Evidence map›Paper›PMID 38662215›Full record

SynthesisEuropean archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery2024

The use of artificial intelligence in reconstructive surgery for head and neck cancer: a systematic review.

Cyril Devault-Tousignant, Myriam Harvie, Eric Bissada, Apostolos Christopoulos, Paul Tabet, Louis Guertin, Houda Bahig, Tareck Ayad

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
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

8 authors.

Cyril Devault-Tousignant *Faculty of Medicine, McGill University, 3605 de la Montagne Street, Montreal, QC, H3G 2M1, Canada. Cyril.devault-tousignant@mail.mcgill.ca.
Myriam Harvie *Faculty of Medicine, University of Montreal, Montreal, QC, Canada.
Eric BissadaDivision of Otolaryngology Head and Neck Surgery, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.
Apostolos ChristopoulosDivision of Otolaryngology Head and Neck Surgery, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.
Paul TabetDivision of Otolaryngology Head and Neck Surgery, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.
Louis GuertinDivision of Otolaryngology Head and Neck Surgery, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.
Houda BahigCentre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.
Tareck AyadDivision of Otolaryngology Head and Neck Surgery, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.ORCID http://orcid.org/0000-0003-3673-7875

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe popularity of artificial intelligence (AI) in head and neck cancer (HNC) management is increasing, but postoperative complications remain prevalent and are the main factor that impact prognosis after surgery. Hence, recent studies aim to assess new AI models to evaluate their ability to predict free flap complications more effectively than traditional algorithms. This systematic review aims to summarize current evidence on the utilization of AI models to predict complications following reconstructive surgery for HNC.

methodsA combination of MeSH terms and keywords was used to cover the following three subjects: "HNC," "artificial intelligence," and "free flap or reconstructive surgery." The electronic literature search was performed in three relevant databases: Medline (Ovid), Embase (Ovid), and Cochrane. Quality appraisal of the included study was conducted using the TRIPOD Statement.

resultsThe review included a total of 5 manuscripts (n = 5) for a total of 7524 patients. Across studies, the highest area under the receiver operating characteristic (AUROC) value achieved was 0.824 by the Auto-WEKA model. However, only 20% of reported AUROCs exceeded 0.70. One study concluded that most AI models were comparable or inferior in performance to conventional logistic regression. The highest predictors of complications were flap type, smoking status, tumour location, and age. DISCUSSION: Some models showed promising results. Predictors identified across studies were different than those found in existing literature, showing the added value of AI models. However, the algorithms showed inconsistent results, underlying the need for better-powered studies with larger databases before clinical implementation.

Indexed as

Artificial IntelligenceHead and Neck NeoplasmsPlastic Surgery ProceduresPostoperative ComplicationsFree Tissue FlapsHumansArtificial intelligenceFree flapHead and neck neoplasmsMachine learningPostoperative complicationsReconstructive surgerySystematic review

Identifiers

PMID38662215

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.