Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology.

ABSTRACT:

Objective

This study aimed to assess reporting quality of machine learning (ML) algorithms in the head and neck oncology literature using the TRIPOD-AI criteria.

Data sources

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.

Review methods

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 indep

SUBMITTER: Alapati R 

PROVIDER: S-EPMC12375222 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature