Artificial intelligence in medicine and male infertility

Lamb, Dolores J.; Niederberger, Craig S.

doi:10.1007/bf00182040

Cited by 20 publications

(18 citation statements)

References 58 publications

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“…In another study that was performed using human data, an ANN and logistic regression were compared [29]. The results were parallel with the findings in [9]. When dealing with fertility data, several reasons can be given for the insufficiency of the statistical methods when compared to ANNs.…”

Section: Related Worksupporting

confidence: 54%

“…The abilities of an ANN are its main advantages when compared to traditional programming and statistical methods. ANNs are applicable in many areas as well as in the decision and classification problems found in medical and biomedical fields, such as the diagnosis of various diseases (including hypertension, cancer, rheumatic diseases, and vertigo), the analysis of medical images obtained by MRI and X-rays, and the prediction of a history of a disease [9]. Similarly, an ANN is the frequently used method in the diagnosis of prostate cancer [10]- [16], detecting changes in tumor and cell structures [17]- [22], tracking periodic differences in retinal images [23] and various urological dysfunctions [16], [24]- [28].…”

Section: Related Workmentioning

confidence: 99%

“…The earliest study on infertility using an ANN was performed with data obtained from hamsters [9]. In this study, the classification of the ANN was compared with linear discriminant function analysis (LDFA) and quadratic discriminant function analysis (QFDA).…”

Section: Related Workmentioning

confidence: 99%

“…The most important point is that semen analysis (SA), which is the main test of the male infertility potential, consists of several different parameters and the results of these tests are psychologically affected. The second major factor is that the data obtained from the patients are often segmented from multiple subjects and inconsistencies occasionally exist between these data [9]. In this respect, ANNs have a significant advantage over statistical methods because they are able to cluster large non-linear data sets flexibly and cost-effectively.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

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

Candemir¹

2018

IJMLC

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Abstract-Infertility is a major problem that directly affects many people worldwide. In recent years, fertility rates have decreased by up to 15% in young men. Changing living conditions, stress levels, environmental factors and nutritional habits play an important role in infertility. This paper suggests a novel method to estimate semen quality from lifestyle, environmental factors and daily habits using radial basis function neural networks. The accuracy of the suggested method was measured as 90%. The results show that the proposed method can be applied as a practical, fast and cheap alternative method to laboratory tests.

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Section: Related Worksupporting

confidence: 54%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Candemir¹

2018

IJMLC

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“…To our knowledge, these methods have not yet been utilized to predict the presence of endocrinopathy based solely on clinical or semen analysis data. 3,4 …”

Section: Introduction and Objectivesmentioning

confidence: 99%

Computational models for detection of endocrinopathy in subfertile males

et al. 2007

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The observation that men with sperm density greater than 10 million/ml had low probability of endocrinopathy led to a refinement in the evaluation of subfertility. Using statistical methods, we sought to provide a more accurate prediction of which patients have an endocrinopathy, and to report the outcome as the odds of having disease. In addition, by examining the parameters that influenced the model significantly, the underlying pathophysiology might be better understood. Records of 1035 men containing variables including testis volume, sperm density, motility as well as the presence of endocrinopathy were randomized into 'training' and 'test' data sets. We modeled the data set using linear and quadratic discriminant function analysis, logistic regression (LR) and a neural network. Wilk's regression analysis was performed to determine which variables influenced the model significantly. Of the four models investigated, LR and a neural network performed the best with receiver operating characteristic areas under the curve of 0.93 and 0.95, respectively, correlating to a sensitivity of 28% and a specificity of 99% for the LR model, and a sensitivity and specificity of 56 and 97% for the neural network model. Reverse regression yielded P-values for the testis volume and sperm density of o0.0001. The neural network and LR models accurately predicted the probability of an endocrinopathy from testis volume, sperm density and motility without serum assays. These models may be accessed via the Internet, allowing urologists to select patients for endocrinologic evaluation at

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Prospect and adversity of artificial intelligence in urology

Eminaga¹,

Liao²

2021

Artificial Intelligence in Medicine

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Artificial intelligence in medicine and male infertility

Cited by 20 publications

References 58 publications

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

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

Computational models for detection of endocrinopathy in subfertile males

Prospect and adversity of artificial intelligence in urology

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