Evidence map›Paper›PMID 33311942›Full record

ArticleWorld journal of gastroenterology2020

Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery.

Sang-Ho Park, Hee-Min Park, Kwang-Ryul Baek, Hong-Min Ahn, In Young Lee, Gyung Mo Son

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

Article in World journal of gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed, 3 pooled it
3.6field-weighted citation impact, top 5% 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

58 citing papers in PubMed, 3 syntheses or guidelines pooled it, 79 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

6 authors at 1 institution in 1 country.

Sang-Ho ParkDepartment of Electronic Engineering, Pusan National University, Busan 46241, South Korea.
Hee-Min ParkDepartment of Electronic Engineering, Pusan National University, Busan 46241, South Korea.
Kwang-Ryul BaekDepartment of Electronic Engineering, Pusan National University, Busan 46241, South Korea.
Hong-Min AhnDepartment of Surgery, Pusan National University Yangsan Hospital, Gyeongsangnam-do 50612, South Korea.
In Young LeeDepartment of Medicine, Pusan National University, Gyeongsangnam-do 50612, South Korea.
Gyung Mo SonDepartment of Surgery, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan-si 50612, South Korea. skm1711@pusan.ac.kr.
Pusan National University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundColonic perfusion status can be assessed easily by indocyanine green (ICG) angiography to predict ischemia related anastomotic complications during laparoscopic colorectal surgery. Recently, various parameter-based perfusion analysis have been studied for quantitative evaluation, but the analysis results differ depending on the use of quantitative parameters due to differences in vascular anatomical structure. Therefore, it can help improve the accuracy and consistency by artificial intelligence (AI) based real-time analysis microperfusion (AIRAM).

aimTo evaluate the feasibility of AIRAM to predict the risk of anastomotic complication in the patient with laparoscopic colorectal cancer surgery.

methodsThe ICG curve was extracted from the region of interest (ROI) set in the ICG fluorescence video of the laparoscopic colorectal surgery. Pre-processing was performed to reduce AI performance degradation caused by external environment such as background, light source reflection, and camera shaking using MATLAB 2019 on an I7-8700k Intel central processing unit (CPU) PC. AI learning and evaluation were performed by dividing into a training patient group (

resultsAI-based risk and the conventional quantitative parameters including

conclusionIn conclusion, AI-based real-time microcirculation analysis had more accurate and consistent performance than the conventional parameter-based method.

Indexed as

Colorectal NeoplasmsColorectal SurgeryLaparoscopyAnastomotic LeakArtificial IntelligenceHumansIndocyanine GreenMicrocirculationReproducibility of ResultsIndocyanine GreenAnastomotic complicationsArtificial intelligentColorectal surgeryIndocyanine greenLaparoscopicMicrocirculation analysis

Identifiers

PMID33311942
PMCPMC7701947
OpenAlexW3110619961

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.