Evidence map›Paper›PMID 35476155›Full record

ArticleSurgical endoscopy2022

Real-time detection of the recurrent laryngeal nerve in thoracoscopic esophagectomy using artificial intelligence.

Kazuma Sato, Takeo Fujita, Hiroki Matsuzaki, Nobuyoshi Takeshita, Hisashi Fujiwara, Shuichi Mitsunaga, Takashi Kojima, Kensaku Mori, Hiroyuki Daiko

Erratum issuedAbstract read
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In one paragraph

Article in Surgical endoscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 2 pooled it
–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

29 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  7. Artificial intelligence in gastrointestinal surgery: A systematic review.World journal of gastrointestinal surgery · 2025
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  11. Artificial Intelligence Assurance in Head and Neck Surgery: Now and Next.Proceedings. IEEE International Symposium on Computer-Based Medical Systems · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Kazuma SatoDivision of Esophageal Surgery, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Takeo FujitaDivision of Esophageal Surgery, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan. takfujit@east.ncc.go.jp.ORCID http://orcid.org/0000-0002-6098-9813
Hiroki MatsuzakiSurgical Device Innovation Office, National Cancer Center Hospital East, Kashiwa, Chiba, Japan.
Nobuyoshi TakeshitaSurgical Device Innovation Office, National Cancer Center Hospital East, Kashiwa, Chiba, Japan.
Hisashi FujiwaraDivision of Esophageal Surgery, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Shuichi MitsunagaCourse of Advanced Clinical Research of Cancer, Juntendo University Graduate School of Medicine, Tokyo, 163-8001, Japan.
Takashi KojimaDivision of Gastrointestinal Oncology, National Cancer Center Hospital East, Kashiwa, Japan.
Kensaku MoriGraduate School of Informatics, Nagoya University School of Medicine, Nagoya, Aichi, Japan.
Hiroyuki DaikoDivision of Esophageal Surgery, National Cancer Center Hospital, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) has been largely investigated in the field of surgery, particularly in quality assurance. However, AI-guided navigation during surgery has not yet been put into practice because a sufficient level of performance has not been reached. We aimed to develop deep learning-based AI image processing software to identify the location of the recurrent laryngeal nerve during thoracoscopic esophagectomy and determine whether the incidence of recurrent laryngeal nerve paralysis is reduced using this software.

methodsMore than 3000 images extracted from 20 thoracoscopic esophagectomy videos and 40 images extracted from 8 thoracoscopic esophagectomy videos were annotated for identification of the recurrent laryngeal nerve. The Dice coefficient was used to assess the detection performance of the model and that of surgeons (specialized esophageal surgeons and certified general gastrointestinal surgeons). The performance was compared using a test set.

resultsThe average Dice coefficient of the AI model was 0.58. This was not significantly different from the Dice coefficient of the group of specialized esophageal surgeons (P = 0.26); however, it was significantly higher than that of the group of certified general gastrointestinal surgeons (P = 0.019).

conclusionsOur software's performance in identification of the recurrent laryngeal nerve was superior to that of general surgeons and almost reached that of specialized surgeons. Our software provides real-time identification and will be useful for thoracoscopic esophagectomy after further developments.

Indexed as

Esophageal NeoplasmsEsophagectomyArtificial IntelligenceHumansLymph Node ExcisionRecurrent Laryngeal NerveRetrospective StudiesArtificial intelligenceDeep learningEsophageal cancerEsophagectomyNavigation surgeryRecurrent laryngeal nerve

Identifiers

PMID35476155

What OpenQuestion holds

Textmetadata
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.