Does advancement in marker-less pose-estimation mean more quality research? A systematic review.

Abstract

Recent breakthroughs in marker-less pose-estimation have driven a significant transformation in computer-vision approaches. Despite the emergence of state-of-the-art keypoint-detection algorithms, the extent to which these tools are employed and the nature of their application in scientific research has yet to be systematically documented. We systematically reviewed the literature to assess how pose-estimation techniques are currently applied in rodent (rat and mouse) models. Our analysis categorized each study by its primary focus: tool-development, method-focused, and study-focused studies. We mapped emerging trends alongside persistent gaps. We conducted a comprehensive search of Crossref, OpenAlex PubMed, and Scopus for articles published on rodent pose-estimation from 2016 through 2025, retrieving 16,412 entries. Utilizing an AI-assisted screening tool, we subsequently reviewed the top ∼1,000 titles and abstracts. 67 papers met our criteria: 30 tool-focused reports, 28 method-focused studies, and nine study-focused papers. Publication frequency trend has accelerated in recent years, with more than half of these studies published after 2021. Through a detailed review of the selected studies, we charted emerging trends and key patterns, from the emergence of new keypoint-detection methods to their integration into behavioral experiments and adoption in various disease contexts. Despite significant progress in marker-less pose-estimation technologies, their widespread application remains limited. Many laboratories still rely on traditional behavioral assays, under-using advanced tools. Establishing standardized protocols is the key step to bridge this gap, which will ultimately realize the full potential of marker-less pose-estimation and even greater insight into preclinical behavioral science.