Artificial intelligence for sperm selection—a systematic review

Cherouveim, Panagiotis; Velmahos, Constantine S.; Bormann, Charles L.

doi:10.1016/j.fertnstert.2023.05.157

Cited by 11 publications

(9 citation statements)

References 91 publications

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“…The sperm motility and kinematics results reported by CASA systems are very reliable, as they show a high correlation with manual semen analysis. However, CASA systems fail to describe the motility or kinematics at the level of a single sperm [28,29]. Despite the automation of sperm motility analysis by CASA, their operational difficulties limit their widespread acceptance [30].…”

Section: Ai In Evaluation Of Sperm Motilitymentioning

confidence: 99%

“…When coupled with additional ML models that examine sperm motility, there is a real possibility of automating the selection of a single excellent sperm for successful intra-cytoplasmic sperm injection (ICSI). Neural networks can improve the accuracy, speed, and dependability of automated single sperm identification procedures for ICSI, where intra-and inter-operator variation are unavoidable [28]. A faster region CNN (FR-CNN) was used to train a novel tail-to-head movement (THMA) algorithm to measure sperm motility, and its tracking capacity had a 97% success rate of accurately classifying individual sperm into low, moderate, and high motility [33].…”

Section: Ai In Evaluation Of Sperm Motilitymentioning

confidence: 99%

“…It has proven difficult to standardize sperm morphology evaluation compared to other parameters such as concentration and motility. Several AI and ML-based models are being continuously trained using morphology characteristics as per WHO guidelines to classify spermatozoa as normal or abnormal [28]. AI-based morphology is evaluated based on the head dimensions alone or combined with other parts (mid-piece and tail) of spermatozoa [28].…”

Section: Ai In Evaluation Of Sperm Morphologymentioning

confidence: 99%

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

Panner Selvam,

Moharana,

Baskaran

et al. 2024

Medicina

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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.

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Section: Ai In Evaluation Of Sperm Motilitymentioning

confidence: 99%

Section: Ai In Evaluation Of Sperm Motilitymentioning

confidence: 99%

Section: Ai In Evaluation Of Sperm Morphologymentioning

confidence: 99%

See 1 more Smart Citation

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

Panner Selvam,

Moharana,

Baskaran

et al. 2024

Medicina

View full text Add to dashboard Cite

show abstract

“…AI has revolutionized society over the past decade, with its limitless applications spanning all industries. Its applications are endless, including its use in the automotive industry, financial industry, transport mapping, military applications, and many others [5,[13][14][15]. Leveraging AI branches such as machine learning (ML), artificial neural networks (ANNs), deep learning (DL), robotics, and natural language processing (NLP) for big data analysis holds valuable applications in reproductive domains.…”

Section: Artificial Intelligence In Reproductive Medicine: Transforma...mentioning

confidence: 99%

“…Over 186 million people worldwide suffer from infertility, making it a serious global problem [2][3][4]. Up to 50% of these cases are thought to be caused by male factors, and this number has been rising over previous years [5]. With regard to male infertility, the exact prevalence is unknown since male infertility is severely underreported [3].…”

Section: Introductionmentioning

confidence: 99%

The Role of Artificial Intelligence in Male Infertility: Evaluation and Treatment: A Narrative Review

Venishetty,

Alkassis,

Raheem

2024

Uro

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Male infertility has affected an increasingly large population over the past few decades, affecting over 186 million people globally. The advent of assisted reproductive technologies (ARTs) and artificial intelligence (AI) has changed the landscape of diagnosis and treatment of male infertility. Through an extensive literature review encompassing the PubMed, Google Scholar, and Scopus databases, various AI techniques such as machine learning (ML), artificial neural networks (ANNs), deep learning (DL), and natural language processing (NLP) were examined in the context of evaluating seminal quality, predicting fertility potential, and improving semen analysis. Research indicates that AI models can accurately estimate the quality of semen, diagnose problems with sperm, and provide guidance on reproductive health decisions. In addition, developments in smartphone-based semen analyzers and computer-assisted semen analysis (CASA) are indicative of initiatives to improve the price, portability, and accuracy of results. Future directions point to possible uses for AI in ultrasonography assessment, microsurgical testicular sperm extraction (microTESE), and home-based semen analysis. Overall, AI holds significant promise in revolutionizing the diagnosis and treatment of male infertility, offering standardized, objective, and efficient approaches to addressing this global health challenge.

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Morphology Classification of Live Unstained Human Sperm Using Ensemble Deep Learning

Shahali,

Murshed,

Spencer

et al. 2024

Advanced Intelligent Systems

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Sperm morphology analysis is crucial in infertility diagnosis and treatment. However, current clinical analytical methods use either chemical stains that render cells unusable for treatment or rely on subjective manual inspection. Here, an ensemble deep‐learning model is presented for classification of live, unstained human sperm using whole‐cell morphology. This model achieves an accuracy and precision of 94% benchmarked against the consensus of three andrology scientists who classified the images independently. The model loses less than a 12% prediction performance even when image resolution is reduced by over sixfold. This ensures compatibility across varied clinical imaging setups. This model also provides a high certainty and robust classification of challenging images, which divided the experts. By providing a consistent, automated approach for classifying live, unstained cells using quantitative data, this model offers promising future opportunities for enhancing clinical sperm selection practices and reducing day‐to‐day variability in clinics.

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Artificial intelligence for sperm selection—a systematic review

Cited by 11 publications

References 91 publications

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

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

The Role of Artificial Intelligence in Male Infertility: Evaluation and Treatment: A Narrative Review

Morphology Classification of Live Unstained Human Sperm Using Ensemble Deep Learning

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