ArticleLaser & photonics reviews2021
Decoding Optical Data with Machine Learning.
Article in Laser & photonics reviews, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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Who cites it
9 citing papers in PubMed.
- Machine learning spectroscopy to advance computation and analysis.Chemical science · 2025Review
- Deep learning prediction of noise-driven nonlinear instabilities in fibre optics.Nature communications · 2025Article
- Seeing through arthropod eyes: An AI-assisted, biomimetic approach for high-resolution, multi-task imaging.Science advances · 2025Article
- Nested deep transfer learning for modeling of multilayer thin films.Advanced photonics · 2024Article
- A Reflectivity Enhanced 3D Optical Storage Nanostructure Application Based on Direct Laser Writing Lithography.Materials (Basel, Switzerland) · 2023Article
- Computational spectrometers enabled by nanophotonics and deep learning.Nanophotonics (Berlin, Germany) · 2022Review
- The impact of the COVID-19 pandemic on O-D flow and airport networks in the origin country and in Northeast Asia.Journal of air transport management · 2022Article
- Heat-Mediated Optical Manipulation.Chemical reviews · 2022Review
- Multimodal Orbital Angular Momentum Data Model Based on Mechanically Reconfigurable Arrays and Neural Networks.Computational intelligence and neuroscience · 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
4 authors.
Funding
Abstract
Optical spectroscopy and imaging techniques play important roles in many fields such as disease diagnosis, biological study, information technology, optical science, and materials science. Over the past decade, machine learning (ML) has proved promising in decoding complex data, enabling rapid and accurate analysis of optical spectra and images. This review aims to shed light on various ML algorithms for optical data analysis with a focus on their applications in a wide range of fields. The goal of this work is to sketch the validity of ML-based optical data decoding. The review concludes with an outlook on unaddressed problems and opportunities in this emerging subject that interfaces optics, data science and ML.
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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.