Estimating the Semen Quality from Life Style Using Fuzzy Radial Basis Functions

Candemir, Cemre

doi:10.18178/ijmlc.2018.8.1.661

Cited by 14 publications

(13 citation statements)

References 27 publications

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“…Although they used different types of AI models, Girela et al [18], El-Shafeiy et al [19], and Candemir [24] applied similar types of data to the task of classifying semen data. All of them used the life habits of male patients and achieved very positive results (Table 1).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Sperm selection is also a decisive criterion to improve the prediction of male fertility and ART outcomes. Most of the publications involving semen quality attained high accuracy [19,20,24], but they also showed limitations such as small datasets and unbalanced data (Table 9). These factors may impact the final accuracy of the models; it is important to have a considerable quantity of data to train and use unbiased data, so that the AI system correctly learns all types of clinically meaningful categories.…”

Section: Clinical Applications Of Ai Techniquesmentioning

confidence: 99%

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Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data

Fernandez

Ferreira

Cecílio

et al. 2020

J Assist Reprod Genet

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Over the past years, the assisted reproductive technologies (ARTs) have been accompanied by constant innovations. For instance, intracytoplasmic sperm injection (ICSI), time-lapse monitoring of the embryonic morphokinetics, and PGS are innovative techniques that increased the success of the ART. In the same trend, the use of artificial intelligence (AI) techniques is being intensively researched whether in the embryo or spermatozoa selection. Despite several studies already published, the use of AI within assisted reproduction clinics is not yet a reality. This is largely due to the different AI techniques that are being proposed to be used in the daily routine of the clinics, which causes some uncertainty in their use. To shed light on this complex scenario, this review briefly describes some of the most frequently used AI algorithms, their functionalities, and their potential use. Several databases were analyzed in search of articles where applied artificial intelligence algorithms were used on reproductive data. Our focus was on the classification of embryonic cells and semen samples. Of a total of 124 articles analyzed, 32 were selected for this review. From the proposed algorithms, most have achieved a satisfactory precision, demonstrating the potential of a wide range of AI techniques. However, the evaluation of these studies suggests the need for more standardized research to validate the proposed models and their algorithms. Routine use of AI in assisted reproduction clinics is just a matter of time. However, the choice of AI technique to be used is supported by a better understanding of the principles subjacent to each technique, that is, its robustness, pros, and cons. We provide some current (although incipient) and potential uses of AI on the clinic routine, discussing how accurate and friendly it could be. Finally, we propose some standards for AI research on the selection of the embryo to be transferred and other future hints. For us, the imminence of its use is evident, providing a revolutionary milestone that will impact the ART.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Clinical Applications Of Ai Techniquesmentioning

confidence: 99%

Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data

Fernandez

Ferreira

Cecílio

et al. 2020

J Assist Reprod Genet

View full text Add to dashboard Cite

show abstract

“…Considering such factors, many investigators have paired predictive analytics with AI techniques in their studies to demonstrate how AI could be of assistance in reproductive urology. In the studies by Gil et al [ 23 ] and Candemir et al [ 24 ], AI networks and algorithmic models were used to predict semen quality by considering variables such as lifestyle and environmental factors. Both studies displayed high accuracies, the first study showing an accuracy of ~86% for sperm concentration and 73–76% for motility and the second showing an accuracy of ~90%.…”

Section: Diagnosismentioning

confidence: 99%

Artificial Intelligence and Its Impact on Urological Diseases and Management: A Comprehensive Review of the Literature

Hameed

Dhavileswarapu

Raza

et al. 2021

JCM

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Recent advances in artificial intelligence (AI) have certainly had a significant impact on the healthcare industry. In urology, AI has been widely adopted to deal with numerous disorders, irrespective of their severity, extending from conditions such as benign prostate hyperplasia to critical illnesses such as urothelial and prostate cancer. In this article, we aim to discuss how algorithms and techniques of artificial intelligence are equipped in the field of urology to detect, treat, and estimate the outcomes of urological diseases. Furthermore, we explain the advantages that come from using AI over any existing traditional methods.

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“…Engy et al [18] conducted a comparative analysis using five AI techniques: ANN, ANN-GA, DT, SVM, and ANN-SWA for male fertility detection, and their reported accuracies were 90%, 95%, 88%, 95%, and 99.96%, respectively. Candemir et al [19] used four Machine Learning (ML) techniques, such as MLP, SVM, DT, and FRBF, to detect male fertility. Of all, the FRBF classifier outperformed, and 90% accuracy was reported.…”

Section: Introductionmentioning

confidence: 99%

Explainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTE

2022

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Infertility is a common problem across the world. Infertility distribution due to male factors ranges from 40% to 50%. Existing artificial intelligence (AI) systems are not often human interpretable. Further, clinicians are unaware of how data analytical tools make decisions, and as a result, they have limited exposure to healthcare. Using explainable AI tools makes AI systems transparent and traceable, enhancing users’ trust and confidence in decision-making. The main contribution of this study is to introduce an explainable model for investigating male fertility prediction. Nine features related to lifestyle and environmental factors are utilized to develop a male fertility prediction model. Five AI tools, namely support vector machine, adaptive boosting, conventional extreme gradient boost (XGB), random forest, and extra tree algorithms are deployed with a balanced and imbalanced dataset. To produce our model in a trustworthy way, an explainable AI is applied. The techniques are (1) local interpretable model-agnostic explanations (LIME) and (2) Shapley additive explanations (SHAP). Additionally, ELI5 is utilized to inspect the feature’s importance. Finally, XGB outperformed and obtained an AUC of 0.98, which is optimal compared to existing AI systems.

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Estimating the Semen Quality from Life Style Using Fuzzy Radial Basis Functions

Cited by 14 publications

References 27 publications

Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data

Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data

Artificial Intelligence and Its Impact on Urological Diseases and Management: A Comprehensive Review of the Literature

Explainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTE

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