A systematic review on machine learning in sellar region diseases: quality and reporting items.

ABSTRACT:

Introduction

Machine learning methods in sellar region diseases present a particular challenge because of the complexity and the necessity for reproducibility. This systematic review aims to compile the current literature on sellar region diseases that utilized machine learning methods and to propose a quality assessment tool and reporting checklist for future studies.

Methods

PubMed and Web of Science were searched to identify relevant studies. The quality assessment included five categories: unmet needs, reproducibility, robustness, generalizability and clinical significance.

Results

Seventeen studies were included with the diagnosis of general pituitary neoplasms, acromegaly, Cushing's disease, craniopharyngioma and growth hormone deficiency. 87.5% of the studies a

SUBMITTER: Qiao N 

PROVIDER: S-EPMC6612064 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature