Review
- Overview of Optical Fiber Biosensors for the Detection of Cancer Biomarkers in the Past Decade.ACS omega · 2026Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI) with optical fiber sensing (OFS) is transforming the capabilities of modern sensing systems, enabling smarter, more adaptive, and higher-performance solutions across diverse applications. This paper presents a comprehensive review of AI-enhanced OFS technologies, encompassing both localized sensors such as fiber Bragg gratings (FBG), Fabry-Perot (FP) interferometers, and Mach-Zehnder interferometers (MZI), and distributed sensing systems based on Rayleigh, Brillouin, and Raman scattering. A wide range of AI algorithms are discussed, including supervised learning, unsupervised learning, reinforcement learning, and deep neural architectures. The applications of AI in OFS were discussed. AI has been employed to enhance sensor design, optimize interrogation systems, and adaptively tune configurations, as well as to interpret complex sensor outputs for tasks like denoising, classification, event detection, and failure forecasting.
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.