Evidence map›Paper›PMID 35918549›Full record

ArticleSurgical endoscopy2023

Validation of an artificial intelligence platform for the guidance of safe laparoscopic cholecystectomy.

Simon Laplante, Babak Namazi, Parmiss Kiani, Daniel A Hashimoto, Adnan Alseidi, Mauricio Pasten, L Michael Brunt, Sujata Gill, Brian Davis, Matthew Bloom and 3 more

Abstract read
PubMed Publisher
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 31 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

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

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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

13 authors.

Simon LaplanteSurgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada. simon.laplante@mail.utoronto.ca.ORCID http://orcid.org/0000-0001-6174-3287
Babak NamaziDepartment of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Parmiss KianiSurgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada.
Daniel A HashimotoDepartment of Surgery, University Hospitals, Cleveland, OH, USA.
Adnan AlseidiDepartment of Surgery, University of California, San Francisco, CA, USA.
Mauricio PastenInstituto de Gastroenterologia Boliviano Japones, Cochabamba, Bolivia.
L Michael BruntDepartment of Surgery, Washington University School of Medicine, St. Louis, MO, USA.
Sujata GillDepartment of Surgery, Northeast Georgia Medical Center, Georgia, USA.
Brian DavisDepartment of Surgery, Texas Tech Paul L Foster School of Medicine, El Paso, TX, USA.
Matthew BloomDepartment of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Luise PernarDepartment of Surgery, Boston medical center, Boston, MA, USA.
Allan OkrainecSurgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada.
Amin MadaniSurgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMany surgical adverse events, such as bile duct injuries during laparoscopic cholecystectomy (LC), occur due to errors in visual perception and judgment. Artificial intelligence (AI) can potentially improve the quality and safety of surgery, such as through real-time intraoperative decision support. GoNoGoNet is a novel AI model capable of identifying safe ("Go") and dangerous ("No-Go") zones of dissection on surgical videos of LC. Yet, it is unknown how GoNoGoNet performs in comparison to expert surgeons. This study aims to evaluate the GoNoGoNet's ability to identify Go and No-Go zones compared to an external panel of expert surgeons.

methodsA panel of high-volume surgeons from the SAGES Safe Cholecystectomy Task Force was recruited to draw free-hand annotations on frames of prospectively collected videos of LC to identify the Go and No-Go zones. Expert consensus on the location of Go and No-Go zones was established using Visual Concordance Test pixel agreement. Identification of Go and No-Go zones by GoNoGoNet was compared to expert-derived consensus using mean F1 Dice Score, and pixel accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

resultsA total of 47 frames from 25 LC videos, procured from 3 countries and 9 surgeons, were annotated simultaneously by an expert panel of 6 surgeons and GoNoGoNet. Mean (± standard deviation) F1 Dice score were 0.58 (0.22) and 0.80 (0.12) for Go and No-Go zones, respectively. Mean (± standard deviation) accuracy, sensitivity, specificity, PPV and NPV for the Go zones were 0.92 (0.05), 0.52 (0.24), 0.97 (0.03), 0.70 (0.21), and 0.94 (0.04) respectively. For No-Go zones, these metrics were 0.92 (0.05), 0.80 (0.17), 0.95 (0.04), 0.84 (0.13) and 0.95 (0.05), respectively.

conclusionsAI can be used to identify safe and dangerous zones of dissection within the surgical field, with high specificity/PPV for Go zones and high sensitivity/NPV for No-Go zones. Overall, model prediction was better for No-Go zones compared to Go zones. This technology may eventually be used to provide real-time guidance and minimize the risk of adverse events.

Indexed as

Cholecystectomy, LaparoscopicSurgeonsArtificial IntelligenceCholecystectomyData CollectionHumansArtificial intelligenceBile duct injuryCholecystectomyDeep learningDeep neural networkMachine learning

Identifiers

PMID35918549

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.