- S -

Artificial intelligence (AI) has been gaining support in the field of in vitro fertilization (IVF). Despite the promising existing data, AI cannot yet claim gold-standard status, which serves as the rationale for this study. This systematic review and data synthesis aims to evaluate and report on the predictive capabilities of AI-based prediction models regarding IVF outcome. The study has been registered in PROSPERO (CRD42021242097). Following a systematic search of the literature in Pubmed/Medline, Embase, and Cochrane Central Library, 18 studies were identified as eligible for inclusion. Regarding live-birth, the Area Under the Curve (AUC) of the Summary Receiver Operating Characteristics (SROC) was 0.905, while the partial AUC (pAUC) was 0.755. The Observed:Expected ratio was 1.12 (95%CI:0.26–2.37; 95%PI:0.02–6.54). Regarding clinical pregnancy with fetal heartbeat, the AUC of the SROC was 0.722, while the pAUC was 0.774. The O:E ratio was 0.77 (95%CI:0.54–1.05; 95%PI:0.21–1.62). According to this data synthesis, the majority of the AI-based prediction models are successful in accurately predicting the IVF outcome regarding live birth, clinical pregnancy, clinical pregnancy with fetal heartbeat, and ploidy status. This review attempted to compare between AI and human prediction capabilities, and although studies do not allow for a meta-analysis, this systematic review indicates that the AI-based prediction models perform rather similarly to the embryologists’ evaluations. While AI models appear marginally more effective, they still have some way to go before they can claim to significantly surpass the clinical embryologists’ predictive competence.

[1]  Zev Rosenwaks,et al.  Artificial intelligence in reproductive medicine: a fleeting concept or the wave of the future? , 2020, Fertility and sterility.

[2]  N. Zaninovic,et al.  Artificial intelligence in human in vitro fertilization and embryology. , 2020, Fertility and sterility.

[3]  M. Meseguer,et al.  An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: a preliminary study. , 2020, Reproductive biomedicine online.

[4]  Sebastian Bohl,et al.  AI in the treatment of fertility: key considerations , 2020, Journal of Assisted Reproduction and Genetics.

[5]  Manoj Kumar Kanakasabapathy,et al.  Performance of a deep learning based neural network in the selection of human blastocysts for implantation , 2020, eLife.

[6]  V. Naranjo,et al.  Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential. , 2020, Fertility and sterility.

[7]  S. Harbottle,et al.  EXTERNAL VALIDATION OF AN AI SYSTEM FOR BLASTOCYST IMPLANTATION PREDICTION , 2020 .

[8]  André Satoshi Ferreira,et al.  FIRST APPLICATION OF ARTIFICIAL NEURONAL NETWORKS FOR HUMAN LIVE BIRTH PREDICTION ON GERI TIME-LAPSE MONITORING SYSTEM BLASTOCYST IMAGES , 2020 .

[9]  Himanshu Arora,et al.  DEVELOPMENT OF AN ARTIFICIAL INTELLIGENCE-BASED ASSESSMENT MODEL FOR PREDICTION OF PREGNANCY SUCCESS USING STATIC IMAGES CAPTURED BY OPTICAL LIGHT MICROSCOPY DURING IVF , 2020, Fertility and Sterility.

[10]  J. Wilkinson,et al.  The prevalence, promotion and pricing of three IVF add-ons on fertility clinic websites , 2020, Reproductive biomedicine online.

[11]  José Celso Rocha,et al.  Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data , 2020, Journal of Assisted Reproduction and Genetics.

[12]  A. Chavez-Badiola,et al.  Embryo Ranking Intelligent Classification Algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation. , 2020, Reproductive biomedicine online.

[13]  Matan Gavish,et al.  Automated Evaluation of Human Embryo Blastulation and Implantation Potential using Deep‐Learning , 2020, Adv. Intell. Syst..

[14]  M. VerMilyea,et al.  Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF , 2020, Human Reproduction (Oxford, England).

[15]  Manoj Kumar Kanakasabapathy,et al.  Consistency and objectivity of automated embryo assessments using deep neural networks , 2020, Fertility and sterility.

[16]  J. P. García-Sandoval,et al.  Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning , 2020, Scientific Reports.

[17]  Alex M. Bronstein,et al.  Data-Driven Prediction of Embryo Implantation Probability Using IVF Time-lapse Imaging , 2020, ArXiv.

[18]  P. V. van Heerden,et al.  Ethical considerations about artificial intelligence for prognostication in intensive care , 2019, Intensive Care Medicine Experimental.

[19]  Henrik Karstoft,et al.  Automatic grading of human blastocysts from time-lapse imaging , 2019, Comput. Biol. Medicine.

[20]  José Celso Rocha,et al.  Is there any room to improve embryo selection? artificial intelligence technology applied for ive birth prediction on blastocysts , 2019, Fertility and Sterility.

[21]  A. Papalois,et al.  Artificial Intelligence in Obstetrics and Gynaecology: Is This the Way Forward? , 2019, In Vivo.

[22]  D. Tran,et al.  Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer , 2019, Human reproduction.

[23]  O. Alotaibi,et al.  Regional odontodysplasia: An analysis of 161 cases from 1953 to 2017 , 2019, The Saudi dental journal.

[24]  M. Abdel-kader,et al.  Oral rehabilitation of a case with regional odontodysplasia using a regenerative approach-A case report and a review of literature. , 2019, Special care in dentistry : official publication of the American Association of Hospital Dentists, the Academy of Dentistry for the Handicapped, and the American Society for Geriatric Dentistry.

[25]  Ian Huang,et al.  Using Deep Learning with Large Dataset of Microscope Images to Develop an Automated Embryo Grading System , 2019, Fertility & Reproduction.

[26]  Ehsan Kazemi,et al.  Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization , 2019, npj Digital Medicine.

[27]  Yasunari Miyagi,et al.  Feasibility of deep learning for predicting live birth from a blastocyst image in patients classified by age , 2019, Reproductive medicine and biology.

[28]  P. Nieminen,et al.  Familial oligodontia and regional odontodysplasia associated with a PAX9 initiation codon mutation , 2019, Clinical Oral Investigations.

[29]  C. Curchoe,et al.  Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018 , 2019, Journal of Assisted Reproduction and Genetics.

[30]  F. Seymen,et al.  Management of regional odontodysplasia: a 10-year-follow-up case report and literature review. , 2019, European oral research.

[31]  Alex John London,et al.  Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability. , 2019, The Hastings Center report.

[32]  Stephen Shaoyi Liao,et al.  Identification of Viable Embryos Using Deep Learning for Medical Image , 2018, ICBRA.

[33]  Reza Ghadimi.  Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017 , 2018, Lancet.

[34]  M. Koutsilieris,et al.  Are computational applications the “crystal ball” in the IVF laboratory? The evolution from mathematics to artificial intelligence , 2018, Journal of Assisted Reproduction and Genetics.

[35]  J. Castilla,et al.  Reliability and agreement on embryo assessment: 5 years of an external quality control programme. , 2017, Reproductive biomedicine online.

[36]  G. Guyatt,et al.  Discrimination and Calibration of Clinical Prediction Models: Users’ Guides to the Medical Literature , 2017, JAMA.

[37]  K. Ivani,et al.  Morphology of the blastocysts is the single most important factor affecting clinical pregnancy rates in IVF PGS cycles with single embryo transfers , 2017 .

[38]  Dianna L. Stone,et al.  The Advantages and Limitations of Using Meta-analysis in Human Resource Management Research , 2017 .

[39]  Christos A. Venetis,et al.  Inter-observer and intra-observer agreement between embryologists during selection of a single Day 5 embryo for transfer: a multicenter study , 2017, Human reproduction.

[40]  L. Hooft,et al.  A guide to systematic review and meta-analysis of prediction model performance , 2017, British Medical Journal.

[41]  Kwang Gi Kim,et al.  Book Review: Deep Learning , 2016, Healthcare Informatics Research.

[42]  Markus Montag,et al.  Development of a generally applicable morphokinetic algorithm capable of predicting the implantation potential of embryos transferred on Day 3 , 2016, Human reproduction.

[43]  C. Bergh,et al.  Conventional morphology performs better than morphokinetics for prediction of live birth after day 2 transfer. , 2016, Reproductive biomedicine online.

[44]  Lida Pishbin,et al.  Clinical, Radiographic, and Histologic Evaluation of Regional Odontodysplasia: a Case Report with 5-year Follow-up , 2016, Journal of dentistry.

[45]  Ronald M. Summers,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.

[46]  Kavita Bala,et al.  Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[47]  K. Lundin,et al.  Quality control and standardization of embryo morphology scoring and viability markers. , 2015, Reproductive biomedicine online.

[48]  A. Mathew,et al.  Ghost teeth: Regional odontodysplasia of maxillary first molar associated with eruption disorders in a 10-year-old girl , 2015, Journal of pharmacy & bioallied sciences.

[49]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[50]  K. Hunter,et al.  Comparative case report of segmental odontomaxillary dysplasia and regional odontodysplasia , 2014 .

[51]  Ronit Machtinger,et al.  Morphological systems of human embryo assessment and clinical evidence. , 2013, Reproductive biomedicine online.

[52]  Mostafa Ibrahim Mostafa,et al.  Generalised versus Regional Odontodysplasia: Diagnosis, Transitional Management, and Long-Term Followup—A Report of 2 Cases , 2013, Case reports in dentistry.

[53]  F. Neukam,et al.  Regional odontodysplasia: orthodontic treatment and transplantation of premolars. , 2012, American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics.

[54]  María Cruz,et al.  Timing of cell division in human cleavage-stage embryos is linked with blastocyst formation and quality. , 2012, Reproductive biomedicine online.

[55]  O. Miloğlu,et al.  Generalized Familial Crown Resorptions in Unerupted Teeth , 2011, European journal of dentistry.

[56]  M. Alikhasi,et al.  Step-by-Step Oral Rehabilitation of a Generalized Odontodysplastic Patient With Implant-Supported Prostheses: A Clinical Report , 2010, Implant dentistry.

[57]  Robert Milewski,et al.  [Prognosis of the IVF ICSI/ET procedure efficiency with the use of artificial neural networks among patients of the Department of Reproduction and Gynecological Endocrinology]. , 2009, Ginekologia polska.

[58]  K. Storhaug,et al.  Unclassified syndrome involving dental enamel, dentine and lack of tooth eruption , 2009, European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry.

[59]  Ayse Bener,et al.  A frequency based encoding technique for transformation of categorical variables in mixed IVF dataset , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[60]  T. Fujiwara,et al.  Simultaneous occurrence of unusual odontodysplasia and oligodontia in the permanent dentition: report of a case. , 2008, International journal of paediatric dentistry.

[61]  R. Carlos,et al.  Regional odontodysplasia: morphological, ultrastructural, and immunohistochemical features of the affected teeth, connective tissue, and odontogenic remnants. , 2008, Journal of dentistry for children.

[62]  Á. Borges,et al.  Regional odontodysplasia: report of a case in the mandible crossing the midline. , 2008, The journal of contemporary dental practice.

[63]  S D Walter,et al.  The partial area under the summary ROC curve , 2005, Statistics in medicine.

[64]  P. Reichart,et al.  Regional odontodysplasia: a review of the literature and report of four cases , 2004, Clinical Oral Investigations.

[65]  S. Walter,et al.  Properties of the summary receiver operating characteristic (SROC) curve for diagnostic test data , 2002, Statistics in medicine.

[66]  W. Hespanhol,et al.  Regional odontodysplasia: report of a case involving two separate affected areas. , 1999, ASDC journal of dentistry for children.

[67]  N. Shah,et al.  Generalized odontodysplasia--a case report. , 1998, Journal of the Indian Society of Pedodontics and Preventive Dentistry.

[68]  T. Arx.  Autotransplantation for treatment of regional odontodysplasia. Case report with 6-year follow-up. , 1998 .

[69]  J. E. van der Wal,et al.  Regional odontodysplasia: report of three cases. , 1993, International journal of oral and maxillofacial surgery.

[70]  N. Pandis,et al.  Regional odontodysplasia. A case associated with asymmetric maxillary and mandibular development. , 1991, Oral surgery, oral medicine, and oral pathology.

[71]  H. Steiman,et al.  Bilateral mandibular regional odontodysplasia with vascular nevus. , 1991, Pediatric dentistry.

[72]  O'Neil Dw,et al.  Regional odontodysplasia: report of case. , 1990, ASDC journal of dentistry for children.

[73]  P. Crawford,et al.  Regional odontodysplasia: a bibliography. , 1989, Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology.

[74]  P. Slootweg,et al.  Regional odontodysplasia in epidermal nevus syndrome. , 1985, Journal of oral pathology.

[75]  J. Bogdanowicz,et al.  REPORT OF THREE CASES. , 1963, American journal of diseases of children.

[76]  Cindy Farquhar,et al.  Time-lapse systems for embryo incubation and assessment in assisted reproduction. , 2018, The Cochrane database of systematic reviews.

[77]  V. Raudonis,et al.  Embryo cell detection using regions with convolutional neural networks , 2017 .

[78]  M. Ramakrishnan,et al.  Odontodysplasia involving single tooth: A rare entity , 2014 .

[79]  Futabako Iizawa,et al.  Regional odontodysplasia: Long-term observation of a case on the mandibular left side , 2010 .

[80]  Thanos Athanasiou,et al.  Summary receiver operating characteristic curve analysis techniques in the evaluation of diagnostic tests. , 2005, The Annals of thoracic surgery.

[81]  C. Palma,et al.  Clinical management of regional odontodysplasia. , 2005, Pediatric dentistry.

[82]  张海霞,et al.  牙根管含氯消毒剂的功效[英]／Zamany A，Safavi K, Spangberg LSW ／／ Oral Surg Oral Med Oral Pathol. , 2005 .

[83]  B. Gogly,et al.  Regional odontodysplasia: expression of matrix metalloproteinases and their natural inhibitors. , 2003, Oral surgery, oral medicine, oral pathology, oral radiology, and endodontics.

[84]  T. von Arx.  Autotransplantation for treatment of regional odontodysplasia. Case report with 6-year follow-up. , 1998, Oral surgery, oral medicine, oral pathology, oral radiology, and endodontics.

[85]  J. C. Belisario,et al.  Report on a case of? , 1954, Australian journal of dermatology.