Genetic algorithm–assisted machine learning for clinical pregnancy prediction in in vitro fertilizationAJOG Global Reports at a Glance
BACKGROUND: A clinical pregnancy prediction model was developed by implementing machine learning technology that uses a combination of static images and medical data to calculate the outcome of an in vitro fertilization cycle.OBJECTIVE: To provide a system that can accurately and sufficiently assist...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-02-01
|
| Series: | AJOG Global Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666577822000818 |
| _version_ | 1797869667626254336 |
|---|---|
| author | Claudio Michael Louis, SKom, BEng Nining Handayani, DVM, MBiomed Tri Aprilliana, BPH Arie A. Polim, MD, SpOG, DMAS, MBHRE Arief Boediono, DVM, PhD Ivan Sini, MD, FRANZOG, GDRM, MMIS, SpOG |
| author_facet | Claudio Michael Louis, SKom, BEng Nining Handayani, DVM, MBiomed Tri Aprilliana, BPH Arie A. Polim, MD, SpOG, DMAS, MBHRE Arief Boediono, DVM, PhD Ivan Sini, MD, FRANZOG, GDRM, MMIS, SpOG |
| author_sort | Claudio Michael Louis, SKom, BEng |
| collection | DOAJ |
| description | BACKGROUND: A clinical pregnancy prediction model was developed by implementing machine learning technology that uses a combination of static images and medical data to calculate the outcome of an in vitro fertilization cycle.OBJECTIVE: To provide a system that can accurately and sufficiently assist with decision making that is critical to in vitro fertilization cycles, primarily embryo selection.STUDY DESIGN: Historical medical data, which consist of clinical information and a complete transferred embryo image dataset, of 697 patients who underwent unique in vitro fertilization were collected. Various techniques of machine learning were used, namely decision tree, random forest, and gradient boosting; each technique used the same data configuration for performance comparison and was subsequently optimized using genetic algorithm.RESULTS: A prediction model with a peak accuracy of approximately 65% was achieved. Significant differences in the performances of the 3 selected algorithms were apparent. Nonetheless, additional metric measurements, such as receiver operating characteristic, area under the receiver operating characteristic curve score, accuracy, and loss, suggested that the gradient boosting model performed the best in predicting clinical pregnancy.CONCLUSION: This study served as a stepping stone toward the application of in vitro fertilization prediction models that use machine learning techniques. However, additional validation steps are required to boost the model's performance for its implementation in the clinical setting. |
| first_indexed | 2024-04-10T00:15:08Z |
| format | Article |
| id | doaj.art-1a034172ec58426cbc1ee0facb55738e |
| institution | Directory Open Access Journal |
| issn | 2666-5778 |
| language | English |
| last_indexed | 2024-04-10T00:15:08Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | AJOG Global Reports |
| spelling | doaj.art-1a034172ec58426cbc1ee0facb55738e2023-03-16T05:06:23ZengElsevierAJOG Global Reports2666-57782023-02-0131100133Genetic algorithm–assisted machine learning for clinical pregnancy prediction in in vitro fertilizationAJOG Global Reports at a GlanceClaudio Michael Louis, SKom, BEng0Nining Handayani, DVM, MBiomed1Tri Aprilliana, BPH2Arie A. Polim, MD, SpOG, DMAS, MBHRE3Arief Boediono, DVM, PhD4Ivan Sini, MD, FRANZOG, GDRM, MMIS, SpOG5IRSI Research and Training Centre, Jakarta, Indonesia (Mr Claudio, Mses Handayani and Aprilliana, and Drs Polim, Boediono, and Sini); Corresponding author: Claudio Michael Louis, SKom, BEng.IRSI Research and Training Centre, Jakarta, Indonesia (Mr Claudio, Mses Handayani and Aprilliana, and Drs Polim, Boediono, and Sini); Morula IVF Jakarta Clinic, Jakarta, Indonesia (Ms Handayani and Drs Polim, Boediono, and Sini)IRSI Research and Training Centre, Jakarta, Indonesia (Mr Claudio, Mses Handayani and Aprilliana, and Drs Polim, Boediono, and Sini)IRSI Research and Training Centre, Jakarta, Indonesia (Mr Claudio, Mses Handayani and Aprilliana, and Drs Polim, Boediono, and Sini); Morula IVF Jakarta Clinic, Jakarta, Indonesia (Ms Handayani and Drs Polim, Boediono, and Sini); Department of Obstetrics and Gynecology, School of Medicine and Health Sciences, Atma Jaya Catholic University of Indonesia, Jakarta, Indonesia (Dr Polim)IRSI Research and Training Centre, Jakarta, Indonesia (Mr Claudio, Mses Handayani and Aprilliana, and Drs Polim, Boediono, and Sini); Morula IVF Jakarta Clinic, Jakarta, Indonesia (Ms Handayani and Drs Polim, Boediono, and Sini); Department of Anatomy, Physiology, and Pharmacology, Institut Pertanian Bogor University, Bogor, Indonesia (Dr Boediono).IRSI Research and Training Centre, Jakarta, Indonesia (Mr Claudio, Mses Handayani and Aprilliana, and Drs Polim, Boediono, and Sini); Morula IVF Jakarta Clinic, Jakarta, Indonesia (Ms Handayani and Drs Polim, Boediono, and Sini)BACKGROUND: A clinical pregnancy prediction model was developed by implementing machine learning technology that uses a combination of static images and medical data to calculate the outcome of an in vitro fertilization cycle.OBJECTIVE: To provide a system that can accurately and sufficiently assist with decision making that is critical to in vitro fertilization cycles, primarily embryo selection.STUDY DESIGN: Historical medical data, which consist of clinical information and a complete transferred embryo image dataset, of 697 patients who underwent unique in vitro fertilization were collected. Various techniques of machine learning were used, namely decision tree, random forest, and gradient boosting; each technique used the same data configuration for performance comparison and was subsequently optimized using genetic algorithm.RESULTS: A prediction model with a peak accuracy of approximately 65% was achieved. Significant differences in the performances of the 3 selected algorithms were apparent. Nonetheless, additional metric measurements, such as receiver operating characteristic, area under the receiver operating characteristic curve score, accuracy, and loss, suggested that the gradient boosting model performed the best in predicting clinical pregnancy.CONCLUSION: This study served as a stepping stone toward the application of in vitro fertilization prediction models that use machine learning techniques. However, additional validation steps are required to boost the model's performance for its implementation in the clinical setting.http://www.sciencedirect.com/science/article/pii/S2666577822000818clinical pregnancyin vitro fertilizationmachine learningprediction modelsingle embryo transfer |
| spellingShingle | Claudio Michael Louis, SKom, BEng Nining Handayani, DVM, MBiomed Tri Aprilliana, BPH Arie A. Polim, MD, SpOG, DMAS, MBHRE Arief Boediono, DVM, PhD Ivan Sini, MD, FRANZOG, GDRM, MMIS, SpOG Genetic algorithm–assisted machine learning for clinical pregnancy prediction in in vitro fertilizationAJOG Global Reports at a Glance AJOG Global Reports clinical pregnancy in vitro fertilization machine learning prediction model single embryo transfer |
| title | Genetic algorithm–assisted machine learning for clinical pregnancy prediction in in vitro fertilizationAJOG Global Reports at a Glance |
| title_full | Genetic algorithm–assisted machine learning for clinical pregnancy prediction in in vitro fertilizationAJOG Global Reports at a Glance |
| title_fullStr | Genetic algorithm–assisted machine learning for clinical pregnancy prediction in in vitro fertilizationAJOG Global Reports at a Glance |
| title_full_unstemmed | Genetic algorithm–assisted machine learning for clinical pregnancy prediction in in vitro fertilizationAJOG Global Reports at a Glance |
| title_short | Genetic algorithm–assisted machine learning for clinical pregnancy prediction in in vitro fertilizationAJOG Global Reports at a Glance |
| title_sort | genetic algorithm assisted machine learning for clinical pregnancy prediction in in vitro fertilizationajog global reports at a glance |
| topic | clinical pregnancy in vitro fertilization machine learning prediction model single embryo transfer |
| url | http://www.sciencedirect.com/science/article/pii/S2666577822000818 |
| work_keys_str_mv | AT claudiomichaellouisskombeng geneticalgorithmassistedmachinelearningforclinicalpregnancypredictionininvitrofertilizationajogglobalreportsataglance AT nininghandayanidvmmbiomed geneticalgorithmassistedmachinelearningforclinicalpregnancypredictionininvitrofertilizationajogglobalreportsataglance AT triaprillianabph geneticalgorithmassistedmachinelearningforclinicalpregnancypredictionininvitrofertilizationajogglobalreportsataglance AT arieapolimmdspogdmasmbhre geneticalgorithmassistedmachinelearningforclinicalpregnancypredictionininvitrofertilizationajogglobalreportsataglance AT ariefboedionodvmphd geneticalgorithmassistedmachinelearningforclinicalpregnancypredictionininvitrofertilizationajogglobalreportsataglance AT ivansinimdfranzoggdrmmmisspog geneticalgorithmassistedmachinelearningforclinicalpregnancypredictionininvitrofertilizationajogglobalreportsataglance |