Evidence map›Paper›PMID 34601587›Full record

ArticleEuropean journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery2022

A scoping review of artificial intelligence applications in thoracic surgery.

Kenneth P Seastedt, Dana Moukheiber, Saurabh A Mahindre, Chaitanya Thammineni, Darin T Rosen, Ammara A Watkins, Daniel A Hashimoto, Chuong D Hoang, Jacques Kpodonu, Leo A Celi

Open access · bronzeAbstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 20% 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Can artificial intelligence models predict hospital stays following non-small cell lung carcinoma surgery?Kardiochirurgia i torakochirurgia polska = Polish journal of cardio-thoracic surgery · 2025
    Article
  6. Article
  7. Review
  8. Review
  9. Review
  10. Article
  11. Article
  12. Article
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  15. Few-Shot Learning Geometric Ensemble for Multi-label Classification of Chest X-Rays.Data augmentation, labelling, and imperfections : second MICCAI workshop, DALI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. DALI (Workshop) (2nd : 2022 : Singapore) · 2022
    Article
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

10 authors at 5 institutions in 1 country.

Kenneth P SeastedtDepartment of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-6345-8217
Dana MoukheiberLaboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA.
Saurabh A MahindreInstitute for Computational and Data Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.
Chaitanya ThammineniHILS Laboratory, University at Buffalo, State University of New York, Buffalo, NY, USA.ORCID 0000-0002-7410-6460
Darin T RosenDepartment of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Ammara A WatkinsDepartment of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-5874-406X
Daniel A HashimotoSurgical AI & Innovation Laboratory, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Chuong D HoangThoracic Surgery Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.ORCID 0000-0003-4016-1434
Jacques KpodonuDivision of Cardiac Surgery, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-7222-2786
Leo A CeliDivision of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-6712-6626
Beth Israel Deaconess Medical Center · USUniversity at Buffalo, State University of New York · USHarvard University · USMassachusetts Institute of Technology · USNational Institutes of Health · US

Funding

microRNA regulatory networks in thoracic cancersZIABC011657 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI HOANG, CHUONG · 2015 to 2025
$18.1M
Critical Care Informatics: Ethical considerations around the use and sharing of health-related dataR01EB017205 · NIBIB · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI CELI, LEO ANTHONY G, MARK, ROGER GREENWOOD · 2014 to 2021
$3.8M
NIBIB NIH HHS R01 EB017205
6 · The paper itself

Abstract

objectivesMachine learning (ML) has great potential, but there are few examples of its implementation improving outcomes. The thoracic surgeon must be aware of pertinent ML literature and how to evaluate this field for the safe translation to patient care. This scoping review provides an introduction to ML applications specific to the thoracic surgeon. We review current applications, limitations and future directions.

methodsA search of the PubMed database was conducted with inclusion requirements being the use of an ML algorithm to analyse patient information relevant to a thoracic surgeon and contain sufficient details on the data used, ML methods and results. Twenty-two papers met the criteria and were reviewed using a methodological quality rubric.

resultsML demonstrated enhanced preoperative test accuracy, earlier pathological diagnosis, therapies to maximize survival and predictions of adverse events and survival after surgery. However, only 4 performed external validation. One demonstrated improved patient outcomes, nearly all failed to perform model calibration and one addressed fairness and bias with most not generalizable to different populations. There was a considerable variation to allow for reproducibility.

conclusionsThere is promise but also challenges for ML in thoracic surgery. The transparency of data and algorithm design and the systemic bias on which models are dependent remain issues to be addressed. Although there has yet to be widespread use in thoracic surgery, it is essential thoracic surgeons be at the forefront of the eventual safe introduction of ML to the clinic and operating room.

Indexed as

Thoracic SurgeryThoracic Surgical ProceduresAlgorithmsArtificial IntelligenceHumansReproducibility of ResultsAlgorithmArtificial intelligenceComplicationsMachine learningPredictionSurvival

Identifiers

PMID34601587
PMCPMC8932394
OpenAlexW3202309439

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.