Evidence map›Paper›PMID 37697115›Full record

ArticleSurgical endoscopy2023

Use of artificial intelligence for decision-support to avoid high-risk behaviors during laparoscopic cholecystectomy.

Muhammad Uzair Khalid, Simon Laplante, Caterina Masino, Adnan Alseidi, Shiva Jayaraman, Haochi Zhang, Pouria Mashouri, Sergey Protserov, Jaryd Hunter, Michael Brudno and 1 more

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In one paragraph

Article in Surgical endoscopy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
3.4field-weighted citation impact, top 7% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 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

11 authors at 3 institutions in 2 countries.

Muhammad Uzair KhalidTemerty Faculty of Medicine, University of Toronto, Medical Sciences Building, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada. uzair.khalid@mail.utoronto.ca.ORCID 0000-0001-6269-9839
Simon LaplanteSurgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada.
Caterina MasinoSurgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada.
Adnan AlseidiDepartment of Surgery, University of California, San Francisco, San Francisco, CA, USA.
Shiva JayaramanDepartment of Surgery, University of Toronto, Toronto, ON, Canada.
Haochi ZhangDATA Team, University Health Network, Toronto, ON, Canada.
Pouria MashouriDATA Team, University Health Network, Toronto, ON, Canada.
Sergey ProtserovDATA Team, University Health Network, Toronto, ON, Canada.
Jaryd HunterDATA Team, University Health Network, Toronto, ON, Canada.
Michael BrudnoDATA Team, University Health Network, Toronto, ON, Canada.
Amin MadaniSurgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada.
University Health Network · CAUniversity of Toronto · CAUniversity of California, San Francisco · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionBile duct injuries (BDIs) are a significant source of morbidity among patients undergoing laparoscopic cholecystectomy (LC). GoNoGoNet is an artificial intelligence (AI) algorithm that has been developed and validated to identify safe ("Go") and dangerous ("No-Go") zones of dissection during LC, with the potential to prevent BDIs through real-time intraoperative decision-support. This study evaluates GoNoGoNet's ability to predict Go/No-Go zones during LCs with BDIs. METHODS AND PROCEDURES: Eleven LC videos with BDI (BDI group) were annotated by GoNoGoNet. All tool-tissue interactions, including the one that caused the BDI, were characterized in relation to the algorithm's predicted location of Go/No-Go zones. These were compared to another 11 LC videos with cholecystitis (control group) deemed to represent "safe cholecystectomy" by experts. The probability threshold of GoNoGoNet annotations were then modulated to determine its relationship to Go/No-Go predictions. Data is shown as % difference [99% confidence interval].

resultsCompared to control, the BDI group showed significantly greater proportion of sharp dissection (+ 23.5% [20.0-27.0]), blunt dissection (+ 32.1% [27.2-37.0]), and total interactions (+ 33.6% [31.0-36.2]) outside of the Go zone. Among injury-causing interactions, 4 (36%) were in the No-Go zone, 2 (18%) were in the Go zone, and 5 (45%) were outside both zones, after maximizing the probability threshold of the Go algorithm.

conclusionAI has potential to detect unsafe dissection and prevent BDIs through real-time intraoperative decision-support. More work is needed to determine how to optimize integration of this technology into the operating room workflow and adoption by end-users.

Indexed as

Bile Duct DiseasesCholecystectomy, LaparoscopicArtificial IntelligenceBile DuctsCholecystectomyHumansRisk-TakingArtificial intelligenceBile duct injuryDeep learningIntraoperative decision-supportLaparoscopic cholecystectomyMachine learning

Identifiers

PMID37697115
OpenAlexW4386600084

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.