ReviewFrontiers in bioengineering and biotechnology2022
Advances of deep learning in electrical impedance tomography image reconstruction.
Review in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Advances in Wearable Bioimaging.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Applications of bioimpedance measurement in clinical and allied fields: a comprehensive review.Biomedical engineering online · 2026Review
- A Data-Constrained and Physics-Guided Conditional Diffusion Model for Electrical Impedance Tomography Image Reconstruction.Sensors (Basel, Switzerland) · 2026Article
- Wearable and Implantable Devices for Continuous Monitoring of Muscle Physiological Activity: A Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Diagnostic accuracy and cost-efficiency of ChatGPT in EIT-based pendelluft detection for mechanically ventilated patients: a multicenter study.Respiratory research · 2026Article
- Role of artificial intelligence in enhancing mechanical ventilation - A peek into the future.Indian journal of anaesthesia · 2025Article
- Application of Electrical Impedance Tomography in Diagnosis of Cancer.Advanced biomedical research · 2025Review
- Electrical Impedance Tomography, Artificial Intelligence, and Variable Ventilation: Transforming Respiratory Monitoring and Treatment in Critical Care.Journal of personalized medicine · 2024Review
- Layered Fusion Reconstruction Based on Fuzzy Features for Multi-Conductivity Electrical Impedance Tomography.Sensors (Basel, Switzerland) · 2024Article
- Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration Separator.Sensors (Basel, Switzerland) · 2023Article
- Image Reconstruction Using Supervised Learning in Wearable Electrical Impedance Tomography of the Thorax.Sensors (Basel, Switzerland) · 2023Article
- Exploratory study of a multifrequency EIT-based method for detecting intracranial abnormalities.Frontiers in neurology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electrical impedance tomography (EIT) has been widely used in biomedical research because of its advantages of real-time imaging and nature of being non-invasive and radiation-free. Additionally, it can reconstruct the distribution or changes in electrical properties in the sensing area. Recently, with the significant advancements in the use of deep learning in intelligent medical imaging, EIT image reconstruction based on deep learning has received considerable attention. This study introduces the basic principles of EIT and summarizes the application progress of deep learning in EIT image reconstruction with regards to three aspects: a single network reconstruction, deep learning combined with traditional algorithm reconstruction, and multiple network hybrid reconstruction. In future, optimizing the datasets may be the main challenge in applying deep learning for EIT image reconstruction. Adopting a better network structure, focusing on the joint reconstruction of EIT and traditional algorithms, and using multimodal deep learning-based EIT may be the solution to existing problems. In general, deep learning offers a fresh approach for improving the performance of EIT image reconstruction and could be the foundation for building an intelligent integrated EIT diagnostic system in the future.
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.