Quantification of human sperm concentration using machine learning-based spectrophotometry

Lesani, Ali; Kazemnejad, Somaieh; Zand, Mahdi Moghimi; Azadi, Mojtaba; Jafari, Hassan; Mofrad, Mohammad R. K.; Nosrati, Reza

doi:10.1016/j.compbiomed.2020.104061

Cited by 21 publications

(15 citation statements)

References 33 publications

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“…Furthermore, studies that investigated sperm motility applied machine learning to videos of sperm specimens and showed good and consistent prediction results [ 82 ]. Research studies that investigated machine learning spectrophotometry in SA found that it was more effective and reliable than the available spectrophotometry methods currently in use [ 83 ]. In addition, there is multimodal SA that uses data from SA video frames, patients’ serum levels of sex hormones, lifestyle habits, and semen parameters, so these data sets will be very practical for applying AI to investigate patterns that lie within [ 84 ].…”

Section: Resultsmentioning

confidence: 99%

The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes—The Value of Regulatory Frameworks

Medenica

Zivanovic²,

Batkoska

et al. 2022

Diagnostics

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Infertility is a global health issue affecting women and men of reproductive age with increasing incidence worldwide, in part due to greater awareness and better diagnosis. Assisted reproduction technologies (ART) are considered the ultimate step in the treatment of infertility. Recently, artificial intelligence (AI) has been progressively used in the many fields of medicine, integrating knowledge and computer science through machine learning algorithms. AI has the potential to improve infertility diagnosis and ART outcomes estimated as pregnancy and/or live birth rate, especially with recurrent ART failure. A broad-ranging review has been conducted, focusing on clinical AI applications up until September 2022, which could be estimated in terms of possible applications, such as ultrasound monitoring of folliculogenesis, endometrial receptivity, embryo selection based on quality and viability, and prediction of post implantation embryo development, in order to eliminate potential contributing risk factors. Oocyte morphology assessment is highly relevant in terms of successful fertilization rate, as well as during oocyte freezing for fertility preservation, and substantially valuable in oocyte donation cycles. AI has great implications in the assessment of male infertility, with computerised semen analysis systems already in use and a broad spectrum of possible AI-based applications in environmental and lifestyle evaluation to predict semen quality. In addition, considerable progress has been made in terms of harnessing AI in cases of idiopathic infertility, to improve the stratification of infertile/fertile couples based on their biological and clinical signatures. With AI as a very powerful tool of the future, our review is meant to summarise current AI applications and investigations in contemporary reproduction medicine, mainly focusing on the nonsurgical aspects of it; in addition, the authors have briefly explored the frames of reference and guiding principles for the definition and implementation of legal, regulatory, and ethical standards for AI in healthcare.

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Section: Resultsmentioning

confidence: 99%

The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes—The Value of Regulatory Frameworks

Medenica

Zivanovic²,

Batkoska

et al. 2022

Diagnostics

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“…Furthermore, an artificial neural network was created to quantify sperm concentration using a machine learning-based spectrophotometry approach. 41 human sperm samples were analyzed under 390–1100 nm light spectra to develop the neural network and the full spectrum neural network model produced results with 93% accuracy and 100% agreement with clinical assessments ( Lesani et al., 2020 ).…”

Section: Challenges and Future Perspectivesmentioning

confidence: 99%

Disposable paper-based microfluidics for fertility testing

et al. 2022

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“…However, this CASA examination is quite expensive, and it has not been included in the 2010 WHO guideline for sperm analysis. Another semen examination method that has utilized artificial intelligence technology is calculating sperm concentration using machine learning-based spectroscopy [1]. Semen analysis is the main procedure in infertility examination to classify sperm samples as having normal or abnormal values.…”

Section: Introductionmentioning

confidence: 99%

Predicting the value of sperm analysis using an electronic nose

Wijaya

Kusumaatmaja

Rizal

2022

IJEECS

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Total motile sperm count and DNA fragmentation Index are two parameters in sperm analysis that have recently been used to determine the outcome of the management of cases of male infertility. Total Motile Sperm Count is one of the values considered better than the 2010 World Health Organization standard sperm analysis in terms of predictive value for the success of the spontaneous ongoing pregnancy rate. High DNA Fragmentation Index values were associated with lower pregnancy success and an increased risk of low fertilization rate or total fertilization failure. In this study, we developed a method to classify sperm analysis based on total motility sperm count and DNA Fragmentation Index values by using an electronic nose. In the total motility sperm count (TMSC) study, we use four algorithms with the result of accuracy values 95% and in the DNA fragmentation Index study, we get a fairly good accuracy value for two algorithms with the accuracy values 70%.

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Quantification of human sperm concentration using machine learning-based spectrophotometry

Cited by 21 publications

References 33 publications

The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes—The Value of Regulatory Frameworks

The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes—The Value of Regulatory Frameworks

Disposable paper-based microfluidics for fertility testing

Predicting the value of sperm analysis using an electronic nose

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