Evidence map›Paper›PMID 35106412›Full record

ReviewAnnals of gastroenterological surgery2022

Artificial intelligence-based computer vision in surgery: Recent advances and future perspectives.

Daichi Kitaguchi, Nobuyoshi Takeshita, Hiro Hasegawa, Masaaki Ito

Abstract readReview
In one paragraph

Review in Annals of gastroenterological surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing 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

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

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Colour-Guided Surgery: A Conceptual Framework for Multimodal Intraoperative Visualisation-A Narrative Review.The international journal of medical robotics + computer assisted surgery : MRCAS · 2026
    Review
  6. Article
  7. Article
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  9. Plastic and Reconstructive Surgery in the Era of Artificial Intelligence.Indian journal of plastic surgery : official publication of the Association of Plastic Surgeons of India · 2026
    Article
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  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
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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

4 authors.

Daichi KitaguchiSurgical Device Innovation Office National Cancer Center Hospital East Kashiwa Japan.
Nobuyoshi TakeshitaSurgical Device Innovation Office National Cancer Center Hospital East Kashiwa Japan.
Hiro HasegawaSurgical Device Innovation Office National Cancer Center Hospital East Kashiwa Japan.
Masaaki ItoSurgical Device Innovation Office National Cancer Center Hospital East Kashiwa Japan.ORCID https://orcid.org/0000-0002-1101-2707

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Technology has advanced surgery, especially minimally invasive surgery (MIS), including laparoscopic surgery and robotic surgery. It has led to an increase in the number of technologies in the operating room. They can provide further information about a surgical procedure, e.g. instrument usage and trajectories. Among these surgery-related technologies, the amount of information extracted from a surgical video captured by an endoscope is especially great. Therefore, the automation of data analysis is essential in surgery to reduce the complexity of the data while maximizing its utility to enable new opportunities for research and development. Computer vision (CV) is the field of study that deals with how computers can understand digital images or videos and seeks to automate tasks that can be performed by the human visual system. Because this field deals with all the processes of real-world information acquisition by computers, the terminology "CV" is extensive, and ranges from hardware for image sensing to AI-based image recognition. AI-based image recognition for simple tasks, such as recognizing snapshots, has advanced and is comparable to humans in recent years. Although surgical video recognition is a more complex and challenging task, if we can effectively apply it to MIS, it leads to future surgical advancements, such as intraoperative decision-making support and image navigation surgery. Ultimately, automated surgery might be realized. In this article, we summarize the recent advances and future perspectives of AI-related research and development in the field of surgery.

Indexed as

artificial intelligencecomputer visiondecision‐makingmachine learningminimally invasive surgery

Identifiers

PMID35106412
PMCPMC8786689

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.