This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Researchers have developed an AI-based system that automatically and objectively detects the moment when fish experience loss of equilibrium (LOE) due to temperature stress. The system combines DeepLabCut, a deep-learning AI that captures animal posture from video, with ResNet34, a deep-learning AI-based image classification technology.
Their system is expected to help predict the effects of climate change on fish. The research team was led by Specially Appointed Lecturer Tomoya Nakayama and Yoshiya Matsuo (a master's student at the time) of the Institute of Transformative Bio-Molecules (WPI-ITbM) at Nagoya University, in collaboration with Associate Professor Tatsuto Hasegawa and Takuya Kato (a master's student at the time) of the University of Fukui. Their research was published in the journal Scientific Reports on Sept. 10, 2026.
Changes in water temperature associated with climate change threaten fish and other species that live in aquatic environments. Because fish are ectothermic animals that cannot self-regulate their body temperature, changes in the surrounding water temperature directly affect various bodily functions. Therefore, accurately evaluating the temperature tolerance of fish is essential for predicting their response to climate change.
A conventional method for investigating fish temperature tolerance is to gradually change the water temperature while recording video and identify the moment when the fish can no longer maintain its balance as an indicator of LOE. Until now, however, researchers have visually examined videos to determine the moment of LOE. This inherent subjectivity could lead to variation in results and required considerable time and effort to examine large populations.
The research group first developed a technology that photographs fish individually in separate compartments and automatically identifies the individual fish in each compartment. They then used DeepLabCut to automatically track movements at seven locations on the fish's body: the tip of the nose, the left and right fins, the center of the body, the front and rear portions of the body, and the tail. Combining information on these movements with video information, an AI model determines the fish's state and automatically detects when a fish reaches LOE.
Across 50 individuals, the accuracy of the AI-based determinations was approximately equivalent to the variation in determinations made by experienced researchers, verifying its reliability. In addition, the AI produced the same result when subjected to repeated analysis of the same video. As such, the system is well suited for large-scale experiments with many videos.
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