Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries.

ABSTRACT:

Background

While there are innumerable machine learning (ML) research algorithms used for segmentation of gliomas, there is yet to be a US FDA cleared product. The aim of this study is to explore the systemic limitations of research algorithms that have prevented translation from concept to product by a review of the current research literature.

Methods

We performed a systematic literature review on 4 databases. Of 11 727 articles, 58 articles met the inclusion criteria and were used for data extraction and screening using TRIPOD.

Results

We found that while many articles were published on ML-based glioma segmentation and report high accuracy results, there were substantial limitations in the methods and results portions of the papers that result in difficulty reprodu

SUBMITTER: Tillmanns N 

PROVIDER: S-EPMC9446682 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature