Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology.
TRIPOD‐AI
artificial intelligence
head and neck oncology
machine learning
Journal
The Laryngoscope
ISSN: 1531-4995
Titre abrégé: Laryngoscope
Pays: United States
ID NLM: 8607378
Informations de publication
Date de publication:
11 Sep 2024
11 Sep 2024
Historique:
revised:
25
07
2024
received:
07
02
2024
accepted:
23
08
2024
medline:
11
9
2024
pubmed:
11
9
2024
entrez:
11
9
2024
Statut:
aheadofprint
Résumé
This study aimed to assess reporting quality of machine learning (ML) algorithms in the head and neck oncology literature using the TRIPOD-AI criteria. A comprehensive search was conducted using PubMed, Scopus, Embase, and Cochrane Database of Systematic Reviews, incorporating search terms related to "artificial intelligence," "machine learning," "deep learning," "neural network," and various head and neck neoplasms. Two independent reviewers analyzed each published study for adherence to the 65-point TRIPOD-AI criteria. Items were classified as "Yes," "No," or "NA" for each publication. The proportion of studies satisfying each TRIPOD-AI criterion was calculated. Additionally, the evidence level for each study was evaluated independently by two reviewers using the Oxford Centre for Evidence-Based Medicine (OCEBM) Levels of Evidence. Discrepancies were reconciled through discussion until consensus was reached. The study highlights the need for improvements in ML algorithm reporting in head and neck oncology. This includes more comprehensive descriptions of datasets, standardization of model performance reporting, and increased sharing of ML models, data, and code with the research community. Adoption of TRIPOD-AI is necessary for achieving standardized ML research reporting in head and neck oncology. Current reporting of ML algorithms hinders clinical application, reproducibility, and understanding of the data used for model training. To overcome these limitations and improve patient and clinician trust, ML developers should provide open access to models, code, and source data, fostering iterative progress through community critique, thus enhancing model accuracy and mitigating biases. NA Laryngoscope, 2024.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024 The American Laryngological, Rhinological and Otological Society, Inc.
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