Artificial Intelligence Based Machine Learning Models Predict Sperm Parameter Upgrading after Varicocele Repair: A Multi-Institutional Analysis

Ory, Jesse; Tradewell, Michael B.; Blankstein, Udi; Lima, Thiago Fernandes Negris; Nackeeran, Sirpi; González, Daniel; Nwefo, Elie; Moryousef, Joseph; Madhusoodanan, Vinayak; Lau, Susan; Jarvi, Keith; Ramasamy, Ranjith

doi:10.5534/wjmh.210159

Cited by 18 publications

(12 citation statements)

References 28 publications

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“…Therefore, to answer the PICO questions formulated in the previous section, we organized and schematically summarized them in the upcoming tables. Indeed, the studies were divided based on the general topic they dealt with; therefore, the specific variables considered in each model included sperm retrieval (Table 2, four studies [37][38][39][40]); sperm quality, further divided into the investigation of sperm quality and morphology (Table 3a, seventeen studies [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57]) and quality of sperm and environmental factors (Table 3b, four studies [58][59][60][61]); non-obstructive azoospermia (Table 4, three studies [62][63][64]); IVF outcome (Table 5, three studies [65][66][67]); environmental and medical factors (Table 6, twelve studies [68][69][70][71][72][73][74][75]…”

Section: Resultsmentioning

confidence: 99%

Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature

Schmeis Arroyo,

Iosa,

Antonucci

et al. 2024

Healthcare

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Male infertility is a relevant public health problem, but there is no systematic review of the different machine learning (ML) models and their accuracy so far. The present review aims to comprehensively investigate the use of ML algorithms in predicting male infertility, thus reporting the accuracy of the used models in the prediction of male infertility as a primary outcome. Particular attention will be paid to the use of artificial neural networks (ANNs). A comprehensive literature search was conducted in PubMed, Scopus, and Science Direct between 15 July and 23 October 2023, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We performed a quality assessment of the included studies using the recommended tools suggested for the type of study design adopted. We also made a screening of the Risk of Bias (RoB) associated with the included studies. Thus, 43 relevant publications were included in this review, for a total of 40 different ML models detected. The studies included reported a good quality, even if RoB was not always good for all the types of studies. The included studies reported a median accuracy of 88% in predicting male infertility using ML models. We found only seven studies using ANN models for male infertility prediction, reporting a median accuracy of 84%.

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Section: Resultsmentioning

confidence: 99%

Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature

Schmeis Arroyo,

Iosa,

Antonucci

et al. 2024

Healthcare

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show abstract

“…AI applications can be utilized in several important fields [ 4 14 15 19 35 43 54 69 109 128 129 ]. A summary of AI applications in andrology is provided in Table 2 [ 9 43 44 57 61 63 79 82 130 131 132 133 134 135 136 137 138 139 140 141 ].…”

Section: Discussionmentioning

confidence: 99%

“…Supervised ML is used to develop a scoring calculator to identify patients with Klinefelter syndrome among azoospermic patients [ 18 ]. A random forest model has also been designed to predict improvement in post-varicocelectomy sperm analysis with superior performance compared to traditional logistic regression [ 9 ]. Other than that, several studies have also shown the benefit of ML for automated sperm classification, sperm selection, fertility prediction, and prediction of in vitro fertilization (IVF) outcomes [ 5 19 20 ].…”

Section: Artificial Intelligence: Machine Learning Neural Network And...mentioning

confidence: 99%

“…AI-based prediction models and automated semen analysis are set to become valuable tools that could support and aid in diagnosing and treating male infertility and improve the accuracy of patient care. These automated, AI-based predictions could offer consistency and efficiency in terms of time and cost in infertility research and clinical management [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics

Ghayda

Cannarella

Calogero

et al. 2024

World J Mens Health

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Artificial intelligence (AI) in medicine has gained a lot of momentum in the last decades and has been applied to various fields of medicine. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have facilitated the role of AI in modern healthcare. Similarly, as in other fields, AI applications, such as machine learning, artificial neural networks, and deep learning, have shown great potential in andrology and reproductive medicine. AI-based tools are poised to become valuable assets with abilities to support and aid in diagnosing and treating male infertility, and in improving the accuracy of patient care. These automated, AI-based predictions may offer consistency and efficiency in terms of time and cost in infertility research and clinical management. In andrology and reproductive medicine, AI has been used for objective sperm, oocyte, and embryo selection, prediction of surgical outcomes, cost-effective assessment, development of robotic surgery, and clinical decision-making systems. In the future, better integration and implementation of AI into medicine will undoubtedly lead to pioneering evidence-based breakthroughs and the reshaping of andrology and reproductive medicine.

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“…As research has progressed and evolved, machine learning models have been increasingly used to predict sperm parameters in MI ( Zeadna et al, 2020 ; Ory et al, 2022 ). Compared with traditional univariate analysis, machine learning usually uses multi-factor analysis methods, which fully consider the relationships between variables.…”

Section: Discussionmentioning

confidence: 99%

Identification and immuno-infiltration analysis of cuproptosis regulators in human spermatogenic dysfunction

Zhao

Liu

et al. 2023

Front. Genet.

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Introduction: Cuproptosis seems to promote the progression of diverse diseases. Hence, we explored the cuproptosis regulators in human spermatogenic dysfunction (SD), analyzed the condition of immune cell infiltration, and constructed a predictive model.Methods: Two microarray datasets (GSE4797 and GSE45885) related to male infertility (MI) patients with SD were downloaded from the Gene Expression Omnibus (GEO) database. We utilized the GSE4797 dataset to obtain differentially expressed cuproptosis-related genes (deCRGs) between SD and normal controls. The correlation between deCRGs and immune cell infiltration status was analyzed. We also explored the molecular clusters of CRGs and the status of immune cell infiltration. Notably, weighted gene co-expression network analysis (WGCNA) was used to identify the cluster-specific differentially expressed genes (DEGs). Moreso, gene set variation analysis (GSVA) was performed to annotate the enriched genes. Subsequently, we selected an optimal machine-learning model from four models. Finally, nomograms, calibration curves, decision curve analysis (DCA), and the GSE45885 dataset were utilized to verify the predictions’ accuracy.Results: Among SD and normal controls, we confirmed that there are deCRGs and activated immune responses. Through the GSE4797 dataset, we obtained 11 deCRGs. ATP7A, ATP7B, SLC31A1, FDX1, PDHA1, PDHB, GLS, CDKN2A, DBT, and GCSH were highly expressed in testicular tissues with SD, whereas LIAS was lowly expressed. Additionally, two clusters were identified in SD. Immune-infiltration analysis showed the existing heterogeneity of immunity at these two clusters. Cuproptosis-related molecular Cluster2 was marked by enhanced expressions of ATP7A, SLC31A1, PDHA1, PDHB, CDKN2A, DBT, and higher proportions of resting memory CD4+ T cells. Furthermore, an eXtreme Gradient Boosting (XGB) model based on 5-gene was built, which showed superior performance on the external validation dataset GSE45885 (AUC = 0.812). Therefore, the combined nomogram, calibration curve, and DCA results demonstrated the accuracy of predicting SD.Conclusion: Our study preliminarily illustrates the relationship between SD and cuproptosis. Moreover, a bright predictive model was developed.

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Artificial Intelligence Based Machine Learning Models Predict Sperm Parameter Upgrading after Varicocele Repair: A Multi-Institutional Analysis

Cited by 18 publications

References 28 publications

Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature

Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature

Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics

Identification and immuno-infiltration analysis of cuproptosis regulators in human spermatogenic dysfunction

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