Can methods of artificial intelligence aid in optimizing patient selection in patients undergoing intrauterine inseminations?
Purpose AI and its machine learning algorithms have proven useful in several fields of medicine, including medically assisted reproduction. The purpose of the study was to construct several predictive models based on clinical data and select the best models to predict IUI procedure outcomes. Methods...
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Published in | Journal of assisted reproduction and genetics Vol. 38; no. 7; pp. 1665 - 1673 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
New York
Springer US
01.07.2021
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Abstract | Purpose
AI and its machine learning algorithms have proven useful in several fields of medicine, including medically assisted reproduction. The purpose of the study was to construct several predictive models based on clinical data and select the best models to predict IUI procedure outcomes.
Methods
Clinical data (patient baseline characteristics, sperm quality, hormonal status, and cycle data) from 1029 IUI procedures performed in 413 couples stimulated by clomiphene citrate, letrozole, or gonadotropins were used to build several models to predict clinical pregnancy. The models included ANN, random forest, PLS, SVM, and linear models using the caret package in R. The models were evaluated using ROC analysis by means of random CV on test data.
Results
Out of the best performing models, the random forest model achieved an AUC of 0.66, a sensitivity of 0.432, and a specificity of 0.756. This performance was followed by the PLS model, which achieved a sensitivity of 0.459 and specificity of 0.734. The other models achieved significantly lower AUCs. When adjusting the predictive cutoff value, confusion matrices show that clinical pregnancy is twice as likely in the case of positive prediction.
Conclusion
Among the compared methods, the random forest and PLS models demonstrated superior performance in predicting the clinical outcome of IUI. With additional research and clinical validation, AI methods may be successfully used in improving patient selection and consequently lead to better clinical results. |
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AbstractList | Purpose
AI and its machine learning algorithms have proven useful in several fields of medicine, including medically assisted reproduction. The purpose of the study was to construct several predictive models based on clinical data and select the best models to predict IUI procedure outcomes.
Methods
Clinical data (patient baseline characteristics, sperm quality, hormonal status, and cycle data) from 1029 IUI procedures performed in 413 couples stimulated by clomiphene citrate, letrozole, or gonadotropins were used to build several models to predict clinical pregnancy. The models included ANN, random forest, PLS, SVM, and linear models using the caret package in R. The models were evaluated using ROC analysis by means of random CV on test data.
Results
Out of the best performing models, the random forest model achieved an AUC of 0.66, a sensitivity of 0.432, and a specificity of 0.756. This performance was followed by the PLS model, which achieved a sensitivity of 0.459 and specificity of 0.734. The other models achieved significantly lower AUCs. When adjusting the predictive cutoff value, confusion matrices show that clinical pregnancy is twice as likely in the case of positive prediction.
Conclusion
Among the compared methods, the random forest and PLS models demonstrated superior performance in predicting the clinical outcome of IUI. With additional research and clinical validation, AI methods may be successfully used in improving patient selection and consequently lead to better clinical results. AI and its machine learning algorithms have proven useful in several fields of medicine, including medically assisted reproduction. The purpose of the study was to construct several predictive models based on clinical data and select the best models to predict IUI procedure outcomes. Clinical data (patient baseline characteristics, sperm quality, hormonal status, and cycle data) from 1029 IUI procedures performed in 413 couples stimulated by clomiphene citrate, letrozole, or gonadotropins were used to build several models to predict clinical pregnancy. The models included ANN, random forest, PLS, SVM, and linear models using the caret package in R. The models were evaluated using ROC analysis by means of random CV on test data. Out of the best performing models, the random forest model achieved an AUC of 0.66, a sensitivity of 0.432, and a specificity of 0.756. This performance was followed by the PLS model, which achieved a sensitivity of 0.459 and specificity of 0.734. The other models achieved significantly lower AUCs. When adjusting the predictive cutoff value, confusion matrices show that clinical pregnancy is twice as likely in the case of positive prediction. Among the compared methods, the random forest and PLS models demonstrated superior performance in predicting the clinical outcome of IUI. With additional research and clinical validation, AI methods may be successfully used in improving patient selection and consequently lead to better clinical results. Abstract Purpose AI and its machine learning algorithms have proven useful in several fields of medicine, including medically assisted reproduction. The purpose of the study was to construct several predictive models based on clinical data and select the best models to predict IUI procedure outcomes. Methods Clinical data (patient baseline characteristics, sperm quality, hormonal status, and cycle data) from 1029 IUI procedures performed in 413 couples stimulated by clomiphene citrate, letrozole, or gonadotropins were used to build several models to predict clinical pregnancy. The models included ANN, random forest, PLS, SVM, and linear models using the caret package in R. The models were evaluated using ROC analysis by means of random CV on test data. Results Out of the best performing models, the random forest model achieved an AUC of 0.66, a sensitivity of 0.432, and a specificity of 0.756. This performance was followed by the PLS model, which achieved a sensitivity of 0.459 and specificity of 0.734. The other models achieved significantly lower AUCs. When adjusting the predictive cutoff value, confusion matrices show that clinical pregnancy is twice as likely in the case of positive prediction. Conclusion Among the compared methods, the random forest and PLS models demonstrated superior performance in predicting the clinical outcome of IUI. With additional research and clinical validation, AI methods may be successfully used in improving patient selection and consequently lead to better clinical results. PURPOSEAI and its machine learning algorithms have proven useful in several fields of medicine, including medically assisted reproduction. The purpose of the study was to construct several predictive models based on clinical data and select the best models to predict IUI procedure outcomes. METHODSClinical data (patient baseline characteristics, sperm quality, hormonal status, and cycle data) from 1029 IUI procedures performed in 413 couples stimulated by clomiphene citrate, letrozole, or gonadotropins were used to build several models to predict clinical pregnancy. The models included ANN, random forest, PLS, SVM, and linear models using the caret package in R. The models were evaluated using ROC analysis by means of random CV on test data. RESULTSOut of the best performing models, the random forest model achieved an AUC of 0.66, a sensitivity of 0.432, and a specificity of 0.756. This performance was followed by the PLS model, which achieved a sensitivity of 0.459 and specificity of 0.734. The other models achieved significantly lower AUCs. When adjusting the predictive cutoff value, confusion matrices show that clinical pregnancy is twice as likely in the case of positive prediction. CONCLUSIONAmong the compared methods, the random forest and PLS models demonstrated superior performance in predicting the clinical outcome of IUI. With additional research and clinical validation, AI methods may be successfully used in improving patient selection and consequently lead to better clinical results. |
Author | Kovač, Vilma Kozar, Nejc Reljič, Milan |
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BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34031765$$D View this record in MEDLINE/PubMed |
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CitedBy_id | crossref_primary_10_1007_s10815_024_03153_2 crossref_primary_10_1097_GCO_0000000000000796 crossref_primary_10_1371_journal_pone_0294727 |
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Keywords | Random forest Neural network Intrauterine insemination Artificial intelligence Partial least squares |
Language | English |
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AI and its machine learning algorithms have proven useful in several fields of medicine, including medically assisted reproduction. The purpose of the... AI and its machine learning algorithms have proven useful in several fields of medicine, including medically assisted reproduction. The purpose of the study... Abstract Purpose AI and its machine learning algorithms have proven useful in several fields of medicine, including medically assisted reproduction. The... PurposeAI and its machine learning algorithms have proven useful in several fields of medicine, including medically assisted reproduction. The purpose of the... PURPOSEAI and its machine learning algorithms have proven useful in several fields of medicine, including medically assisted reproduction. The purpose of the... |
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SubjectTerms | Adult Artificial Intelligence Assisted Reproduction Technologies Citric acid Clomiphene Clomiphene - therapeutic use Decision Making, Computer-Assisted Female Fertility Agents, Female - therapeutic use Gonadotropins Gonadotropins - therapeutic use Gynecology Human Genetics Humans Insemination, Artificial - methods Learning algorithms Letrozole - therapeutic use Machine learning Male Medicine Medicine & Public Health Neural Networks, Computer Patient Selection Patients Pituitary (anterior) Prediction models Pregnancy Reproductive Medicine Spermatozoa - cytology Spermatozoa - physiology Support Vector Machine |
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Title | Can methods of artificial intelligence aid in optimizing patient selection in patients undergoing intrauterine inseminations? |
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