Automated acoustic detection of mouse scratching

Elliott, Peter; G’Sell, Max; Snyder, Lindsey M.; Ross, Sarah E.; Ventura, Valérie

doi:10.1371/journal.pone.0179662

Cited by 10 publications

(10 citation statements)

References 16 publications

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“…However, they typically use direct observation to detect mouse scratching, which is labour-intensive, and has a low throughput. Automated detection systems are typically more sophisticated but often require specific equipment 4 , 6 . In the current study, we recorded mouse behaviour using a commercially available handy camera and analysed the images using a common desktop computer with a GPU.…”

Section: Discussionmentioning

confidence: 99%

“…Physiological disorders, as well as mental stress induce an itching sensation that manifests as scratching behaviour in animals. Several methods exist for measuring scratching, such as visual observation, acoustic detection, and induction current detection generated by the scratching motion 4 – 6 . These methods are accurate and are widely used in research.…”

Section: Introductionmentioning

confidence: 99%

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Automated detection of mouse scratching behaviour using convolutional recurrent neural network

Kobayashi

Matsushita

Shimizu

et al. 2021

Sci Rep

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Scratching is one of the most important behaviours in experimental animals because it can reflect itching and/or psychological stress. Here, we aimed to establish a novel method to detect scratching using deep neural network. Scratching was elicited by injecting a chemical pruritogen lysophosphatidic acid to the back of a mouse, and behaviour was recorded using a standard handy camera. Images showing differences between two consecutive frames in each video were generated, and each frame was manually labelled as showing scratching behaviour or not. Next, a convolutional recurrent neural network (CRNN), composed of sequential convolution, recurrent, and fully connected blocks, was constructed. The CRNN was trained using the manually labelled images and then evaluated for accuracy using a first-look dataset. Sensitivity and positive predictive rates reached 81.6% and 87.9%, respectively. The predicted number and durations of scratching events correlated with those of the human observation. The trained CRNN could also successfully detect scratching in the hapten-induced atopic dermatitis mouse model (sensitivity, 94.8%; positive predictive rate, 82.1%). In conclusion, we established a novel scratching detection method using CRNN and showed that it can be used to study disease models.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated detection of mouse scratching behaviour using convolutional recurrent neural network

Kobayashi

Matsushita

Shimizu

et al. 2021

Sci Rep

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“…In many previous studies involving laboratory animals such as mice, scratching was counted manually as in scratching bouts by human annotators by playing video recordings back and forth, which is a labor-intensive and time-consuming process often hinders the progress of the study and may also subject to human errors. Thus, it has attempted to develop automated detecting methods for mouse scratching based on various strategies, including the detection of vibration and sound generated when a mouse scratches the skin with the hind paw, motional detection of a metal ring or magnet implanted to the hind limb, the use of force platform detecting repetitive events, and also computer vision-based analysis [3456789101112]. Few studies have involved a machine-learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Based Automatic and Real-time Detection of Mouse Scratching Behaviors

et al. 2019

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Scratching is a main behavioral response accompanied by acute and chronic itch conditions, and has been quantified as an objective correlate to assess itch in studies using laboratory animals. Scratching has been counted mostly by human annotators, which is a time-consuming and laborious process. It has been attempted to develop automated scoring methods using various strategies, but they often require specialized equipment, costly software, or implantation of device which may disturb animal behaviors. To complement limitations of those methods, we have adapted machine learning-based strategy to develop a novel automated and real-time method detecting mouse scratching from experimental movies captured using monochrome cameras such as a webcam. Scratching is identified by characteristic changes in pixels, body position, and body size by frame as well as the size of body. To build a training model, a novel two-step J48 decision tree-inducing algorithm along with a C4.5 post-pruning algorithm was applied to three 30-min video recordings in which a mouse exhibits scratching following an intradermal injection of a pruritogen, and the resultant frames were then used for the next round of training. The trained method exhibited, on average, a sensitivity and specificity of 95.19% and 92.96%, respectively, in a performance test with five new recordings. This result suggests that it can be used as a non-invasive, automated and objective tool to measure mouse scratching from video recordings captured in general experimental settings, permitting rapid and accurate analysis of scratching for preclinical studies and high throughput drug screening.

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“…(d) Mouse scratching can happen at ten times per second or more [3,6], which is a rather fast movement compared to the time (usually a fraction of a second [18]) necessary for one automated adjustment of the focus by the video camera. While most commonly available 30 fps video cameras are equipped with image sensors fast enough to clearly record these scratch behaviour [15,18], chances are that those recorded LASV images blur because the mouse scratches its neck too fast for the video camera to achieve a satisfactory focus on the subject and to clearly record each and every scratch movement of the mouse.…”

Section: Lasv Blurriness: Why and What To Do Next?mentioning

confidence: 99%

“…Itch is a sensation that causes the desire to scratch [1][2][3]. In laboratory animal studies, scratch has been used as an objective correlate for quantitative assessment of itch [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Variation of the Performance of Machine-Learning Based Image Classifier in Automated Detection of Itch-Induced Scratch

Liu¹,

Sheng-xiang²,

Wu³

et al. 2020

Preprint

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A 'little brother' of pain, itch is an unpleasant sensation that creates a specific urge to scratch. To date, various machine-learning based image classifiers (MBICs) have been proposed for quantitative analysis of itch-induced scratch behaviour of laboratory animals in an automated, non-invasive, inexpensive and real-time manner. In spite of MBICs' advantages, the overall performances (accuracy, sensitivity and specificity) of current MBIC approaches remains inconsistent, with their values varying from ~50% to ~99%, for which the reasons underlying have yet to be investigated further, both computationally and experimentally. To look into the variation of the performance of MBICs in automated detection of itch-induced scratch, this article focuses on the experimental data recording step, and reports here for the first time that MBICs' overall performance is inextricably linked to the sharpness of experimentally recorded video of laboratory animal scratch behaviour. This article furthermore demonstrates for the first time that a linearly correlated relationship exists between video sharpness and overall performance (accuracy and specificity, but not sensitivity) of MBICs, and highlight the primary role of experimental data recording in rapid, accurate and consistent quantitative assessment of laboratory animal itch.

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Automated acoustic detection of mouse scratching

Cited by 10 publications

References 16 publications

Automated detection of mouse scratching behaviour using convolutional recurrent neural network

Automated detection of mouse scratching behaviour using convolutional recurrent neural network

Machine-Learning Based Automatic and Real-time Detection of Mouse Scratching Behaviors

Variation of the Performance of Machine-Learning Based Image Classifier in Automated Detection of Itch-Induced Scratch

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