Deep learning-based method for analyzing the optically trapped sperm rotation

Zhao, Jiangcheng; Bai, Chuanbiao; Zhang, Zhiguo; Zhang, Qingchuan

doi:10.1038/s41598-023-39819-7

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…A MATLAB-based program was applied to the extraction of longitudinal rolling parameters from videos of optically trap sperm. Briefly, the gradient of greyscale image of vertically trapped sperm cell per frame was calculated, followed by steps of image dilation, hole filling, image erosion and binarization, and then fit with an ellipse [ 24 ], and the time dependent orientation angle θ ( t ) of the sperm head can be calculated according to the orientation of the ellipse ( Fig. 1(a) ).…”

Section: Methodsmentioning

confidence: 99%

Time and power dependence of laser-induced photodamage on human sperm revealed by longitudinal rolling measurement using optical tweezers

He,

Wei,

Wang

et al. 2024

Biomed. Opt. Express

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Lasers are widely applied in assisted reproductive technologies, including sperm fixation, sperm selection and intracytoplasmic sperm injections, to reduce procedure time and improve consistency and reproducibility. However, quantitative studies on laser-induced photodamage of sperm are lacking. In this study, we demonstrated that, by using optical tweezers, the kinematic parameters of freely swimming sperm are correlated with the frequency as well as the percentage of pausing duration of longitudinal rolling of the same sperm head in the optical trap. Furthermore, by trapping individual sperm cells using 1064-nm optical tweezers, we quantitatively characterized the time-dependence of longitudinal rolling frequency and percentage of pausing duration of sperm under different laser powers. Our study revealed that, as trapping time and the laser power time increase, the longitudinal rolling frequency of the optically trapped sperm decreases with an increasing percentage of pausing duration, which characterizes the effect of laser power and duration on the photodamage of individual sperm cells. Our study provides experimental basis for the optimization of laser application in assisted reproductive technology, which may reduce the photodamage-induced biosafety risk in the future.

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Section: Methodsmentioning

confidence: 99%

Time and power dependence of laser-induced photodamage on human sperm revealed by longitudinal rolling measurement using optical tweezers

He,

Wei,

Wang

et al. 2024

Biomed. Opt. Express

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show abstract

“…The full data sets and code are published, see data availability below. CNNs have precedent in classifying optical tweezers signals, but are largely used in image based deep learning[29,[66][67][68][69]. We applied a simple 1D CNN architecture to classify the transmitted optical signal of EVs.…”

mentioning

confidence: 99%

Classification of single extracellular vesicles in a double nanohole optical tweezer for cancer detection

Peters,

Halvaei,

Zhao

et al. 2024

J. Phys. Photonics

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A major challenge in cancer prognostics is finding early biomarkers that can accurately identify cancer. Circulating tumor cells are rare and circulating tumor DNA can not provide information about the originating cell. Extracellular vesicles (EVs) contain cell specific information, are abundant in fluids, and have unique properties between cancerous and non-cancerous. Fluorescence measurements have limitations from intrinsic fluorescent background signals, photobleaching, non-specific labelling, and EV structural modifications. Here, we demonstrate a label-free approach to classification of 3 different EVs, derived from non-malignant, non-invasive cancerous, and invasive cancerous cell lines. Using double nanohole optical tweezers, the scattering from single trapped EVs is measured, and using a 1D convolutional neural network, we are able to classify the time series optical signal into its respective EV class with greater than 90% accuracy.

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Artificial Intelligence for Clinical Management of Male Infertility, a Scoping Review

Naik,

Roth,

Lundy

2024

Curr Urol Rep

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Purpose of Review Infertility impacts one in six couples worldwide, with male infertility contributing to approximately half of these cases. However, the causes of infertility remain incompletely understood, and current methods of clinical management are cost-restrictive, time-intensive, and have limited success. Artificial intelligence (AI) may help address some of these challenges. In this review, we synthesize recent literature in AI with implications for the clinical management of male infertility. Recent Findings Artificial intelligence may offer opportunities for proactive, cost-effective, and efficient management of male infertility, specifically in the areas of hypogonadism, semen analysis, and interventions such as assisted reproductive technology. Summary Patients may benefit from the integration of AI into a male infertility specialist’s clinical workflow. The ability of AI to integrate large volumes of data into predictive models could help clinicians guide conversations with patients on the value of various treatment options in infertility, but caution must be taken to ensure the quality of care being delivered remains high.

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Deep learning-based method for analyzing the optically trapped sperm rotation

Cited by 4 publications

References 31 publications

Time and power dependence of laser-induced photodamage on human sperm revealed by longitudinal rolling measurement using optical tweezers

Time and power dependence of laser-induced photodamage on human sperm revealed by longitudinal rolling measurement using optical tweezers

Classification of single extracellular vesicles in a double nanohole optical tweezer for cancer detection

Artificial Intelligence for Clinical Management of Male Infertility, a Scoping Review

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