ReviewPhotoacoustics2021
Deep learning for biomedical photoacoustic imaging: A review.
Review in Photoacoustics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 123 papers, 1 of them a synthesis that pooled it.
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
123 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Photoacoustic imaging for cutaneous melanoma assessment: a comprehensive review.Journal of biomedical optics · 2024Pooled it
- Non-invasive optoacoustic imaging of glycogen-storage and muscle degeneration in late-onset Pompe disease.Nature communications · 2024Trial
- In-silico investigation into optimal photoacoustic probe design using a digital thyroid phantom.Journal of biomedical optics · 2027Article
- Benchmarking deep learning-based reconstruction methods for photoacoustic computed tomography with clinically relevant synthetic datasets.Journal of biomedical optics · 2027Article
- Blind single image restoration for ring-array photoacoustic tomography system via physics-aided unsupervised learning.Photoacoustics · 2026Article
- Deep learning-driven quantitative spectroscopic photoacoustic imaging for segmentation and oxygen saturation estimation.Ultrasonics · 2026Article
- OpenQPAIData: Measurements of test objects with reference labels for quantitative photoacoustic imaging research.Scientific data · 2026Article
- PIT-Net: Physics-informed transformer with differentiable diffusion constraints for quantitative photoacoustic tomography of deep tissues.Photoacoustics · 2026Article
- Nanomaterials for In Vivo Photoacoustic Sensing: Emerging Strategies and Future Outlook.ACS sensors · 2026Review
- Three-dimensional aperture-driven photoacoustic tomography (3D-ADPAT).Science advances · 2026Article
- PhotIQA: A photoacoustic image data set with image quality ratings.Scientific data · 2026Article
- FM-Adapt: Foundation model adaptation with photoacoustic-supervised learning for interventional ultrasound.Photoacoustics · 2026Article
- Physics-consistent denoising of full-field photoacoustic displacement maps using an adaptive residual attention network.Photoacoustics · 2026Article
- Machine learning enhanced optical spectroscopy for breast cancer diagnosis: A review.Lasers in medical science · 2026Review
- Physics-driven deep learning photoacoustic tomography.Fundamental research · 2026Article
- Photoacoustic Imaging for Women's Gynecological Health: Advances and Clinical Prospects.Bioengineering (Basel, Switzerland) · 2026Review
- Deep-learning-Assisted Photoacoustic and Ultrasound Evaluation for Pre-transplant Human Liver Graft Quality and Transplant Suitability.medRxiv : the preprint server for health sciences · 2026Article
- Efficient denoising in LED-based optoacoustic tomography with squeeze-and-excitation deep convolutional networks.Journal of biomedical optics · 2026Article
- Predictive modeling of chronic foot ulcer outcomes using longitudinal photoacoustic imaging.Npj imaging · 2026Article
- Noninvasive photoacoustic computed mesoscopy for longitudinal brain imaging.Science advances · 2026Article
63 more citing papers are in PubMed but not listed here.
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
Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables spatially resolved imaging of optical tissue properties up to several centimeters deep in tissue, creating the potential for numerous exciting clinical applications. However, extraction of relevant tissue parameters from the raw data requires the solving of inverse image reconstruction problems, which have proven extremely difficult to solve. The application of deep learning methods has recently exploded in popularity, leading to impressive successes in the context of medical imaging and also finding first use in the field of PAI. Deep learning methods possess unique advantages that can facilitate the clinical translation of PAI, such as extremely fast computation times and the fact that they can be adapted to any given problem. In this review, we examine the current state of the art regarding deep learning in PAI and identify potential directions of research that will help to reach the goal of clinical applicability.
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.