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Wang S, Chen L, Sun H. Interpretable artificial intelligence-assisted embryo selection improved single-blastocyst transfer outcomes: a prospective cohort study. Reprod Biomed Online 2023; 47:103371. [PMID: 37839212 DOI: 10.1016/j.rbmo.2023.103371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/01/2023] [Accepted: 08/23/2023] [Indexed: 10/17/2023]
Abstract
RESEARCH QUESTION What is the pregnancy and neonatal outcomes of an interpretable artificial intelligence (AI) model for embryo selection in a prospective clinical trial? DESIGN This single-centre prospective cohort study was carried out from October 2021 to March 2022. A total of 330 eligible patients were assigned to their preferred groups, with 250 patients undergoing a fresh single-blastocyst transfer cycle after the exclusion criteria had been applied. For the AI-assisted group (AAG), embryologists selected the embryos for transfer based on the ranking recommendations provided by an interpretable AI system, while with the manual group, embryologists used the Gardner grading system to make their decisions. RESULTS The implantation rate was significantly higher in the AAG than the manual group (80.87% versus 68.15%, P = 0.022). No significant difference was found in terms of monozygotic twin rate, miscarriage rate, live birth rate and ectopic pregnancy rate between the groups. Furthermore, there was no significant difference in terms of neonatal outcomes, including gestational weeks, premature birth rate, birth height, birthweight, sex ratio at birth and newborn malformation rate. The consensus rate between the AI and retrospective analysis by the embryologists was significantly higher for good-quality embryos (i.e. grade 4BB or higher) versus poor-quality embryos (i.e. less than 4BB) (84.71% versus 25%, P < 0.001). CONCLUSIONS These prospective trial results suggest that the proposed AI system could effectively help embryologists to improve the implantation rate with single-blastocyst transfer compared with traditional manual evaluation methods.
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Affiliation(s)
- Shanshan Wang
- Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Lei Chen
- Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Haixiang Sun
- Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
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2
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Serdarogullari M, Raad G, Yarkiner Z, Bazzi M, Mourad Y, Alpturk S, Fakih F, Fakih C, Liperis G. Identifying predictors of Day 5 blastocyst utilization rate using an artificial neural network. Reprod Biomed Online 2023; 47:103399. [PMID: 37862857 DOI: 10.1016/j.rbmo.2023.103399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 09/02/2023] [Accepted: 09/05/2023] [Indexed: 10/22/2023]
Abstract
RESEARCH QUESTION Can artificial intelligence identify predictors of an increased Day 5 blastocyst utilization rate (D5BUR), which is one of the most informative key performance indicators in an IVF laboratory? DESIGN This retrospective, multicentre study evaluated six variables for predicting D5BUR using an artificial neural network (ANN): number of metaphase II (MII) oocytes injected (intracytoplasmic sperm injection); use of autologous/donated gametes; maternal age at oocyte retrieval; sperm concentration; progressive sperm motility rate; and fertilization rate. Cycles were divided into training and testing sets through stratified random sampling. D5BUR on Day 5 was grouped into <60% and ≥60% as per the Vienna consensus benchmark values. RESULTS The area under the receiver operating characteristic curve (AUC) to predict the D5BUR groups was 80.2%. From the ANN model, all six independent variables were found to be of significant value for the prediction of D5BUR (P<0.0001), with the most important variable being the number of MII oocytes injected. Investigation of the effect of MII oocytes injected on D5BUR indicated an inverse correlation, with injection of an increasing number of MII oocytes resulting in a decreasing D5BUR (r=-0.344, P<0.001) and injection of up to six oocytes resulting in D5BUR ≥60%. CONCLUSION The number of MII oocytes injected is the most important predictor of D5BUR. Exploration of additional variables and further validation of models that can predict D5BUR can guide the way towards personalized treatment and increased safety.
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Affiliation(s)
| | - Georges Raad
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon; Faculty of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon
| | - Zalihe Yarkiner
- Cyprus International University, Faculty of Arts and Sciences, Department of Basic Sciences and Humanities, Northern Cyprus via Mersin 10, Turkey
| | - Marwa Bazzi
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon
| | - Youmna Mourad
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon
| | | | - Fadi Fakih
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon
| | - Chadi Fakih
- Al Hadi Laboratory and Medical Centre, Beirut, Lebanon
| | - George Liperis
- Westmead Fertility Centre, Institute of Reproductive Medicine, University of Sydney, Westmead, NSW, Australia.
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3
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Yuan G, Lv B, Hao C. Application of artificial neural networks in reproductive medicine. HUM FERTIL 2023; 26:1195-1201. [PMID: 36628627 DOI: 10.1080/14647273.2022.2156301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 09/01/2022] [Indexed: 01/12/2023]
Abstract
With the emergence of the age of information, the data on reproductive medicine has improved immensely. Nonetheless, healthcare workers who wish to utilise the relevance and implied value of the various data available to aid clinical decision-making encounter the difficulty of statistically analysing such large data. The application of artificial intelligence becoming widespread in recent years has emerged as a turning point in this regard. Artificial neural networks (ANNs) exhibit beneficial characteristics of comprehensive analysis and autonomous learning, owing to which these are being applied to disease diagnosis, embryo quality assessment, and prediction of pregnancy outcomes. The present report aims to summarise the application of ANNs in the field of reproduction and analyse its further application potential.
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Affiliation(s)
- Guanghui Yuan
- Department of Qingdao Medical College, Qingdao University, Qingdao, Shandong, China
| | - Bohan Lv
- Department of Intensive Care Unit, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Cuifang Hao
- Department of Reproductive Medicine, The Affiliated Women and Children's Hospital of Qingdao University, Qingdao, Shandong, China
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4
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Berman A, Anteby R, Efros O, Klang E, Soffer S. Deep learning for embryo evaluation using time-lapse: a systematic review of diagnostic test accuracy. Am J Obstet Gynecol 2023; 229:490-501. [PMID: 37116822 DOI: 10.1016/j.ajog.2023.04.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 03/28/2023] [Accepted: 04/19/2023] [Indexed: 04/30/2023]
Abstract
OBJECTIVE This study aimed to investigate the accuracy of convolutional neural network models in the assessment of embryos using time-lapse monitoring. DATA SOURCES A systematic search was conducted in PubMed and Web of Science databases from January 2016 to December 2022. The search strategy was carried out by using key words and MeSH (Medical Subject Headings) terms. STUDY ELIGIBILITY CRITERIA Studies were included if they reported the accuracy of convolutional neural network models for embryo evaluation using time-lapse monitoring. The review was registered with PROSPERO (International Prospective Register of Systematic Reviews; identification number CRD42021275916). METHODS Two reviewer authors independently screened results using the Covidence systematic review software. The full-text articles were reviewed when studies met the inclusion criteria or in any uncertainty. Nonconsensus was resolved by a third reviewer. Risk of bias and applicability were evaluated using the QUADAS-2 tool and the modified Joanna Briggs Institute or JBI checklist. RESULTS Following a systematic search of the literature, 22 studies were identified as eligible for inclusion. All studies were retrospective. A total of 522,516 images of 222,998 embryos were analyzed. Three main outcomes were evaluated: successful in vitro fertilization, blastocyst stage classification, and blastocyst quality. Most studies reported >80% accuracy, and embryologists were outperformed in some. Ten studies had a high risk of bias, mostly because of patient bias. CONCLUSION The application of artificial intelligence in time-lapse monitoring has the potential to provide more efficient, accurate, and objective embryo evaluation. Models that examined blastocyst stage classification showed the best predictions. Models that predicted live birth had a low risk of bias, used the largest databases, and had external validation, which heightens their relevance to clinical application. Our systematic review is limited by the high heterogeneity among the included studies. Researchers should share databases and standardize reporting.
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Affiliation(s)
- Aya Berman
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
| | - Roi Anteby
- Department of Surgery and Transplantation B, Chaim Sheba Medical Center, Tel Hashomer, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Orly Efros
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; National Hemophilia Center and Institute of Thrombosis & Hemostasis, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Eyal Klang
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Institute for Health Care Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY; Deep Vision Lab, Chaim Sheba Medical Center, Ramat Gan, Israel; Division of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Shelly Soffer
- Deep Vision Lab, Chaim Sheba Medical Center, Ramat Gan, Israel; Internal Medicine B, Assuta Medical Center, Ashdod, Israel; Ben-Gurion University of the Negev, Be'er Sheva, Israel
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Zhu X, Zhu Z, Gu L, Chen L, Zhan Y, Li X, Huang C, Xu J, Li J. Prediction models and associated factors on the fertility behaviors of the floating population in China. Front Public Health 2022; 10:977103. [PMID: 36187657 PMCID: PMC9521649 DOI: 10.3389/fpubh.2022.977103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 08/15/2022] [Indexed: 01/25/2023] Open
Abstract
The floating population has been growing rapidly in China, and their fertility behaviors do affect urban management and development. Based on the data set of the China Migrants Dynamic Survey in 2016, the logistic regression model and multiple linear regression model were used to explore the related factors of fertility behaviors among the floating populace. The artificial neural network model, the naive Bayes model, and the logistic regression model were used for prediction. The findings showed that age, gender, ethnic, household registration, education level, occupation, duration of residence, scope of migration, housing, economic conditions, and health services all affected the reproductive behavior of the floating population. Among them, the improvement duration of post-migration residence and family economic conditions positively impacted their fertility behavior. Non-agricultural new industry workers with college degrees or above living in first-tier cities were less likely to have children and more likely to delay childbearing. Among the prediction models, both the artificial neural network model and logistic regression model had better prediction effects. Improving the employment and income of new industry workers, and introducing preferential housing policies might improve their probability of bearing children. The artificial neural network and logistic regression model could predict individual fertility behavior and provide a scientific basis for the urban population management.
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Affiliation(s)
- Xiaoxia Zhu
- Department of Epidemiology & Biostatistics, and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Zhixin Zhu
- Department of Epidemiology & Biostatistics, and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Lanfang Gu
- Department of Epidemiology & Biostatistics, and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Liang Chen
- Department of Epidemiology & Biostatistics, and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Yancen Zhan
- Department of Epidemiology & Biostatistics, and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Xiuyang Li
- Department of Epidemiology & Biostatistics, and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China,*Correspondence: Xiuyang Li
| | - Cheng Huang
- Zhejiang University Library, Zhejiang University, Hangzhou, China
| | - Jiangang Xu
- Zhejiang University Library, Zhejiang University, Hangzhou, China
| | - Jie Li
- Zhejiang University Library, Zhejiang University, Hangzhou, China
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6
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Enatsu N, Miyatsuka I, An LM, Inubushi M, Enatsu K, Otsuki J, Iwasaki T, Kokeguchi S, Shiotani M. A novel system based on artificial intelligence for predicting blastocyst viability and visualizing the explanation. Reprod Med Biol 2022; 21:e12443. [PMID: 35386375 PMCID: PMC8967284 DOI: 10.1002/rmb2.12443] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 01/16/2022] [Accepted: 01/18/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose The purpose of the study was to invent and evaluate the novel artificial intelligence (AI) system named Fertility image Testing Through Embryo (FiTTE) for predicting blastocyst viability and visualizing the explanations via gradient‐based localization. Methods The authors retrospectively analyzed 19 342 static blastocyst images with related inspection histories from 9961 infertile patients who underwent in vitro fertilization. Among these data, 17 984 cycles of single‐blastocyst transfer were used for training, and data from 1358 cycles were used for testing purposes. Results The prediction accuracy for clinical pregnancy achieved by a control model using conventional Gardner scoring system was 59.8%, and area under the curve (AUC) was 0.62. FiTTE improved the prediction accuracy by using blastocyst images to 62.7% and AUC of 0.68. Additionally, the accuracy achieved by an ensemble model using image plus clinical data was 65.2% and AUC was 0.71, representing an improvement in prediction accuracy. The visualization algorithm showed brighter colors with blastocysts that resulted in clinical pregnancy. Conclusions The authors invented the novel AI system, FiTTE, which could provide more precise prediction of the probability of clinical pregnancy using blastocyst images secondary to single embryo transfer than the conventional Gardner scoring assessments. FiTTE could also provide explanation of AI prediction using colored blastocyst images.
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Affiliation(s)
| | | | | | | | | | - Junko Otsuki
- Hanabusa Women’s Clinic Kobe Hyogo Japan
- Assisted Reproductive Technology Center Okayama University Okayama Japan
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7
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Nagaya M, Ukita N. Embryo Grading With Unreliable Labels Due to Chromosome Abnormalities by Regularized PU Learning With Ranking. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:320-331. [PMID: 34748484 DOI: 10.1109/tmi.2021.3126169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
We propose a method for human embryo grading with its images. This grading has been achieved by positive-negative classification (i.e., live birth or non-live birth). However, negative (non-live birth) labels collected in clinical practice are unreliable because the visual features of negative images are equal to those of positive (live birth) images if these non-live birth embryos have chromosome abnormalities. For alleviating an adverse effect of these unreliable labels, our method employs Positive-Unlabeled (PU) learning so that live birth and non-live birth are labeled as positive and unlabeled, respectively, where unlabeled samples contain both positive and negative samples. In our method, this PU learning on a deep CNN is improved by a learning-to-rank scheme. While the original learning-to-rank scheme is designed for positive-negative learning, it is extended to PU learning. Furthermore, overfitting in this PU learning is alleviated by regularization with mutual information. Experimental results with 643 time-lapse image sequences demonstrate the effectiveness of our framework in terms of the recognition accuracy and the interpretability. In quantitative comparison, the full version of our proposed method outperforms positive-negative classification in recall and F-measure by a wide margin (0.22 vs. 0.69 in recall and 0.27 vs. 0.42 in F-measure). In qualitative evaluation, visual attentions estimated by our method are interpretable in comparison with morphological assessments in clinical practice.
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8
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Bormann CL, Curchoe CL. AIM in Medical Disorders in Pregnancy. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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9
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Benchaib M, Labrune E, Giscard d'Estaing S, Salle B, Lornage J. Shallow artificial networks with morphokinetic time‐lapse parameters coupled to
ART
data allow to predict live birth. Reprod Med Biol 2022; 21:e12486. [PMID: 36310657 PMCID: PMC9601773 DOI: 10.1002/rmb2.12486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 08/10/2022] [Accepted: 09/08/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose The purpose of this work was to construct shallow neural networks (SNN) using time‐lapse technology (TLT) from morphokinetic parameters coupled to assisted reproductive technology (ART) parameters in order to assist the choice of embryo(s) to be transferred with the highest probability of achieving a live birth (LB). Methods A retrospective observational single‐center study was performed, 654 cycles were included. Three SNN: multilayers perceptron (MLP), simple recurrent neuronal network (simple RNN) and long short term memory RNN (LSTM‐RNN) were trained with K‐fold cross‐validation to avoid sampling bias. The predictive power of SNNs was measured using performance scores as AUC (area under curve), accuracy, precision, Recall and F1 score. Results In the training data group, MLP and simple RNN provide the best performance scores; however, all AUCs were above 0.8. In the validating data group, all networks were equivalent with no performance scores difference and all AUC values were above 0.8. Conclusion Coupling morphokinetic parameters with ART parameters allows to SNNs to predict the probability of LB, and all SNNs seems to be efficient according to the performance scores. An automatic time recognition system coupled to one of these SNNs could allow a complete automation to choose the blastocyst(s) to be transferred.
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Affiliation(s)
- Mehdi Benchaib
- Hospices Civil de Lyon, HFME, Médecine de la Reproduction & Préservation de la Fertilité Féminine Bron cedex France
- UMR CNRS 5558 LBBE Villeurbanne Cedex France
- Université Lyon I, Faculté de Médecine Lyon Est Lyon France
| | - Elsa Labrune
- Hospices Civil de Lyon, HFME, Médecine de la Reproduction & Préservation de la Fertilité Féminine Bron cedex France
- Université Lyon I, Faculté de Médecine Lyon Est Lyon France
- Inserm U1208 Bron cedex France
| | - Sandrine Giscard d'Estaing
- Hospices Civil de Lyon, HFME, Médecine de la Reproduction & Préservation de la Fertilité Féminine Bron cedex France
- Inserm U1208 Bron cedex France
- Université Lyon I, Faculté de Médecine Lyon Sud Oullins cedex France
| | - Bruno Salle
- Hospices Civil de Lyon, HFME, Médecine de la Reproduction & Préservation de la Fertilité Féminine Bron cedex France
- Inserm U1208 Bron cedex France
- Université Lyon I, Faculté de Médecine Lyon Sud Oullins cedex France
| | - Jacqueline Lornage
- Hospices Civil de Lyon, HFME, Médecine de la Reproduction & Préservation de la Fertilité Féminine Bron cedex France
- Inserm U1208 Bron cedex France
- Université Lyon I, Faculté de Médecine Lyon Sud Oullins cedex France
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10
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Dimitriadis I, Zaninovic N, Badiola AC, Bormann CL. Artificial intelligence in the embryology laboratory: a review. Reprod Biomed Online 2021; 44:435-448. [PMID: 35027326 DOI: 10.1016/j.rbmo.2021.11.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 10/07/2021] [Accepted: 11/04/2021] [Indexed: 02/03/2023]
Abstract
The goal of an IVF cycle is a healthy live-born baby. Despite the many advances in the field of assisted reproductive technologies, accurately predicting the outcome of an IVF cycle has yet to be achieved. One reason for this is the method of selecting an embryo for transfer. Morphological assessment of embryos is the traditional method of evaluating embryo quality and selecting which embryo to transfer. However, this subjective method of assessing embryos leads to inter- and intra-observer variability, resulting in less than optimal IVF success rates. To overcome this, it is common practice to transfer more than one embryo, potentially resulting in high-risk multiple pregnancies. Although time-lapse incubators and preimplantation genetic testing for aneuploidy have been introduced to help increase the chances of live birth, the outcomes remain less than ideal. Utilization of artificial intelligence (AI) has become increasingly popular in the medical field and is increasingly being leveraged in the embryology laboratory to help improve IVF outcomes. Many studies have been published investigating the use of AI as an unbiased, automated approach to embryo assessment. This review summarizes recent AI advancements in the embryology laboratory.
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Affiliation(s)
- Irene Dimitriadis
- Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston MA, USA
| | - Nikica Zaninovic
- The Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York NY, USA
| | - Alejandro Chavez Badiola
- New Hope Fertility Center, Av. Prado Norte 135, Lomas de Chapultepec, Mexico City, Mexico; IVF 2.0 LTD, 1 Liverpool Rd, Maghull, Merseyside, UK; School of Biosciences, University of Kent Kent, UK
| | - Charles L Bormann
- Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston MA, USA.
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Nguyen DP, Pham QT, Tran TL, Vuong LN, Ho TM. Blastocyst Prediction of Day-3 Cleavage-Stage Embryos Using Machine Learning. FERTILITY & REPRODUCTION 2021. [DOI: 10.1142/s266131822150016x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Background: Embryo selection plays an important role in the success of in vitro fertilization (IVF). However, morphological embryo assessment has a number of limitations, including the time required, lack of accuracy, and inconsistency. This study determined whether a machine learning-based model could predict blastocyst formation using day-3 embryo images. Methods: Day-3 embryo images from IVF/intracytoplasmic sperm injection (ICSI) cycles performed at My Duc Phu Nhuan Hospital between August 2018 and June 2019 were retrospectively analyzed to inform model development. Day-3 embryo images derived from two-pronuclear (2PN) zygotes with known blastocyst formation data were extracted from the CCM-iBIS time-lapse incubator (Astec, Japan) at 67 hours post ICSI, and labeled as blastocyst/non-blastocyst based on results at 116 hours post ICSI. Images were used as the input dataset to train (85%) and validate (15%) the convolutional neural network (CNN) model, then model accuracy was determined using the training and validation dataset. The performance of 13 experienced embryologists for predicting blastocyst formation based on 100 day-3 embryo images was also evaluated. Results: A total of 1,135 images were allocated into training ([Formula: see text] 967) and validation ([Formula: see text] 168) sets, with an even distribution for blastocyst formation outcome. The accuracy of the final model for blastocyst formation was 97.72% in the training dataset and 76.19% in the validation dataset. The final model predicted blastocyst formation from day-3 embryo images in the validation dataset with an area under the curve of 0.75 (95% confidence interval [CI] 0.69–0.81). Embryologists predicted blastocyst formation with the accuracy of 70.07% (95% CI 68.12%–72.03%), sensitivity of 87.04% (95% CI 82.56%–91.52%), and specificity of 30.93% (95% CI 29.35%–32.51%). Conclusions: The CNN-based machine learning model using day-3 embryo images predicted blastocyst formation more accurately than experienced embryologists. The CNN-based model is a potential tool to predict additional IVF outcomes.
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Affiliation(s)
- Dung P. Nguyen
- IVFMD PN, My Duc Phu Nhuan Hospital, Ho Chi Minh City, Vietnam
- HOPE Research Center, My Duc Hospital, Ho Chi Minh City, Vietnam
| | - Quan T. Pham
- HOPE Research Center, My Duc Hospital, Ho Chi Minh City, Vietnam
| | - Thanh L. Tran
- IVFMD PN, My Duc Phu Nhuan Hospital, Ho Chi Minh City, Vietnam
- HOPE Research Center, My Duc Hospital, Ho Chi Minh City, Vietnam
| | - Lan N. Vuong
- HOPE Research Center, My Duc Hospital, Ho Chi Minh City, Vietnam
- IVFMD, My Duc Hospital, Ho Chi Minh City, Vietnam
- Department of Obstetrics and Gynecology, University of Medicine and Pharmacy at Ho Chi Minh City, Vietnam
| | - Tuong M. Ho
- HOPE Research Center, My Duc Hospital, Ho Chi Minh City, Vietnam
- IVFMD, My Duc Hospital, Ho Chi Minh City, Vietnam
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12
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Kragh MF, Karstoft H. Embryo selection with artificial intelligence: how to evaluate and compare methods? J Assist Reprod Genet 2021; 38:1675-1689. [PMID: 34173914 PMCID: PMC8324599 DOI: 10.1007/s10815-021-02254-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 06/02/2021] [Indexed: 12/19/2022] Open
Abstract
Embryo selection within in vitro fertilization (IVF) is the process of evaluating qualities of fertilized oocytes (embryos) and selecting the best embryo(s) available within a patient cohort for subsequent transfer or cryopreservation. In recent years, artificial intelligence (AI) has been used extensively to improve and automate the embryo ranking and selection procedure by extracting relevant information from embryo microscopy images. The AI models are evaluated based on their ability to identify the embryo(s) with the highest chance(s) of achieving a successful pregnancy. Whether such evaluations should be based on ranking performance or pregnancy prediction, however, seems to divide studies. As such, a variety of performance metrics are reported, and comparisons between studies are often made on different outcomes and data foundations. Moreover, superiority of AI methods over manual human evaluation is often claimed based on retrospective data, without any mentions of potential bias. In this paper, we provide a technical view on some of the major topics that divide how current AI models are trained, evaluated and compared. We explain and discuss the most common evaluation metrics and relate them to the two separate evaluation objectives, ranking and prediction. We also discuss when and how to compare AI models across studies and explain in detail how a selection bias is inevitable when comparing AI models against current embryo selection practice in retrospective cohort studies.
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Affiliation(s)
- Mikkel Fly Kragh
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, Denmark.
- Vitrolife A/S, Viby J, Denmark.
| | - Henrik Karstoft
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, Denmark
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13
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Miyagi Y, Hata T, Bouno S, Koyanagi A, Miyake T. Recognition of facial expression of fetuses by artificial intelligence (AI). J Perinat Med 2021; 49:596-603. [PMID: 33548168 DOI: 10.1515/jpm-2020-0537] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 12/27/2020] [Indexed: 01/19/2023]
Abstract
OBJECTIVES The development of the artificial intelligence (AI) classifier to recognize fetal facial expressions that are considered as being related to the brain development of fetuses as a retrospective, non-interventional pilot study. METHODS Images of fetal faces with sonography obtained from outpatient pregnant women with a singleton fetus were enrolled in routine conventional practice from 19 to 38 weeks of gestation from January 1, 2020, to September 30, 2020, with completely de-identified data. The images were classified into seven categories, such as eye blinking, mouthing, face without any expression, scowling, smiling, tongue expulsion, and yawning. The category in which the number of fetuses was less than 10 was eliminated before preparation. Next, we created a deep learning AI classifier with the data. Statistical values such as accuracy for the test dataset and the AI confidence score profiles for each category per image for all data were obtained. RESULTS The number of fetuses/images in the rated categories were 14/147, 23/302, 33/320, 8/55, and 10/72 for eye blinking, mouthing, face without any expression, scowling, and yawning, respectively. The accuracy of the AI fetal facial expression for the entire test data set was 0.985. The accuracy/sensitivity/specificity values were 0.996/0.993/1.000, 0.992/0.986/1.000, 0.985/1.000/0.979, 0.996/0.888/1.000, and 1.000/1.000/1.000 for the eye blinking, mouthing, face without any expression, scowling categories, and yawning, respectively. CONCLUSIONS The AI classifier has the potential to objectively classify fetal facial expressions. AI can advance fetal brain development research using ultrasound.
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Affiliation(s)
- Yasunari Miyagi
- Department of Gynecology, Miyake Ofuku Clinic, Okayama, Japan.,Medical Data Labo, Okayama, Japan.,Department of Gynecologic Oncology, Saitama Medical University International Medical Center, Hidaka, Japan
| | - Toshiyuki Hata
- Department of Obstetrics and Gynecology, Miyake Clinic, Okayama, Japan.,Department of Perinatology and Gynecology, Kagawa University Graduate School of Medicine, Kagawa, Japan
| | - Saori Bouno
- Department of Obstetrics and Gynecology, Miyake Clinic, Okayama, Japan
| | - Aya Koyanagi
- Department of Obstetrics and Gynecology, Miyake Clinic, Okayama, Japan
| | - Takahito Miyake
- Department of Gynecology, Miyake Ofuku Clinic, Okayama, Japan.,Department of Obstetrics and Gynecology, Miyake Clinic, Okayama, Japan
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14
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Curchoe CL. The paper chase and the big data arms race. J Assist Reprod Genet 2021; 38:1613-1615. [PMID: 33715133 DOI: 10.1007/s10815-021-02122-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/03/2021] [Indexed: 12/15/2022] Open
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15
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Bormann CL, Curchoe CL. AIM in Medical Disorders in Pregnancy. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_160-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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16
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Coticchio G, Fiorentino G, Nicora G, Sciajno R, Cavalera F, Bellazzi R, Garagna S, Borini A, Zuccotti M. Cytoplasmic movements of the early human embryo: imaging and artificial intelligence to predict blastocyst development. Reprod Biomed Online 2020; 42:521-528. [PMID: 33558172 DOI: 10.1016/j.rbmo.2020.12.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 10/27/2020] [Accepted: 12/18/2020] [Indexed: 12/23/2022]
Abstract
RESEARCH QUESTION Can artificial intelligence and advanced image analysis extract and harness novel information derived from cytoplasmic movements of the early human embryo to predict development to blastocyst? DESIGN In a proof-of-principle study, 230 human preimplantation embryos were retrospectively assessed using an artificial neural network. After intracytoplasmic sperm injection, embryos underwent time-lapse monitoring for 44 h. For comparison, standard embryo assessment of each embryo by a single embryologist was carried out to predict development to blastocyst stage based on a single picture frame taken at 42 h of development. In the experimental approach, in embryos that developed to blastocyst or destined to arrest, cytoplasm movement velocity was recorded by time-lapse monitoring during the first 44 h of culture and analysed with a Particle Image Velocimetry algorithm to extract quantitative information. Three main artificial intelligence approaches, the k-Nearest Neighbour, the Long-Short Term Memory Neural Network and the hybrid ensemble classifier were used to classify the embryos. RESULTS Blind operator assessment classified each embryo in terms of ability to develop to blastocyst, with 75.4% accuracy, 76.5% sensitivity, 74.3% specificity, 74.3% precision and 75.4% F1 score. Integration of results from artificial intelligence models with the blind operator classification, resulted in 82.6% accuracy, 79.4% sensitivity, 85.7% specificity, 84.4% precision and 81.8% F1 score. CONCLUSIONS The present study suggests the possibility of predicting human blastocyst development at early cleavage stages by detection of cytoplasm movement velocity and artificial intelligence analysis. This indicates the importance of the dynamics of the cytoplasm as a novel and valuable source of data to assess embryo viability.
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Affiliation(s)
- Giovanni Coticchio
- 9.baby Family and Fertility Center, Via Dante, 15, Bologna 40125, Italy.
| | - Giulia Fiorentino
- Department of Biology and Biotechnology 'Lazzaro Spallanzani', University of Pavia, Via Ferrata, 9 27100, Italy; Centre for Health Technology, University of Pavia, Pavia, Italy
| | - Giovanna Nicora
- Centre for Health Technology, University of Pavia, Pavia, Italy; Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Raffaella Sciajno
- 9.baby Family and Fertility Center, Via Dante, 15, Bologna 40125, Italy
| | - Federica Cavalera
- Department of Biology and Biotechnology 'Lazzaro Spallanzani', University of Pavia, Via Ferrata, 9 27100, Italy
| | - Riccardo Bellazzi
- Centre for Health Technology, University of Pavia, Pavia, Italy; Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Silvia Garagna
- Department of Biology and Biotechnology 'Lazzaro Spallanzani', University of Pavia, Via Ferrata, 9 27100, Italy; Centre for Health Technology, University of Pavia, Pavia, Italy
| | - Andrea Borini
- 9.baby Family and Fertility Center, Via Dante, 15, Bologna 40125, Italy
| | - Maurizio Zuccotti
- Department of Biology and Biotechnology 'Lazzaro Spallanzani', University of Pavia, Via Ferrata, 9 27100, Italy; Centre for Health Technology, University of Pavia, Pavia, Italy.
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17
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Chéles DS, Molin EAD, Rocha JC, Nogueira MFG. Mining of variables from embryo morphokinetics, blastocyst's morphology and patient parameters: an approach to predict the live birth in the assisted reproduction service. JBRA Assist Reprod 2020; 24:470-479. [PMID: 32293823 PMCID: PMC7558892 DOI: 10.5935/1518-0557.20200014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 03/17/2020] [Indexed: 11/20/2022] Open
Abstract
Based on growing demand for assisted reproduction technology, improved predictive models are required to optimize in vitro fertilization/intracytoplasmatic sperm injection strategies, prioritizing single embryo transfer. There are still several obstacles to overcome for the purpose of improving assisted reproductive success, such as intra- and inter-observer subjectivity in embryonic selection, high occurrence of multiple pregnancies, maternal and neonatal complications. Here, we compare studies that used several variables that impact the success of assisted reproduction, such as blastocyst morphology and morphokinetic aspects of embryo development as well as characteristics of the patients submitted to assisted reproduction, in order to predict embryo quality, implantation or live birth. Thereby, we emphasize the proposal of an artificial intelligence-based platform for a more objective method to predict live birth.
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Affiliation(s)
- Dóris Spinosa Chéles
- Laboratório de Matemática Aplicada, Department of Biological Sciences, School of Languages and Sciences, Campus Assis, São Paulo State University (UNESP), Assis, SP, Brazil
- Laboratório de Micromanipulação Embrionária, Department of Biological Sciences, School of Sciences and Languages, Campus Assis, São Paulo State University (UNESP), Assis, SP, Brazil
| | - Eloiza Adriane Dal Molin
- Laboratório de Matemática Aplicada, Department of Biological Sciences, School of Languages and Sciences, Campus Assis, São Paulo State University (UNESP), Assis, SP, Brazil
| | - José Celso Rocha
- Laboratório de Matemática Aplicada, Department of Biological Sciences, School of Languages and Sciences, Campus Assis, São Paulo State University (UNESP), Assis, SP, Brazil
| | - Marcelo Fábio Gouveia Nogueira
- Laboratório de Micromanipulação Embrionária, Department of Biological Sciences, School of Sciences and Languages, Campus Assis, São Paulo State University (UNESP), Assis, SP, Brazil
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18
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Miyagi Y, Habara T, Hirata R, Hayashi N. Predicting a live birth by artificial intelligence incorporating both the blastocyst image and conventional embryo evaluation parameters. Artif Intell Med Imaging 2020. [DOI: 10.35711/wjbc.v1.i3.87] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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19
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Miyagi Y, Habara T, Hirata R, Hayashi N. Predicting a live birth by artificial intelligence incorporating both the blastocyst image and conventional embryo evaluation parameters. Artif Intell Med Imaging 2020; 1:94-107. [DOI: 10.35711/aimi.v1.i3.94] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 09/15/2020] [Accepted: 09/19/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The achievement of live birth is the goal of assisted reproductive technology in reproductive medicine. When the selected blastocyst is transferred to the uterus, the degree of implantation of the blastocyst is evaluated by microscopic inspection, and the result is only about 30%-40%, and the method of predicting live birth from the blastocyst image is unknown. Live births correlate with several clinical conventional embryo evaluation parameters (CEE), such as maternal age. Therefore, it is necessary to develop artificial intelligence (AI) that combines blastocyst images and CEE to predict live births.
AIM To develop an AI classifier for blastocyst images and CEE to predict the probability of achieving a live birth.
METHODS A total of 5691 images of blastocysts on the fifth day after oocyte retrieval obtained from consecutive patients from January 2009 to April 2017 with fully deidentified data were retrospectively enrolled with explanations to patients and a website containing additional information with an opt-out option. We have developed a system in which the original architecture of the deep learning neural network is used to predict the probability of live birth from a blastocyst image and CEE.
RESULTS The live birth rate was 0.387 (= 1587/4104 cases). The number of independent clinical information for predicting live birth is 10, which significantly avoids multicollinearity. A single AI classifier is composed of ten layers of convolutional neural networks, and each elementwise layer of ten factors is developed and obtained with 42792 as the number of training data points and 0.001 as the L2 regularization value. The accuracy, sensitivity, specificity, negative predictive value, positive predictive value, Youden J index, and area under the curve values for predicting live birth are 0.743, 0.638, 0.789, 0.831, 0.573, 0.427, and 0.740, respectively. The optimal cut-off point of the receiver operator characteristic curve is 0.207.
CONCLUSION AI classifiers have the potential of predicting live births that humans cannot predict. Artificial intelligence may make progress in assisted reproductive technology.
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Affiliation(s)
- Yasunari Miyagi
- Department of Artificial Intelligence, Medical Data Labo, Okayama 703-8267, Japan
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, Hidaka 350-1298, Saitama, Japan
| | - Toshihiro Habara
- Department of Reproduction, Okayama Couples' Clinic, Okayama 701-1152, Japan
| | - Rei Hirata
- Department of Reproduction, Okayama Couples' Clinic, Okayama 701-1152, Japan
| | - Nobuyoshi Hayashi
- Department of Reproduction, Okayama Couples' Clinic, Okayama 701-1152, Japan
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20
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Fernandez EI, Ferreira AS, Cecílio MHM, Chéles DS, de Souza RCM, Nogueira MFG, Rocha JC. Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data. J Assist Reprod Genet 2020; 37:2359-2376. [PMID: 32654105 DOI: 10.1007/s10815-020-01881-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 07/03/2020] [Indexed: 12/18/2022] Open
Abstract
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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Affiliation(s)
- Eleonora Inácio Fernandez
- Laboratory of Applied Mathematics, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil
| | - André Satoshi Ferreira
- Laboratory of Applied Mathematics, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil
| | - Matheus Henrique Miquelão Cecílio
- Laboratory of Applied Mathematics, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil
| | - Dóris Spinosa Chéles
- Laboratory of Applied Mathematics, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil.,Laboratory of Embryonic Micromanipulation, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil
| | - Rebeca Colauto Milanezi de Souza
- Laboratory of Applied Mathematics, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil
| | - Marcelo Fábio Gouveia Nogueira
- Laboratory of Embryonic Micromanipulation, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil
| | - José Celso Rocha
- Laboratory of Applied Mathematics, Department of Biological Sciences, São Paulo State University (UNESP), Campus Assis, Av. Dom Antônio, São Paulo, 2100, Brazil. .,Universidade Estadual Paulista Julio de Mesquita Filho, Assis, São Paulo, Brazil.
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21
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Miyagi Y, Takehara K, Nagayasu Y, Miyake T. Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images combined with HPV types. Oncol Lett 2020; 19:1602-1610. [PMID: 31966086 PMCID: PMC6956417 DOI: 10.3892/ol.2019.11214] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 10/10/2019] [Indexed: 01/16/2023] Open
Abstract
The aim of the present study was to explore the feasibility of using deep learning, such as artificial intelligence (AI), to classify cervical squamous epithelial lesions (SILs) from colposcopy images combined with human papilloma virus (HPV) types. Among 330 patients who underwent colposcopy and biopsy performed by gynecological oncologists, a total of 253 patients with confirmed HPV typing tests were enrolled in the present study. Of these patients, 210 were diagnosed with high-grade SIL (HSIL) and 43 were diagnosed with low-grade SIL (LSIL). An original AI classifier with a convolutional neural network catenating with an HPV tensor was developed and trained. The accuracy of the AI classifier and gynecological oncologists was 0.941 and 0.843, respectively. The AI classifier performed better compared with the oncologists, although not significantly. The sensitivity, specificity, positive predictive value, negative predictive value, Youden's J index and the area under the receiver-operating characteristic curve ± standard error for AI colposcopy combined with HPV types and pathological results were 0.956 (43/45), 0.833 (5/6), 0.977 (43/44), 0.714 (5/7), 0.789 and 0.963±0.026, respectively. Although further study is required, the clinical use of AI for the classification of HSIL/LSIL by both colposcopy and HPV type may be feasible.
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Affiliation(s)
- Yasunari Miyagi
- Medical Data Labo, Okayama, Okayama 703-8267, Japan
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, Hidaka, Saitama 350-1298, Japan
- Miyake Ofuku Clinic, Okayama, Okayama 701-0204, Japan
| | - Kazuhiro Takehara
- Department of Gynecologic Oncology, National Hospital Organization Shikoku Cancer Center, Matsuyama, Ehime 791-0208, Japan
| | - Yoko Nagayasu
- Medical Data Labo, Okayama, Okayama 703-8267, Japan
- Department of Obstetrics and Gynecology, Osaka Medical College, Takatsuki, Osaka 569-0801, Japan
| | - Takahito Miyake
- Department of Obstetrics and Gynecology, Miyake Clinic, Okayama, Okayama 701-0204, Japan
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22
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Miyagi Y, Tada K, Yasuhi I, Maekawa Y, Okura N, Kawakami K, Yamaguchi K, Ogawa M, Kodama T, Nomiyama M, Mizunoe T, Miyake T. New method for determining fibrinogen and FDP threshold criteria by artificial intelligence in cases of massive hemorrhage during delivery. J Obstet Gynaecol Res 2019; 46:256-265. [PMID: 31762151 DOI: 10.1111/jog.14166] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 11/10/2019] [Indexed: 02/06/2023]
Abstract
AIM To investigate the feasibility of a novel method using artificial intelligence (AI), in which the fibrinogen criterion was determined by the quantitative relation between the distributions of fibrin/fibrinogen degradation products (FDPs) and fibrinogen. METHODS A dataset of 154 deliveries comprising more than 2000 g of blood lost due to hemorrhage, excluding disseminated intravascular coagulation (DIC), among patients from eight national perinatal centers in Japan from 2011 to 2015 were obtained. The fibrinogen threshold criterion was identified by using the function that best fit the distributions of FDP as determined by AI. FDP production was described by differential equations using a dataset containing fibrinogen levels less than the fibrinogen criterion and solved numerically. RESULTS A fibrinogen level of 237 mg/dL as the threshold criterion was obtained. The FDP threshold criteria were 2.0 and 8.5 mg/dL for no coagulopathy and a failed coagulation system, respectively. CONCLUSION The fibrinogen threshold criterion for patients with massive hemorrhage excluding DIC at delivery were obtained by selecting the functions that best fit the distributions of FDP data by using AI.
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Affiliation(s)
- Yasunari Miyagi
- Medical Data Labo, Okayama, Japan.,Department of Gynecologic Oncology, Saitama Medical University International Medical Center, Saitama, Japan.,Miyake Ofuku Clinic, Okayama, Japan
| | - Katsuhiko Tada
- Department of Obstetrics and Gynecology, National Hospital Organization Okayama Medical Center, Okayama, Japan
| | - Ichiro Yasuhi
- Department of Obstetrics and Gynecology, National Hospital Organization Nagasaki Medical Center, Omura, Japan
| | - Yuka Maekawa
- Department of Obstetrics and Gynecology, National Hospital Organization Mie Chuo Medical Center, Tsu, Japan
| | - Naofumi Okura
- Department of Obstetrics and Gynecology, National Hospital Organization Kokura Medical Center, Kitakyushu, Japan
| | - Kosuke Kawakami
- Department of Obstetrics and Gynecology, National Hospital Organization Kokura Medical Center, Kitakyushu, Japan
| | - Ken Yamaguchi
- Department of Gynecology and Obstetrics, Kyoto University Graduate School of Medicine, Kyoto, Japan.,Department of Obstetrics and Gynecology, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Masanobu Ogawa
- Research Center for Environment and Developmental Medical Sciences, Kyusyu University, Fukuoka, Japan.,Department of Obstetrics and Gynecology/Clinical Research Institute, National Hospital Organization Kyusyu Medical Center, Fukuoka, Japan
| | - Takashi Kodama
- Department of Obstetrics and Gynecology, National Hospital Organization Higashihiroshima Medical Center, Higashihiroshima, Japan
| | - Makoto Nomiyama
- Department of Obstetrics and Gynecology, National Hospital Organization Saga National Hospital, Saga, Japan
| | - Tomoya Mizunoe
- Department of Obstetrics and Gynecology, National Hospital Organization Kure Medical Center, Kure, Japan
| | - Takahito Miyake
- Department of Obstetrics and Gynecology, Miyake Clinic, Okayama, Japan
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Miyagi Y, Takehara K, Miyake T. Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images. Mol Clin Oncol 2019; 11:583-589. [PMID: 31692958 PMCID: PMC6826263 DOI: 10.3892/mco.2019.1932] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 09/09/2019] [Indexed: 01/12/2023] Open
Abstract
The aim of the present study was to explore the feasibility of using deep learning as artificial intelligence (AI) to classify cervical squamous epithelial lesions (SIL) from colposcopy images. A total of 330 patients who underwent colposcopy and biopsy by gynecologic oncologists were enrolled in the current study. A total of 97 patients received a pathological diagnosis of low-grade SIL (LSIL) and 213 of high-grade SIL (HSIL). An original AI-classifier with 11 layers of the convolutional neural network was developed and trained. The accuracy, sensitivity, specificity and Youden's J index of the AI-classifier and oncologists for diagnosing HSIL were 0.823 and 0.797, 0.800 and 0.831, 0.882 and 0.773, and 0.682 and 0.604, respectively. The area under the receiver-operating characteristic curve was 0.826±0.052 (mean ± standard error), and the 95% confidence interval 0.721–0.928. The optimal cut-off point was 0.692. Cohen's Kappa coefficient for AI and colposcopy was 0.437 (P<0.0005). The AI-classifier performed better than oncologists, although not significantly. Although further study is required, the clinical use of AI for the classification of HSIL/LSIL from by colposcopy may be feasible.
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Affiliation(s)
- Yasunari Miyagi
- Medical Data Labo, Okayama 703-8267, Japan.,Department of Gynecologic Oncology, Saitama Medical University International Medical Center, Hidaka, Saitama 350-1298, Japan.,Department of Gynecology, Miyake Ofuku Clinic, Okayama 701-0204, Japan
| | - Kazuhiro Takehara
- Department of Gynecologic Oncology, National Hospital Organization, Shikoku Cancer Center, Matsuyama, Ehime 791-0208, Japan
| | - Takahito Miyake
- Department of Obstetrics and Gynecology, Miyake Clinic, Okayama 701-0204, Japan
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Miyagi Y, Habara T, Hirata R, Hayashi N. Feasibility of predicting live birth by combining conventional embryo evaluation with artificial intelligence applied to a blastocyst image in patients classified by age. Reprod Med Biol 2019; 18:344-356. [PMID: 31607794 PMCID: PMC6780028 DOI: 10.1002/rmb2.12284] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 05/21/2019] [Accepted: 06/02/2019] [Indexed: 12/12/2022] Open
Abstract
PURPOSE To identify the multivariate logistic regression in a combination (combination method) involving artificial intelligence (AI) classifiers in images of blastocysts along with a conventional embryo evaluation (CEE) to predict the probability of accomplishing a live birth in patients classified by maternal age. METHODS Retrospectively, a total of 5691 blastocysts were enrolled. Images captured 115 hours or 139 hours if not yet sufficiently large after insemination were classified according to age as follows: <35, 35-37, 38-39, 40-41, and ≥42 years old. The classifiers for each category were created by using convolutional neural networks associated with deep learning. Next, the feasibility of a method combining AI with multivariate logistic model functions by CEE was investigated. RESULTS The values of the area under the curve (AUC) and the accuracies to predict live birth achieved by the CEE/AI/combination methods were 0.651/0.634/0.655, 0.697/0.688/0.723, 0.771/0.728/0.791, 0.788/0.743/0.806 and 0.820/0.837/0.888, and 0.631/0.647/0.616, 0.687/0.675/0.671, 0.725/0.697/0.732, 0.714/0.776/0.801, and 0.910/0.866/0.784 for age categories of <35, 35-37, 38-39, 40-41, and ≥42 years old, respectively. CONCLUSIONS Though there were mostly no significant differences regarding the AUC and the sensitivity plus specificity in all age categories, the combination method seemed to be the best.
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Affiliation(s)
- Yasunari Miyagi
- Medical Data LaboOkayama CityJapan
- Department of Gynecologic OncologySaitama Medical University International Medical CenterHidaka CityJapan
| | | | - Rei Hirata
- Okayama Couple’s ClinicOkayama CityJapan
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