The Laryngoscope最新文献

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In Reference to When Should We Recommend Intracapsular Tonsillectomy in Pediatric Patients? 关于何时建议对儿科患者进行扁桃体囊内切除术?
The Laryngoscope Pub Date : 2024-09-11 DOI: 10.1002/lary.31764
Jeffrey Koempel
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引用次数: 0
Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology 评估头颈部肿瘤学中机器学习算法的报告质量
The Laryngoscope Pub Date : 2024-09-11 DOI: 10.1002/lary.31756
Rahul Alapati, Bryan Renslo, Sarah F. Wagoner, Omar Karadaghy, Aisha Serpedin, Yeo Eun Kim, Maria Feucht, Naomi Wang, Uma Ramesh, Antonio Bon Nieves, Amelia Lawrence, Celina Virgen, Tuleen Sawaf, Anaïs Rameau, Andrés M. Bur
{"title":"Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology","authors":"Rahul Alapati, Bryan Renslo, Sarah F. Wagoner, Omar Karadaghy, Aisha Serpedin, Yeo Eun Kim, Maria Feucht, Naomi Wang, Uma Ramesh, Antonio Bon Nieves, Amelia Lawrence, Celina Virgen, Tuleen Sawaf, Anaïs Rameau, Andrés M. Bur","doi":"10.1002/lary.31756","DOIUrl":"https://doi.org/10.1002/lary.31756","url":null,"abstract":"ObjectiveThis study aimed to assess reporting quality of machine learning (ML) algorithms in the head and neck oncology literature using the TRIPOD‐AI criteria.Data SourcesA 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.Review MethodsTwo 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.ResultsThe 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.ConclusionCurrent 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.Level of EvidenceNA <jats:italic>Laryngoscope</jats:italic>, 2024","PeriodicalId":501696,"journal":{"name":"The Laryngoscope","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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