DATA SOURCES: None.
STUDY SELECTION: Current literature describing the conduct, reporting, and appraisal of systematic reviews and meta-analyses.
DATA EXTRACTION: Best practices for conducting, reporting, and appraising systematic review were summarized.
DATA SYNTHESIS: A systematic review is a review of a clearly formulated question that uses systematic and explicit methods to identify, select, and critically appraise relevant original research, and to collect and analyze data from the studies that are included in the review. Critical appraisal methods address both the credibility (quality of conduct) and rate the confidence in the quality of summarized evidence from a systematic review. The A Measurement Tool to Assess Systematic Reviews-2 tool is a widely used practical tool to appraise the conduct of a systematic review. Confidence in estimates of effect is determined by assessing for risk of bias, inconsistency of results, imprecision, indirectness of evidence, and publication bias.
CONCLUSIONS: Systematic reviews are transparent and reproducible summaries of research and conclusions drawn from them are only as credible and reliable as their development process and the studies which form the systematic review. Applying evidence from a systematic review to patient care considers whether the results can be directly applied, whether all important outcomes have been considered, and if the benefits are worth potential harms and costs.
METHODS: This bibliometric work investigated the academic publication trends in medical image segmentation technology. These data were collected from the Web of Science (WoS) Core Collection and the Scopus. In the quantitative analysis stage, important visual maps were produced to show publication trends from five different perspectives including annual publications, countries, top authors, publication sources, and keywords. In the qualitative analysis stage, the frequently used methods and research trends in the medical image segmentation field were analyzed from 49 publications with the top annual citation rates.
RESULTS: The analysis results showed that the number of publications had increased rapidly by year. The top related countries include the Chinese mainland, the United States, and India. Most of these publications were conference papers, besides there are also some top journals. The research hotspot in this field was deep learning-based medical image segmentation algorithms based on keyword analysis. These publications were divided into three categories: reviews, segmentation algorithm publications, and other relevant publications. Among these three categories, segmentation algorithm publications occupied the vast majority, and deep learning neural network-based algorithm was the research hotspots and frontiers.
CONCLUSIONS: Through this bibliometric research work, the research hotspot in the medical image segmentation field is uncovered and can point to future research in the field. It can be expected that more researchers will focus their work on deep learning neural network-based medical image segmentation.