Evidence map›Paper›PMID 34253767›Full record

ArticleScientific reports2021

Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy.

Julia Gong, F Christopher Holsinger, Julia E Noel, Sohei Mitani, Jeff Jopling, Nikita Bedi, Yoon Woo Koh, Lisa A Orloff, Claudio R Cernea, Serena Yeung

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.4field-weighted citation impact, top 11% of its field
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

16 citing papers in PubMed, 25 citations in OpenAlex.

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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 at 4 institutions in 4 countries.

Julia GongDepartment of Computer Science, Stanford University, Stanford, CA, USA.
F Christopher HolsingerDivision of Head and Neck Surgery, Department of Otolaryngology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA, 94305, USA. holsinger@stanford.edu.
Julia E NoelDivision of Head and Neck Surgery, Department of Otolaryngology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA, 94305, USA.
Sohei MitaniDivision of Head and Neck Surgery, Department of Otolaryngology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA, 94305, USA.
Jeff JoplingDepartment of Surgery, Stanford University, Stanford, CA, USA.
Nikita BediDivision of Head and Neck Surgery, Department of Otolaryngology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA, 94305, USA.
Yoon Woo KohDepartment of Head and Neck Surgery, Yonsei University School of Medicine, Seoul, Korea.
Lisa A OrloffDivision of Head and Neck Surgery, Department of Otolaryngology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA, 94305, USA.
Claudio R CerneaDepartment of Surgery, University of São Paulo Medical School, São Paulo, Brazil.
Serena YeungDepartment of Computer Science, Stanford University, Stanford, CA, USA. syyeung@stanford.edu.
Stanford University · USEhime University · JPUniversidade de São Paulo · BRYonsei University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Surgeons must visually distinguish soft-tissues, such as nerves, from surrounding anatomy to prevent complications and optimize patient outcomes. An accurate nerve segmentation and analysis tool could provide useful insight for surgical decision-making. Here, we present an end-to-end, automatic deep learning computer vision algorithm to segment and measure nerves. Unlike traditional medical imaging, our unconstrained setup with accessible handheld digital cameras, along with the unstructured open surgery scene, makes this task uniquely challenging. We investigate one common procedure, thyroidectomy, during which surgeons must avoid damaging the recurrent laryngeal nerve (RLN), which is responsible for human speech. We evaluate our segmentation algorithm on a diverse dataset across varied and challenging settings of operating room image capture, and show strong segmentation performance in the optimal image capture condition. This work lays the foundation for future research in real-time tissue discrimination and integration of accessible, intelligent tools into open surgery to provide actionable insights.

Indexed as

Deep LearningHumansRecurrent Laryngeal NerveThyroid DiseasesThyroidectomyThyroid Gland

Identifiers

PMID34253767
PMCPMC8275665
OpenAlexW3183095616

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.