Deep Learning Based Evaluation of Spermatozoid Motility for Artificial Insemination

WISE: whole-scenario embryo identification using self-supervised learning encoder in IVF

PURPOSE

To study the effectiveness of whole-scenario embryo identification using a self-supervised learning encoder (WISE) in in vitro fertilization (IVF) on time-lapse, cross-device, and cryo-thawed scenarios.

METHODS

WISE was based on the vision transformer (ViT) architecture and masked autoencoders (MAE), a self-supervised learning (SSL) method. To train WISE, we prepared three datasets including the SSL pre-training dataset, the time-lapse identification dataset, and the cross-device identification dataset. To identify whether pairs of images were from the same embryos in different scenarios in the downstream identification tasks, embryo images including time-lapse and microscope images were first pre-processed through object detection, cropping, padding, and resizing, and then fed into WISE to get predictions.

RESULTS

WISE could accurately identify embryos in the three scenarios. The accuracy was 99.89% on the time-lapse identification dataset, and 83.55% on the cross-device identification dataset. Besides, we subdivided a cryo-thawed evaluation set from the cross-device test set to have a better estimation of how WISE performs in the real-world, and it reached an accuracy of 82.22%. There were approximately 10% improvements in cross-device and cryo-thawed identification tasks after the SSL method was applied. Besides, WISE demonstrated improvements in the accuracy of 9.5%, 12%, and 18% over embryologists in the three scenarios.

CONCLUSION

SSL methods can improve embryo identification accuracy even when dealing with cross-device and cryo-thawed paired images. The study is the first to apply SSL in embryo identification, and the results show the promise of WISE for future application in embryo witnessing.

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.

DTLR-CS: Deep tensor low rank channel cross fusion neural network for reproductive cell segmentation

In recent years, with the development of deep learning technology, deep neural networks have been widely used in the field of medical image segmentation. U-shaped Network(U-Net) is a segmentation network proposed for medical images based on full-convolution and is gradually becoming the most commonly used segmentation architecture in the medical field. The encoder of U-Net is mainly used to capture the context information in the image, which plays an important role in the performance of the semantic segmentation algorithm. However, it is unstable for U-Net with simple skip connection to perform unstably in global multi-scale modelling, and it is prone to semantic gaps in feature fusion. Inspired by this, in this work, we propose a Deep Tensor Low Rank Channel Cross Fusion Neural Network (DTLR-CS) to replace the simple skip connection in U-Net. To avoid space compression and to solve the high rank problem, we designed a tensor low-ranking module to generate a large number of low-rank tensors containing context features. To reduce semantic differences, we introduced a cross-fusion connection module, which consists of a channel cross-fusion sub-module and a feature connection sub-module. Based on the proposed network, experiments have shown that our network has accurate cell segmentation performance.

A Hand-Held Platform for Boar Sperm Viability Diagnosis Based on Smartphone

The swine fever virus seriously affects pork production, and to improve pork production, pig breeding efficiency needs to be improved, and the detection of boar sperm activity is an important part of the pig breeding process. Traditional laboratory testing methods rely on bulky testing equipment, such as phase-contrast microscopes, high-speed cameras, and computers, which limit the testing scenarios. To solve the above problems, in this paper, a microfluidic chip was designed to simulate sperm in the oviduct with a channel thickness of 20 um, which can only accommodate sperm for two-dimensional movement. A miniature microscope system which can be used in combination with a smartphone is designed that is only the size of the palm of the hand and has a magnification of about 38 times. An intelligent diagnostic app was developed using Java language, which can automatically identify and track boar sperm with a recognition rate of 96.08% and an average tracking rate of 86%. The results show that the proposed smartphone-based hand-held platform can effectively replace the traditional microscope compound computer to diagnose sperm activity. In contrast, the platform is smaller, easier to use and is not limited by the usage scenarios.

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.

Sperm motility assessed by deep convolutional neural networks into WHO categories

Semen analysis is central in infertility investigation. Manual assessment of sperm motility according to the WHO recommendations is the golden standard, and extensive training is a requirement for accurate and reproducible results. Deep convolutional neural networks (DCNN) are especially suitable for image classification. In this study, we evaluated the performance of the DCNN ResNet-50 in predicting the proportion of sperm in the WHO motility categories. Two models were evaluated using tenfold cross-validation with 65 video recordings of wet semen preparations from an external quality assessment programme for semen analysis. The corresponding manually assessed data was obtained from several of the reference laboratories, and the mean values were used for training of the DCNN models. One model was trained to predict the three categories progressive motility, non-progressive motility, and immotile spermatozoa. Another model was used in predicting four categories, where progressive motility was differentiated into rapid and slow. The resulting average mean absolute error (MAE) was 0.05 and 0.07, and the average ZeroR baseline was 0.09 and 0.10 for the three-category and the four-category model, respectively. Manual and DCNN-predicted motility was compared by Pearson's correlation coefficient and by difference plots. The strongest correlation between the mean manually assessed values and DCNN-predicted motility was observed for % progressively motile spermatozoa (Pearson's r = 0.88, p < 0.001) and % immotile spermatozoa (r = 0.89, p < 0.001). For rapid progressive motility, the correlation was moderate (Pearson's r = 0.673, p < 0.001). The median difference between manual and predicted progressive motility was 0 and 2 for immotile spermatozoa. The largest bias was observed at high and low percentages of progressive and immotile spermatozoa. The DCNN-predicted value was within the range of the interlaboratory variation of the results for most of the samples. In conclusion, DCNN models were able to predict the proportion of spermatozoa into the WHO motility categories with significantly lower error than the baseline. The best correlation between the manual and the DCNN-predicted motility values was found for the categories progressive and immotile. Of note, there was considerable variation between the mean motility values obtained for each category by the reference laboratories, especially for rapid progressive motility, which impacts the training of the DCNN models.

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.

An Algorithm to Predict the Lack of Pregnancy after Intrauterine Insemination in Infertile Patients

Increasing intrauterine insemination (IUI) success rates is essential to improve the quality of care for infertile couples. Additionally, straight referral of couples with less probability of achieving a pregnancy through IUI to more complex methods such as in vitro fertilization is important to reduce costs and the time to pregnancy. The aim of the present study is to prospectively evaluate the threshold values for different parameters related to success in intrauterine insemination in order to provide better reproductive counseling to infertile couples, moreover, to generate an algorithm based on male and female parameters to predict whether the couple is suitable for achieving pregnancy using IUI. For that, one hundred ninety-seven infertile couples undergoing 409 consecutive cycles of intrauterine insemination during a two-year period were included. The first year served as a definition of the parameters and thresholds related to pregnancy achievement, while the second year was used to validate the consistency of these parameters. Subsequently, those parameters that remained consistent throughout two years were included in a generalized estimating equation model (GEE) to determine their significance in predicting pregnancy achievement. Parameters significantly associated with the lack of pregnancy through IUI and included in the GEE were (p < 0.05): (i) male age > 41 years; (ii) ejaculate sperm count < 51.79 x 10⁶ sperm; (iii) swim-up alkaline Comet > 59%; (iv) female body mass index > 45 kg/m²; (v) duration of infertility (>84 months), and (vi) basal LH levels > 27.28 mUI/mL. The application of these limits could provide a pregnancy prognosis to couples before undergoing intrauterine insemination, therefore avoiding it in couples with low chances of success. The retrospective application of these parameters to the same cohort of patients would have increased the pregnancy rate by up to 30%.

YOLOv5s-SA: Light-Weighted and Improved YOLOv5s for Sperm Detection

Sperm detection performance is particularly critical for sperm motility tracking. However, there are a large number of non-sperm objects, sperm occlusion and poorly detailed texture features in semen images, which directly affect the accuracy of sperm detection. To solve the problem of false detection and missed detection in sperm detection, a multi-sperm target detection model, Yolov5s-SA, with an SA attention mechanism is proposed based on the YOLOv5s algorithm. Firstly, a depthwise, separable convolution structure is used to replace the partial convolution of the backbone network, which can ensure stable precision and reduce the number of model parameters. Secondly, a new multi-scale feature fusion module is designed to enhance the perception of feature information to supplement the positional information and high-resolution of the deep feature map. Finally, the SA attention mechanism is integrated into the neck network before the output of the feature map to enhance the correlation between the feature map channels and improve the fine-grained feature fusion ability of YOLOv5s. Experimental results show that compared with various YOLO algorithms, the proposed algorithm improves the detection accuracy and speed to a certain extent. Compared with the YOLOv3, YOLOv3-spp, YOLOv5s and YOLOv5m models, the average accuracy increases by 18.1%, 15.2%, 6.9% and 1.9%, respectively. It can effectively reduce the missed detection of occluded sperm and achieve lightweight and efficient multi-sperm target detection.

motilitAI: A Machine Learning Framework for Automatic Prediction of Human Sperm Motility

In this article, human semen samples from the Visem dataset are automatically assessed with machine learning methods for their quality with respect to sperm motility. Several regression models are trained to automatically predict the percentage (0–100) of progressive, non-progressive, and immotile spermatozoa. The videos are adopted for unsupervised tracking and two different feature extraction methods—in particular custom movement statistics and displacement features. We train multiple neural networks and support vector regression models on the extracted features. Best results are achieved using a linear Support Vector Regressor with an aggregated and quantized representation of individual displacement features of each sperm cell. Compared to the best submission of the Medico Multimedia for Medicine challenge, which used the same dataset and splits, the mean absolute error (MAE) could be reduced from 8.83 to 7.31. We provide the source code for our experiments on GitHub (Code available at: https://github.com/EIHW/motilitAI).

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Improvements to state of the art in automatic human sperm motility prediction

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Unsupervised feature quantization used with off-the-shelf tracking algorithms

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Framework publicly available on GitHub: https://github.com/EIHW/motilitAI

Evaluation Algorithm for the Effectiveness of Stroke Rehabilitation Treatment Using Cross-Modal Deep Learning

It is important to study the evaluation algorithm for the stroke rehabilitation treatment effect to make accurate evaluation and optimize the stroke disease treatment plan according to the evaluation results. To address the problems of poor restoration effect of positron emission tomography (PET) image and recognition restoration effect of evaluation data and so on. In the paper, we propose a stroke rehabilitation treatment effect evaluation algorithm based on cross-modal deep learning. Magnetic resonance images (MRI) and PET of stroke patients were collected as evaluation data to construct a multimodal evaluation dataset, and the data were divided into positive samples and negative samples. According to the mapping relationship between MRI and PET, three-dimensional cyclic adversarial is used to generate the neural network model to recover the missing PET data. Using the cross-modal depth learning network model, the RGB image, depth image, gray image, and normal images of MRI and PET are taken as the feature images and the multifeature fusion method is used to fuse the feature images, output the recognition results of MRI and PET, and evaluate the effect of stroke rehabilitation treatment according to the recognition results. The results show that the proposed algorithm can accurately restore PET images, the evaluation data recognition effect is good, and the evaluation data recognition accuracy is higher than 95%. The evaluation accuracy of stroke rehabilitation treatment effect is high, the evaluation time varies between 0.56 s and 0.91 s, and the practical application effect is good.

Human, All Too Human? An All-Around Appraisal of the "Artificial Intelligence Revolution" in Medical Imaging

Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.

Artificial intelligence in the fertility clinic: status, pitfalls and possibilities

In recent years, the amount of data produced in the field of ART has increased exponentially. The diversity of data is large, ranging from videos to tabular data. At the same time, artificial intelligence (AI) is progressively used in medical practice and may become a promising tool to improve success rates with ART. AI models may compensate for the lack of objectivity in several critical procedures in fertility clinics, especially embryo and sperm assessments. Various models have been developed, and even though several of them show promising performance, there are still many challenges to overcome. In this review, we present recent research on AI in the context of ART. We discuss the strengths and weaknesses of the presented methods, especially regarding clinical relevance. We also address the pitfalls hampering successful use of AI in the clinic and discuss future possibilities and important aspects to make AI truly useful for ART.

An End-to-End Pipeline for Early Diagnosis of Acute Promyelocytic Leukemia Based on a Compact CNN Model

Timely microscopy screening of peripheral blood smears is essential for the diagnosis of acute promyelocytic leukemia (APL) due to the occurrence of early death (ED) before or during the initial therapy. Screening manually is time-consuming and tedious, and may lead to missed diagnosis or misdiagnosis because of subjective bias. To address these problems, we develop a three-step pipeline to help in the early diagnosis of APL from peripheral blood smears. The entire pipeline consists of leukocytes focusing, cell classification and diagnostic opinions. As the key component of the pipeline, a compact classification model based on attention embedded convolutional neural network blocks is proposed to distinguish promyelocytes from normal leukocytes. The compact classification model is validated on both the combination of two public datasets, APL-Cytomorphology_LMU and APL-Cytomorphology_JHH, as well as the clinical dataset, to yield a precision of 96.53% and 99.20%, respectively. The results indicate that our model outperforms the other evaluated popular classification models owing to its better accuracy and smaller size. Furthermore, the entire pipeline is validated on realistic patient data. The proposed method promises to act as an assistant tool for APL diagnosis.