Deep Learning-Based Morphological Classification of Human Sperm Heads

High-Throughput and Stain-Free Morphological Analysis of Sperm Using Optofluidic Time-Stretch Imaging Flow Cytometry

The incidence of male infertility has kept increasing year by year, severely affecting the sustainability of society. Sperm morphological analysis plays an important role in assessing sperm fertility. However, current sperm morphological analysis is usually performed using microscopic images of stained sperm, which requires complex staining operations, making it difficult to achieve a comprehensive and efficient diagnosis. In this work, we propose and experimentally demonstrate sperm morphological analysis with optofluidic time-stretch (OTS) imaging flow cytometry. Specifically, we capture the stain-free sperm images with high-throughput OTS imaging flow cytometry and analyze the morphological features using a U-Net network. The analysis of 40,000 images from 20 clinical semen samples indicates that our method achieves higher accuracy than the smear test and shows high concordance with the results using comprehensive methods of routine semen examination. This work provides a high-throughput, stain-free sperm morphological analysis method, which holds promise for a comprehensive evaluation of semen.

MRI assessment of sacral injury location and analysis of influencing factors after high-intensity focused ultrasound ablation for patients with uterine fibroids

Purpose

Exploration of the location of sacral injuries following ultrasound-guided high-intensity focused ultrasound (USgHIFU) ablation for uterine fibroids and analysis of its influencing factors.

Methods

A retrospective analysis was conducted on 663 patients with uterine fibroids treated by USgHIFU ablation. Patients with vertebral injuries were identified based on postoperative MRI images, with specific locations of the injuries documented. Additionally, the condition of muscle damage around the vertebral body was assessed. Patients were divided into Upper group and Lower group based on the location of vertebral injuries. Univariate and multivariate logistic regression analyses were conducted to identify the influencing factors. The χ2 test was used to explore the relationship between the location of vertebral injuries and postoperative clinical adverse events, as well as muscle damage.

Results

Postoperative MRI examinations revealed that 42.3% (281/663) of the patients experienced vertebral injuries, which were localized to the range from L5 to S5. The injuries from L5 to S2 were classified as Upper group, accounting for 45.2% (127/281), while those from S3 to S5 were classified as Lower group, accounting for 54.8% (154/281). Multivariate analysis revealed that the distance from the ventral side of the fibroid to the abdominal wall skin, uterine position, and T2WI signal intensity were positively correlated with the location of sacral injuries (p < 0.05). Additionally, the location of sacral injuries was significantly associated with the occurrence of postoperative sacrococcygeal pain (p < 0.05). 162 patients (57.6%) with sacral injury were accompanied by piriformis and gluteus maximus muscle injuries, with piriformis injury accounting for 95.06%. The location of sacral injury was significantly correlated with piriformis injury (p < 0.05).

Conclusion

Postoperative MRI images of some patients with uterine fibroids treated with USgHIFU ablation show vertebral and surrounding muscle injuries, mainly involving sacrum and piriformis. For those with a retroverted uterus, a large distance between the ventral side of the fibroid and the abdominal wall, or fibroids exhibiting high signals on T2-weighted images (T2WI), the location of postoperative sacral injuries tends to be more inferior. Additionally, these patients face an increased risk of concurrent piriformis injury and a higher likelihood of experiencing sacrococcygeal pain.

Artificial intelligence model for the assessment of unstained live sperm morphology

Abstract

Traditional sperm morphology assessment requires staining and high magnification (100×), rendering sperm unsuitable for further use. We aimed to determine whether an in-house artificial intelligence (AI) model could reliably assess normal sperm morphology in living sperm and compare its performance with that of computer-aided semen analysis and conventional semen analysis methods. In this experimental study, we enrolled 30 healthy male volunteers aged 18-40 years at the Songklanagarind Assisted Reproductive Centre, Songklanagarind Hospital. We developed a novel dataset of sperm morphological images captured with confocal laser scanning microscopy at low magnification and high resolution to train and validate an AI model. Semen samples were divided into three aliquots and assessed for unstained live sperm morphology using the AI model, whereas computer-aided and conventional semen analysis methods evaluated fixed sperm morphology. The performance of our in-house AI model for evaluating unstained live sperm morphology was compared with that of the other two methods. The in-house AI model showed the strongest correlation with computer-aided semen analysis (r = 0.88), followed by conventional semen analysis (r = 0.76). The correlation between computer-aided semen analysis and conventional semen analysis was weaker (r = 0.57). Both the in-house AI and conventional semen analysis methods detected normal sperm morphology at significantly higher rates than computer-aided semen analysis. The in-house AI model could enhance assisted reproductive technology outcomes by improving the selection of high-quality sperm with normal morphology. This could lead to better outcomes of intracytoplasmic sperm injections and other fertility treatments.

Lay summary

We evaluated a new in-house AI model for assessing the shape and size (morphology) of live sperm without staining and performed comparisons with computer-aided semen analysis and conventional semen analysis, which require sperm to be fixed and stained before analysis. This new method of assessing unstained, live sperm is significant because it facilitates viable sperm selection for use in assisted reproductive technology immediately after assessment, ultimately contributing to improved fertility outcomes. The AI model allowed sperm morphology assessments with significantly improved accuracy and reliability. By using high-resolution images and advanced microscopy, the AI model could detect subcellular features. This AI model could be an effective tool in clinical settings, because it minimizes subjectivity and improves sperm selection for assisted reproductive technologies, potentially leading to higher success rates in infertility treatments. Further research can refine the model and validate its effectiveness in diverse clinical environments.

Health-related quality of life and sexual function among women with overweight or obesity and urinary incontinence: a cross-sectional study

PURPOSE

To assess the health related quality of life (HRQoL) and sexual function related to urinary incontinence (UI) severity among women with overweight or obesity.

METHODS

From September 2023 to January 2024, a cross-sectional was conducted among women seeking weight loss with overweight or obesity focusing on the symptoms and effects of UI. The degree of UI severity, UI-specific HRQoL, sexual function, and generic HRQoL were detected via Incontinence Modular Questionnaire-Urinary Incontinence Short Form (ICIQ-UI-SF), Incontinence Impact Questionnaire-Short Form (IIQ-7), Short-form Prolapse Incontinence Sexual Questionnaire (PISQ-12), and European Quality of Life-5 Dimensions 5-Level questionnaire (EQ-5D-5 L) respectively.

RESULTS

Out of 1205 valid responses, 564 (46.8%) reported UI with 354 classified as mild, 179 as moderate, and 31 as severe based on ICIQ-UI-SF scores. The mean age and body mass index of the respondents were 36.65 years and 29.9 kg/m², respectively. Individuals with more severe symptoms of UI exhibited correspondingly lower levels of UI-specific HRQoL, sexual function, and generic HRQoL. Although the correlations were weak, the severity of UI symptoms measured by ICIQ-UI-SF and IIQ-7 were significantly correlated with the mean utility values (r=-0.335, and - 0.351, P<0.001) of EQ-5D-5 L especially in the domains of anxiety/depression symptoms (r = 0.339 and 0.322, P<0.001).

CONCLUSION

Nearly half of women seeking weight loss with overweight or obesity may experience UI, which significantly affects HRQoL and sexual function. The severity of UI symptoms is significantly correlated with the generic HRQoL measured by EQ-5D-5 L, especially in the domain of anxiety/depression symptoms.

Impact of air pollution and heavy metal exposure on sperm quality: A clinical prospective research study

Exposure to air pollution poses significant risks to human health, including detrimental effects on the reproductive system, affecting both men and women. Our prospective clinical study aimed to assess the impact of prolonged air pollution exposure on sperm quality in male patients attending a fertility clinic. The current study was conducted at Sri Narayani Hospital and Research Centre in Vellore, Tamil Nadu, India, and the study examined sperm samples obtained from individuals with extended exposure to air pollution. Microscopic analysis, including scanning electron microscopy (SEM), was conducted to evaluate sperm morphology. At the same time, atomic absorption spectroscopy (AAS) determined the presence of heavy metals, including Zinc (Zn), Magnesium (Mg), Lead (Pb) and Cadmium (Cd), known to affect sperm production. Our findings revealed that long-term exposure to air pollution adversely affects sperm quality, manifesting in alterations during the spermatogenesis cycle, morphological abnormalities observed through SEM, and impaired sperm motility. Additionally, epidemiological evidence suggests that elevated levels of cadmium and lead in the environment induce oxidative stress, leading to sperm DNA damage and reduced sperm concentrations. These results underscore the urgent need for environmental interventions to mitigate air pollution and protect reproductive health.

Deep learning classification method for boar sperm morphology analysis

BACKGROUND

Boar semen quality emphasizes three major criteria: sperm concentration, motility, and morphology. Methods to analyze concentration and motility quickly and objectively readily exist, but few exist for analyzing morphology outside of subjective manual counting. Other vital factors for fertilization, like acrosome health, lack efficient detection methods due to limitations in detection by the human eye and costly biomarker analysis, which is rarely used in semen diagnostics.

OBJECTIVE

To overcome these challenges, we propose a novel approach integrating deep-learning technology with high-throughput image-based flow cytometry (IBFC) for objective and accurate analysis of both morphology and label-free acrosome health of thousands of individual spermatozoa at once, as opposed to manually counting on a microscope slide.

MATERIALS AND METHODS

Images of 10,000 spermatozoa were captured using an IBFC and manually annotated based on the primary morphological defect or acrosome health status for the training of the convolutional neural network (CNN). The CNN used these images to train and then applied that training to unannotated images to predict the model accuracy.

RESULTS

Using the CNNs, high F1 scores of 96.73%, 98.55%, and 99.31% for 20x, 40x, and 60x magnifications, respectively, for morphological classification were attained. Additionally, the model demonstrates an F1 score of 99.8% in detecting subtle acrosome health variations at the 60x magnification.

DISCUSSION AND CONCLUSIONS

We have established an integrated approach to rapidly collect and classify morphological defects and acrosome health status, without the use of manual counting or biomarker labeling. Our study underscores the potential of artificial intelligence in semen diagnostics, reducing technician variability, streamlining assays, and facilitating the development of additional label-free detection methods. This innovative approach addresses the barriers hindering biomarker adoption in semen analysis, offering a promising avenue for enhancing reproductive health assessments.

Genetic Causes of Qualitative Sperm Defects: A Narrative Review of Clinical Evidence

Several genes are implicated in spermatogenesis and fertility regulation, and these genes are presently being analysed in clinical practice due to their involvement in male factor infertility (MFI). However, there are still few genetic analyses that are currently recommended for use in clinical practice. In this manuscript, we reviewed the genetic causes of qualitative sperm defects. We distinguished between alterations causing reduced sperm motility (asthenozoospermia) and alterations causing changes in the typical morphology of sperm (teratozoospermia). In detail, the genetic causes of reduced sperm motility may be found in the alteration of genes associated with sperm mitochondrial DNA, mitochondrial proteins, ion transport and channels, and flagellar proteins. On the other hand, the genetic causes of changes in typical sperm morphology are related to conditions with a strong genetic basis, such as macrozoospermia, globozoospermia, and acephalic spermatozoa syndrome. We tried to distinguish alterations approved for routine clinical application from those still unsupported by adequate clinical studies. The most important aspect of the study was related to the correct identification of subjects to be tested and the correct application of genetic tests based on clear clinical data. The correct application of available genetic tests in a scenario where reduced sperm motility and changes in sperm morphology have been observed enables the delivery of a defined diagnosis and plays an important role in clinical decision-making. Finally, clarifying the genetic causes of MFI might, in future, contribute to reducing the proportion of so-called idiopathic MFI, which might indeed be defined as a subtype of MFI whose cause has not yet been revealed.

Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis

Background and Objectives: Infertility rates and the number of couples undergoing reproductive care have both increased substantially during the last few decades. Semen analysis is a crucial step in both the diagnosis and the treatment of male infertility. The accuracy of semen analysis results remains quite poor despite years of practice and advancements. Artificial intelligence (AI) algorithms, which can analyze and synthesize large amounts of data, can address the unique challenges involved in semen analysis due to the high objectivity of current methodologies. This review addresses recent AI advancements in semen analysis. Materials and Methods: A systematic literature search was performed in the PubMed database. Non-English articles and studies not related to humans were excluded. We extracted data related to AI algorithms or models used to evaluate semen parameters from the original studies, excluding abstracts, case reports, and meeting reports. Results: Of the 306 articles identified, 225 articles were rejected in the preliminary screening. The evaluation of the full texts of the remaining 81 publications resulted in the exclusion of another 48 articles, with a final inclusion of 33 original articles in this review. Conclusions: AI and machine learning are becoming increasingly popular in biomedical applications. The examination and selection of sperm by andrologists and embryologists may benefit greatly from using these algorithms. Furthermore, when bigger and more reliable datasets become accessible for training, these algorithms may improve over time.

Using a Decision Tree Algorithm Predictive Model for Sperm Count Assessment and Risk Factors in Health Screening Population

Purpose

Approximately 20% of couples face infertility challenges and struggle to conceive naturally. Despite advances in artificial reproduction, its success hinges on sperm quality. Our previous study used five machine learning (ML) algorithms, random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting, to model health data from 1375 Taiwanese males and identified ten risk factors affecting sperm count.

Methods

We employed the CART algorithm to generate decision trees using identified risk factors to predict healthy sperm counts. Four error metrics, SMAPE, RAE, RRSE, and RMSE, were used to evaluate the decision trees. We identified the top five decision trees based on their low errors and discussed in detail the tree with the least error.

Results

The decision tree featuring the least error, comprising BMI, UA, ST, T-Cho/HDL-C ratio, and BUN, corroborated the negative impacts of metabolic syndrome, particularly high BMI, on sperm count, while emphasizing the link between good sleep and male fertility. Our study also sheds light on the potentially significant influence of high BUN on spermatogenesis. Two novel risk factors, T-Cho/HDL-C and UA, warrant further investigation.

Conclusion

The ML algorithm established a predictive model for healthcare personnel to assess low sperm counts. Refinement of the model using additional data is crucial for improved precision. The risk factors identified offer avenues for future investigations.

Ensembling noisy segmentation masks of blurred sperm images

BACKGROUND

Sperm tail morphology and motility have been demonstrated to be important factors in determining sperm quality for in vitro fertilization. However, many existing computer-aided sperm analysis systems leave the sperm tail out of the analysis, as detecting a few tail pixels is challenging. Moreover, some publicly available datasets for classifying morphological defects contain images limited only to the sperm head. This study focuses on the segmentation of full sperm, which consists of the head and tail parts, and appear alone and in groups.

METHODS

We re-purpose the Feature Pyramid Network to ensemble an input image with multiple masks from state-of-the-art segmentation algorithms using a scale-specific cross-attention module. We normalize homogeneous backgrounds for improved training. The low field depth of microscopes blurs the images, easily confusing human raters in discerning minuscule sperm from large backgrounds. We thus propose evaluation protocols for scoring segmentation models trained on imbalanced data and noisy ground truth.

RESULTS

The neural ensembling of noisy segmentation masks outperforms all single, state-of-the-art segmentation algorithms in full sperm segmentation. Human raters agree more on the head than tail masks. The algorithms also segment the head better than the tail.

CONCLUSIONS

The extensive evaluation of state-of-the-art segmentation algorithms shows that full sperm segmentation is challenging. We release the SegSperm dataset of images from Intracytoplasmic Sperm Injection procedures to spur further progress on full sperm segmentation with noisy and imbalanced ground truth. The dataset is publicly available at https://doi.org/10.34808/6wm7-1159.

Application of artificial intelligence in gametes and embryos selection

Gamete and embryo quality are critical to the success rate of Assisted Reproductive Technology (ART) cycles, but there remains a lack of methods to accurately measure the quality of sperm, oocytes and embryos. The ability of Artificial Intelligence (AI) technology to analyze large amounts of data, especially video and images, is particularly useful in gamete and embryo assessment and selection. The well-trained model has fast calculation speed and high accuracy, which can help embryologists to perform more objective gamete and embryo selection. Various artificial intelligence models have been developed for gamete and embryo assessment, some of which exhibit good performance. In this review, we summarize the latest applications of AI technology in semen analysis, as well as selection for sperm, oocyte and embryo, and discuss the existing problems and development directions of artificial intelligence in this field.

AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review

Infertility has massively disrupted social and marital life, resulting in stressful emotional well-being. Early diagnosis is the utmost need for faster adaption to respond to these changes, which makes possible via AI tools. Our main objective is to comprehend the role of AI in fertility detection since we have primarily worked to find biomarkers and related risk factors associated with infertility. This paper aims to vividly analyse the role of AI as an effective method in screening, predicting for infertility and related risk factors. Three scientific repositories: PubMed, Web of Science, and Scopus, are used to gather relevant articles via technical terms: (human infertility OR human fertility) AND risk factors AND (machine learning OR artificial intelligence OR intelligent system). In this way, we systematically reviewed 42 articles and performed a meta-analysis. The significant findings and recommendations are discussed. These include the rising importance of data augmentation, feature extraction, explainability, and the need to revisit the meaning of an effective system for fertility analysis. Additionally, the paper outlines various mitigation actions that can be employed to tackle infertility and its related risk factors. These insights contribute to a better understanding of the role of AI in fertility analysis and the potential for improving reproductive health outcomes.

A Dual Architecture Fusion and AutoEncoder for Automatic Morphological Classification of Human Sperm

Infertility has become a common problem in global health, and unsurprisingly, many couples need medical assistance to achieve reproduction. Many human behaviors can lead to infertility, which is none other than unhealthy sperm. The important thing is that assisted reproductive techniques require selecting healthy sperm. Hence, machine learning algorithms are presented as the subject of this research to effectively modernize and make accurate standards and decisions in classifying sperm. In this study, we developed a deep learning fusion architecture called SwinMobile that combines the Shifted Windows Vision Transformer (Swin) and MobileNetV3 into a unified feature space and classifies sperm from impurities in the SVIA Subset-C. Swin Transformer provides long-range feature extraction, while MobileNetV3 is responsible for extracting local features. We also explored incorporating an autoencoder into the architecture for an automatic noise-removing model. Our model was tested on SVIA, HuSHem, and SMIDS. Comparison to the state-of-the-art models was based on F1-score and accuracy. Our deep learning results accurately classified sperm and performed well in direct comparisons with previous approaches despite the datasets' different characteristics. We compared the model from Xception on the SVIA dataset, the MC-HSH model on the HuSHem dataset, and Ilhan et al.'s model on the SMIDS dataset and the astonishing results given by our model. The proposed model, especially SwinMobile-AE, has strong classification capabilities that enable it to function with high classification results on three different datasets. We propose that our deep learning approach to sperm classification is suitable for modernizing the clinical world. Our work leverages the potential of artificial intelligence technologies to rival humans in terms of accuracy, reliability, and speed of analysis. The SwinMobile-AE method we provide can achieve better results than state-of-the-art, even for three different datasets. Our results were benchmarked by comparisons with three datasets, which included SVIA, HuSHem, and SMIDS, respectively (95.4% vs. 94.9%), (97.6% vs. 95.7%), and (91.7% vs. 90.9%). Thus, the proposed model can realize technological advances in classifying sperm morphology based on the evidential results with three different datasets, each having its characteristics related to data size, number of classes, and color space.

Machine Learning Predictive Models for Evaluating Risk Factors Affecting Sperm Count: Predictions Based on Health Screening Indicators

In many countries, especially developed nations, the fertility rate and birth rate have continually declined. Taiwan's fertility rate has paralleled this trend and reached its nadir in 2022. Therefore, the government uses many strategies to encourage more married couples to have children. However, couples marrying at an older age may have declining physical status, as well as hypertension and other metabolic syndrome symptoms, in addition to possibly being overweight, which have been the focus of the studies for their influences on male and female gamete quality. Many previous studies based on infertile people are not truly representative of the general population. This study proposed a framework using five machine learning (ML) predictive algorithms-random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting-to identify the major risk factors affecting male sperm count based on a major health screening database in Taiwan. Unlike traditional multiple linear regression, ML algorithms do not need statistical assumptions and can capture non-linear relationships or complex interactions between dependent and independent variables to generate promising performance. We analyzed annual health screening data of 1375 males from 2010 to 2017, including data on health screening indicators, sourced from the MJ Group, a major health screening center in Taiwan. The symmetric mean absolute percentage error, relative absolute error, root relative squared error, and root mean squared error were used as performance evaluation metrics. Our results show that sleep time (ST), alpha-fetoprotein (AFP), body fat (BF), systolic blood pressure (SBP), and blood urea nitrogen (BUN) are the top five risk factors associated with sperm count. ST is a known risk factor influencing reproductive hormone balance, which can affect spermatogenesis and final sperm count. BF and SBP are risk factors associated with metabolic syndrome, another known risk factor of altered male reproductive hormone systems. However, AFP has not been the focus of previous studies on male fertility or semen quality. BUN, the index for kidney function, is also identified as a risk factor by our established ML model. Our results support previous findings that metabolic syndrome has negative impacts on sperm count and semen quality. Sleep duration also has an impact on sperm generation in the testes. AFP and BUN are two novel risk factors linked to sperm counts. These findings could help healthcare personnel and law makers create strategies for creating environments to increase the country's fertility rate. This study should also be of value to follow-up research.

Does artificial intelligence have a role in the IVF clinic?

The success of IVF has remained stagnant for a decade. The focus of a great deal of research is to improve on the current ~30% success rate of IVF. Artificial intelligence (AI), or machines that mimic human intelligence, has been gaining traction for its potential to improve outcomes in medicine, such as cancer diagnosis from medical images. In this commentary, we discuss whether AI has the potential to improve fertility outcomes in the IVF clinic. Based on existing research, we examine the potential of adopting AI within multiple facets of an IVF cycle, including egg/sperm and embryo selection, as well as formulation of an IVF treatment regimen. We discuss both the potential benefits and concerns of the patient and clinician in adopting AI in the clinic. We outline hurdles that need to be overcome prior to implementation. We conclude that AI has an important future in improving IVF success.

What advances may the future bring to the diagnosis, treatment, and care of male sexual and reproductive health?

Over the past 40 years, since the publication of the original WHO Laboratory Manual for the Examination and Processing of Human Semen, the laboratory methods used to evaluate semen markedly changed and benefited from improved precision and accuracy, as well as the development of new tests and improved, standardized methodologies. Herein, we present the impact of the changes put forth in the sixth edition together with our views of evolving technologies that may change the methods used for the routine semen analysis, up-and-coming areas for the development of new procedures, and diagnostic approaches that will help to extend the often-descriptive interpretations of several commonly performed semen tests that promise to provide etiologies for the abnormal semen parameters observed. As we look toward the publication of the seventh edition of the manual in approximately 10 years, we describe potential advances that could markedly impact the field of andrology in the future.

Accurate Quantitative Histomorphometric-Mathematical Image Analysis Methodology of Rodent Testicular Tissue and Its Possible Future Research Perspectives in Andrology and Reproductive Medicine

Infertility is increasing worldwide; male factors can be identified in nearly half of all infertile couples. Histopathologic evaluation of testicular tissue can provide valuable information about infertility; however, several different evaluation methods and semi-quantitative score systems exist. Our goal was to describe a new, accurate and easy-to-use quantitative computer-based histomorphometric-mathematical image analysis methodology for the analysis of testicular tissue. On digitized, original hematoxylin-eosin (HE)-stained slides (scanned by slide-scanner), quantitatively describable characteristics such as area, perimeter and diameter of testis cross-sections and of individual tubules were measured with the help of continuous magnification. Immunohistochemically (IHC)-stained slides were digitized with a microscope-coupled camera, and IHC-staining intensity measurements on digitized images were also taken. Suggested methods are presented with mathematical equations, step-by-step detailed characterization and representative images are given. Our novel quantitative histomorphometric-mathematical image analysis method can improve the reproducibility, objectivity, quality and comparability of andrological-reproductive medicine research by recognizing even the mild impairments of the testicular structure expressed numerically, which might not be detected with the present semi-quantitative score systems. The technique is apt to be subjected to further automation with machine learning and artificial intelligence and can be named ‘Computer-Assisted or -Aided Testis Histology’ (CATHI).

A new deep-learning model using YOLOv3 to support sperm selection during intracytoplasmic sperm injection procedure

Purpose

To create and evaluate a machine-learning model for YOLOv3 that can simultaneously perform morphological evaluation and tracking in a short time, which can be adapted to video data under an inverted microscope.

Methods

Japanese patients who underwent intracytoplasmic sperm injection at the Jikei University School of Medicine and Keiai Reproductive and Endosurgical Clinic from January 2019 to March 2020 were included. An AI model that simultaneously performs morphological assessment and tracking was created and its performance was evaluated.

Results

For morphological assessment, the sensitivity and positive predictive value (PPV) of this model for abnormal sperm were 0.881 and 0.853, respectively. The sensitivity and PPV for normal sperm were 0.794 and 0.689, respectively. For tracking performance, among the 51 objects, 40 (78.4%) were mostly tracked, 11 (21.6%) were partially tracked, and 0 (0%) were mostly lost.

Conclusions

This study showed that evaluating sperm morphology while tracking in a single model is possible by training YOLO v3. This model could acquire time-series data of one sperm, which will assist in acquiring and annotating sperm image data.

Multi-model CNN fusion for sperm morphology analysis

Infertility is a common disorder affecting 20% of couples worldwide. Furthermore, 40% of all cases are related to male infertility. The first step in the determination of male infertility is semen analysis. The morphology, concentration, and motility of sperm are important characteristics evaluated by experts during semen analysis. Most laboratories perform the tests manually. However, manual semen analysis requires much time and is subject to observer variability during the evaluation. Therefore, computer-assisted systems are required. Additionally, to obtain more objective results, a large amount of data is necessary. Deep learning networks, which have become popular in recent years, are used for processing and analysing such quantities of data. Convolutional neural networks (CNNs) are a class of deep learning algorithm that are used extensively for processing and analysing images. In this study, six different CNN models were created for completely automating the morphological classification of sperm images. Additionally, two decision-level fusion techniques namely hard-voting and soft-voting were applied over these CNNs. To evaluate the performance of the proposed approach, three publicly available sperm morphology data sets were used in the experimental tests. For an objective analysis, a cross-validation technique was applied by dividing the data sets into five sub-sets. In addition, various data augmentation scales and mini-batch analysis were employed to obtain the highest classification accuracies. Finally, in the classification, accuracies 90.73%, 85.18% and 71.91% were obtained for the SMIDS, HuSHeM and SCIAN-Morpho data sets, respectively, using the soft-voting based fusion approach over the six created CNN models. The results suggested that the proposed approach could automatically classify as well as achieve high success in three different data sets.

Precision medicine and artificial intelligence: overview and relevance to reproductive medicine

Traditionally, new treatments have been developed for the population at large. Recently, large-scale genomic sequencing analyses have revealed tremendous genetic diversity between individuals. In diseases driven by genetic events such as cancer, genomic sequencing can unravel all the mutations that drive individual tumors. The ability to capture the genetic makeup of individual patients has led to the concept of precision medicine, a modern, technology-driven form of personalized medicine. Precision medicine matches each individual to the best treatment in a way that is tailored to his or her genetic uniqueness. To further personalize medicine, precision medicine increasingly incorporates and integrates data beyond genomics, such as epigenomics and metabolomics, as well as imaging. Increasingly, the robust use and integration of these modalities in precision medicine require the use of artificial intelligence and machine learning. This modern view of precision medicine, adopted early in certain areas of medicine such as cancer, has started to impact the field of reproductive medicine. Here we review the concepts and history of precision medicine and artificial intelligence, highlight their growing impact on reproductive medicine, and outline some of the challenges and limitations that these new fields have encountered in medicine.

Importance of real-time measurement of sperm head morphology in intracytoplasmic sperm injection

Intracytoplasmic sperm injection (ICSI) is an important technique in male infertility treatment. Currently, sperm selection for ICSI in human assisted reproductive technology (ART) is subjective, based on a visual assessment by the operator. Therefore, it is desirable to develop methods that can objectively provide an accurate assessment of the shape and size of sperm heads that use low-magnification microscopy available in most standard fertility clinics. Recent studies have shown a correlation between sperm head size and shape and chromosomal abnormalities, and fertilization rate, and various attempts have been made to establish automated computer-based measurement of the sperm head itself. For example, a dictionary-learning technique and a deep-learning-based method have both been developed. Recently, an automatic algorithm was reported that detects sperm head malformations in real time for selection of the best sperm for ICSI. These data suggest that a real-time sperm selection system for use in ICSI is necessary. Moreover, these systems should incorporate inverted microscopes (×400-600 magnification) but not the fluorescence microscopy techniques often used for a dictionary-learning technique and a deep-learning-based method. These advances are expected to improve future success rates of ARTs. In this review, we summarize recent reports on the assessment of sperm head shape, size, and acrosome status in relation to fertility, and propose further improvements that can be made to the ARTs used in infertility treatments.

Faster region convolutional neural network and semen tracking algorithm for sperm analysis

BACKGROUND AND OBJECTIVES

Semen analysis is a primary and mandatory procedure to evaluate the infertility during clinical examination. This procedure includes the analysis and classification of normal and abnormal Sperm, selection and efficient tracking of healthy sperm in the sample. Many methods were proposed earlier for the analysis of semen. The fast sperm movement and high dense cluster of sperm is a challenging task for researchers.

METHODS

The paper proposes a novel Faster Region Convolutional Neural Network (FRCNN) with Elliptic Scanning Algorithm (ESA) for classifying human sperm and a Novel Tail to Head movement algorithm (THMA) for the motility analysis and tracking. This proposed method improves the accuracy of computer assisted semen analysis (CASA).

RESULTS

The proposed method outperforms and provides better results than existing methods. Method provides better accuracy of 97.37%. Sperm detection and identifying the sperm motility in the group is performed with minimum execution time of 1.12 s.

CONCLUSIONS

A novel FRCNN with ESA detection algorithm is proposed for the analysis of human sperm classification. This method provides an accuracy of 97.37%. A Tail head movement-based (THMA) algorithm is explained for the motility analysis.

Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images

As an analytic tool in medicine, deep learning has gained great attention and opened new ways for disease diagnosis. Recent studies validate the effectiveness of deep learning algorithms for binary classification of skin lesions (i.e., melanomas and nevi classes) with dermoscopic images. Nonetheless, those binary classification methods cannot be applied to the general clinical situation of skin cancer screening in which multi-class classification must be taken into account. The main objective of this research is to develop, implement, and calibrate an advanced deep learning model in the context of automated multi-class classification of skin lesions. The proposed Deep Convolutional Neural Network (DCNN) model is carefully designed with several layers, and multiple filter sizes, but fewer filters and parameters to improve efficacy and performance. Dermoscopic images are acquired from the International Skin Imaging Collaboration databases (ISIC-17, ISIC-18, and ISIC-19) for experiments. The experimental results of the proposed DCNN approach are presented in terms of precision, sensitivity, specificity, and other metrics. Specifically, it attains 94 % precision, 93 % sensitivity, and 91 % specificity in ISIC-17. It is demonstrated by the experimental results that this proposed DCNN approach outperforms state-of-the-art algorithms, exhibiting 0.964 area under the receiver operating characteristics (AUROC) in ISIC-17 for the classification of skin lesions and can be used to assist dermatologists in classifying skin lesions. As a result, this proposed approach provides a novel and feasible way for automating and expediting the skin lesion classification task as well as saving effort, time, and human life.

Deep Learning Based Evaluation of Spermatozoid Motility for Artificial Insemination

We propose a deep learning method based on the Region Based Convolutional Neural Networks (R-CNN) architecture for the evaluation of sperm head motility in human semen videos. The neural network performs the segmentation of sperm heads, while the proposed central coordinate tracking algorithm allows us to calculate the movement speed of sperm heads. We have achieved 91.77% (95% CI, 91.11-92.43%) accuracy of sperm head detection on the VISEM (A Multimodal Video Dataset of Human Spermatozoa) sperm sample video dataset. The mean absolute error (MAE) of sperm head vitality prediction was 2.92 (95% CI, 2.46-3.37), while the Pearson correlation between actual and predicted sperm head vitality was 0.969. The results of the experiments presented below will show the applicability of the proposed method to be used in automated artificial insemination workflow.

Effect of deep transfer and multi-task learning on sperm abnormality detection

Analyzing the abnormality of morphological characteristics of male human sperm has been studied for a long time mainly because it has many implications on the male infertility problem, which accounts for approximately half of the infertility problems in the world. Yet, detecting such abnormalities by embryologists has several downsides. To clarify, analyzing sperms through visual inspection of an expert embryologist is a highly subjective and biased process. Furthermore, it takes much time for a specialist to make a diagnosis. Hence, in this paper, we proposed two deep learning algorithms that are able to automate this process. The first algorithm uses a network-based deep transfer learning approach, while the second technique, named Deep Multi-task Transfer Learning (DMTL), employs a novel combination of network-based deep transfer learning and multi-task learning to classify sperm's head, vacuole, and acrosome as either normal or abnormal. This DMTL technique is capable of classifying all the aforementioned parts of the sperm in a single prediction. Moreover, this is the first time that the concept of multi-task learning has been introduced to the field of Sperm Morphology Analysis (SMA). To benchmark our algorithms, we employed a freely-available SMA dataset named MHSMA. During our experiments, our algorithms reached the state-of-the-art results on the accuracy, precision, and f_0.5, as well as other important metrics, such as the Matthews Correlation Coefficient on one, two, or all three labels. Notably, our algorithms increased the accuracy of the head, acrosome, and vacuole by 6.66%, 3.00%, and 1.33%, and reached the accuracy of 84.00%, 80.66%, and 94.00% on these labels, respectively. Consequently, our algorithms can be used in health institutions, such as fertility clinics, with further recommendations to practically improve the performance of our algorithms.