Evidence map›Paper›PMID 38894486›Full record

ArticleSensors (Basel, Switzerland)2024

Investigation of Appropriate Scaling of Networks and Images for Convolutional Neural Network-Based Nerve Detection in Ultrasound-Guided Nerve Blocks.

Takaaki Sugino, Shinya Onogi, Rieko Oishi, Chie Hanayama, Satoki Inoue, Shinjiro Ishida, Yuhang Yao, Nobuhiro Ogasawara, Masahiro Murakawa, Yoshikazu Nakajima

Abstract read
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In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Takaaki SuginoDepartment of Biomedical Informatics, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo 101-0062, Japan.ORCID 0000-0002-4922-7375
Shinya OnogiDepartment of Biomedical Informatics, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo 101-0062, Japan.ORCID 0000-0002-0642-0511
Rieko OishiDepartment of Anesthesiology, Fukushima Medical University, Fukushima 960-1295, Japan.
Chie HanayamaDepartment of Anesthesiology, Fukushima Medical University, Fukushima 960-1295, Japan.
Satoki InoueDepartment of Anesthesiology, Fukushima Medical University, Fukushima 960-1295, Japan.ORCID 0000-0001-8746-3152
Shinjiro IshidaTCC Media Lab Co., Ltd., Tokyo 192-0152, Japan.
Yuhang YaoIOT SOFT Co., Ltd., Tokyo 103-0023, Japan.
Nobuhiro OgasawaraTCC Media Lab Co., Ltd., Tokyo 192-0152, Japan.
Masahiro MurakawaDepartment of Anesthesiology, Fukushima Medical University, Fukushima 960-1295, Japan.
Yoshikazu NakajimaDepartment of Biomedical Informatics, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo 101-0062, Japan.ORCID 0000-0003-3067-6290

Funding

Japan Society for the Promotion of Science KAKENHI JP23K11867"Subsidy for operating cost to promote practical development for area rehabilitation" by METI and Fukushima Prefecture Not availableThe Cooperative Research Project of Research Center for Biomedical Engineering 4069
6 · The paper itself

Abstract

Ultrasound imaging is an essential tool in anesthesiology, particularly for ultrasound-guided peripheral nerve blocks (US-PNBs). However, challenges such as speckle noise, acoustic shadows, and variability in nerve appearance complicate the accurate localization of nerve tissues. To address this issue, this study introduces a deep convolutional neural network (DCNN), specifically Scaled-YOLOv4, and investigates an appropriate network model and input image scaling for nerve detection on ultrasound images. Utilizing two datasets, a public dataset and an original dataset, we evaluated the effects of model scale and input image size on detection performance. Our findings reveal that smaller input images and larger model scales significantly improve detection accuracy. The optimal configuration of model size and input image size not only achieved high detection accuracy but also demonstrated real-time processing capabilities.

Indexed as

Nerve BlockNeural Networks, ComputerUltrasonographyHumansImage Processing, Computer-AssistedPeripheral NervesUltrasonography, Interventionalconvolutional neural networksnerve detectionultrasound-guided nerve block anesthesiaultrasound image sensing

Identifiers

PMID38894486
PMCPMC11175212

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Registered trials

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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.