ArticleJournal of biophotonics2022
Computer-aided Veress needle guidance using endoscopic optical coherence tomography and convolutional neural networks.
Article in Journal of biophotonics, 2022. 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Deep learning fusion of multi-channel imaging from polarization-sensitive optical coherence tomography.Biomedical optics express · 2026Article
- Percutaneous nephrostomy guidance by a convolutional-neural-network-based optical coherence tomography endoscope.Communications engineering · 2026Article
- Integration of nested cross-validation, automated hyperparameter optimization, high-performance computing to reduce and quantify the variance of test performance estimation of deep learning models.Computer methods and programs in biomedicine · 2025Article
- Deep Learning for Autonomous Surgical Guidance Using 3-Dimensional Images From Forward-Viewing Endoscopic Optical Coherence Tomography.Journal of biophotonics · 2025Article
- Octascope: A Lightweight Pre-Trained Model for Optical Coherence Tomography.IEEE access : practical innovations, open solutions · 2025Article
- Automatic renal carcinoma biopsy guidance using forward-viewing endoscopic optical coherence tomography and deep learning.Communications engineering · 2024Article
- Dimension-based quantification of aging-associated cerebral microvasculature determined by optical coherence tomography and two-photon microscopy.Journal of biophotonics · 2024Article
- Enhancing epidural needle guidance using a polarization-sensitive optical coherence tomography probe with convolutional neural networks.Journal of biophotonics · 2024Article
- Automatic renal carcinoma biopsy guidance using forward-viewing endoscopic optical coherence tomography and deep learning.Research square · 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
12 authors.
Funding
Abstract
During laparoscopic surgery, the Veress needle is commonly used in pneumoperitoneum establishment. Precise placement of the Veress needle is still a challenge for the surgeon. In this study, a computer-aided endoscopic optical coherence tomography (OCT) system was developed to effectively and safely guide Veress needle insertion. This endoscopic system was tested by imaging subcutaneous fat, muscle, abdominal space, and the small intestine from swine samples to simulate the surgical process, including the situation with small intestine injury. Each tissue layer was visualized in OCT images with unique features and subsequently used to develop a system for automatic localization of the Veress needle tip by identifying tissue layers (or spaces) and estimating the needle-to-tissue distance. We used convolutional neural networks (CNNs) in automatic tissue classification and distance estimation. The average testing accuracy in tissue classification was 98.53 ± 0.39%, and the average testing relative error in distance estimation reached 4.42 ± 0.56% (36.09 ± 4.92 μm).
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.