Computer software (SiD) assisted real-time single sperm selection associated with fertilization and blastocyst formation

Mendizabal-Ruiz, Gerardo; Chávez-Badiola, Alejandro; Figueroa, Isaac Aguilar; Nuño, V.; Farías, Adolfo Flores-Saiffe; Valencia-Murillo, Roberto; Drakeley, Andrew J.; García-Sandoval, J.P.; Cohen, Jacques

doi:10.1016/j.rbmo.2022.03.036

Cited by 21 publications

(15 citation statements)

References 38 publications

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“…So far, ML models have been developed for determining DFI preliminarily using tests such as SCSA, AO, single sperm DFI assay as well as SCD. [ 5 9 13 14 ]…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based robust automated system for predicting human sperm DNA fragmentation index

Kumar¹,

Sharma²,

Halder³

et al. 2023

J Hum Reprod Sci

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A BSTRACT Background: Determining the DNA fragmentation index (DFI) by the sperm chromatin dispersion (SCD) test involves manual counting of stained sperms with halo and no halo. Aims: The aim of this study is to build a robust artificial intelligence-based solution to predict the DFI. Settings and Design: This is a retrospective experimental study conducted in a secondary in vitro fertilisation setup. Materials and Methods: We obtained 24,415 images from 30 patients after the SCD test using a phase-contrast microscope. We classified the dataset into two, binary (halo/no halo) and multiclass (big/medium/small halo/degraded (DEG)/dust). Our approach consists of a training and prediction phase. The 30 patients’ images were divided into training (24) and prediction (6) sets. A pre-processing method M was developed to automatically segment the images to detect sperm-like regions and was annotated by three embryologists. Statistical Analysis Used: To interpret the findings, the precision-recall curve and F1 score were utilised. Results: Binary and multiclass datasets containing 8887 and 15,528 cropped sperm image regions showed an accuracy of 80.15% versus 75.25%. A precision-recall curve was determined and the binary and multiclass datasets obtained an F1 score of 0.81 versus 0.72. A confusion matrix was applied for predicted and actuals for the multiclass approach where small halo and medium halo confusion were found to be highest. Conclusion: Our proposed machine learning model can standardise and aid in arriving at accurate results without using expensive software. It provides accurate information about healthy and DEG sperms in a given sample, thereby attaining better clinical outcomes. The binary approach performed better with our model than the multiclass approach. However, the multiclass approach can highlight the distribution of fragmented and non-fragmented sperms.

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“…So far, ML models have been developed for determining DFI preliminarily using tests such as SCSA, AO, single sperm DFI assay as well as SCD. [ 5 9 13 14 ]…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based robust automated system for predicting human sperm DNA fragmentation index

Kumar¹,

Sharma²,

Halder³

et al. 2023

J Hum Reprod Sci

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“…There have been considerable studies on the utility of machine learning and AI-based image analysis on the selection of embryos for prediction of euploidy status, implantation potential and incidence of miscarriage (Barnes et al, 2023, Diakiw et al, 2022, Duval et al, 2023, Hariharan et al, 2019, Tran et al, 2018, VerMilyea et al, 2020). Studies have also proven the application of ML in the selection and assessment of sperm for use in ICSI by tacking sperm correlated with better quality blastocysts (Joshi et al, 2023, Mendizabal-Ruiz et al, 2022). Furthermore, studies using images of sperm having been labelled as normal or abnormally shaped by a professional or stained for DNA integrity; given a sufficient volume and variety of these labelled images, ML models have been trained to label the morphology of predict DNA fragmentation of new, unseen, images of sperm (McCallum et al, 2019, Wang et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

AI facilitated sperm detection in azoospermic samples for use in ICSI

Goss,

Vasilescu,

Vasilescu

et al. 2023

Preprint

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Research question: Can artificial intelligence (AI) improve efficiency and efficacy of sperm searches in azoospermic samples? Design: This two-phase proof-of-concept study beginning with a training phase using 8 azoospermic patients (>10000 sperm images) to provide a variety of surgically collected samples for sperm morphology and debris variation to train a convolutional neural network to identify sperm. Secondly, side-by-side testing on 2 cohorts, an embryologist versus the AI identifying all sperm in still images (cohort 1, N=4, 2660 sperm) and then a side-by-side test with deployment of the AI model on an ICSI microscope and the embryologist performing a search with and without the aid of the AI (cohort 2, N=4, >1300 sperm). Time taken, accuracy and precision of sperm identification was measured. Results: In cohort 1, the AI model showed improvement in time-taken to identify all sperm per field of view (0.019+-0.30 x 10-5s versus 36.10+-1.18s, P<0.0001) and improved accuracy (91.95+-0.81% vs 86.52+-1.34%, P<0.001) compared to an embryologist. From a total of 688 sperm in all samples combined, 560 were found by an embryologist and 611 were found by the AI in <1000th of the time. In cohort 2, the AI-aided embryologist took significantly less time per droplet (98.90+-3.19s vs 168.7+-7.84s, P<0.0001) and found 1396 sperm, while 1274 were found without AI, although no significant difference was observed. Conclusions: AI-powered image analysis has the potential for seamless integration into laboratory workflows, and to reduce time to identify and isolate sperm from surgical sperm samples from hours to minutes, thus increasing success rates from these treatments.

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“…Eventually, integrating our model with an automated selection system for sperm cells based on their motility, morphology, or DNA fragmentation [12][13][14][15][16][17]41,42] is another important step in the path for an accurate, efficient, fully automatic sperm selection framework for ICSI, which eventually will lead to higher success rate.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…[7] Many tracking algorithms have been used to estimate the current location of sperm cells, mainly for the purpose of motility feature extraction. [8][9][10][11] Computerized motility feature extraction [12] facilitates fast detection and quality analysis of sperm cells. Researchers have also explored the use of various microscopic imaging techniques, including quantitative phase imaging and deep learning algorithms, to predict the quality of sperm cells based on their morphology and estimate the extent of their DNA fragmentation, [13][14][15][16] in addition to their motility.…”

Section: Introductionmentioning

confidence: 99%

Location Prediction of Sperm Cells Using Long Short‐Term Memory Networks

Noy

Barnea

Dudaie

et al. 2023

Advanced Intelligent Systems

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Intracytoplasmic sperm injection (ICSI) requires precise selection of a single sperm cell in a dish to be injected into an oocyte. This task is challenging due to high sperm velocity, collision with other sperm cells, and changes in the imaging focus. Herein, a new model is proposed, which is based on a multilayer long short‐term memory (LSTM) network combined with linear extrapolation calculation, for predicting the future location of individual sperm cells based on their previous paths. The model is trained with a unique loss function, constructed from the predicted location and trajectory, and results in low mean location error predictions. The results are based on comparing different input sequences length, number of time frames ahead, and motility parameters of sperm cells. This model can provide faster and more accurate sperm motility predictions, better tracking, and aid automatic sperm capturing technologies.

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Computer software (SiD) assisted real-time single sperm selection associated with fertilization and blastocyst formation

Cited by 21 publications

References 38 publications

Deep learning-based robust automated system for predicting human sperm DNA fragmentation index

Deep learning-based robust automated system for predicting human sperm DNA fragmentation index

AI facilitated sperm detection in azoospermic samples for use in ICSI

Location Prediction of Sperm Cells Using Long Short‐Term Memory Networks

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