Clinical Usability of Embryo Development Using a Combined Qualitative and Quantitative Approach in a Single Vitrified-Warmed Blastocyst Transfer: Assessment of Pre-Vitrified Blastocyst Diameter and Post-Warmed Blastocyst Re-Expansion Speed

Park, Jae Kyun; Ahn, Soyeon; Seok, Su Hee; Park, Sol Yi; Bang, So-Young; Eum, Jin Hee; Kwak, In Pyung; Kim, Ji Won; Lee, Woo Sik

doi:10.3390/jcm11237085

Cited by 8 publications

(4 citation statements)

References 51 publications

(80 reference statements)

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“…Therefore, without a consensus for which grading system to use, the clinical relevance of these models is restricted to clinics that are willing to adopt a new grading system or already use the system employed by the AI model. Although the quality evaluation has been correlated to pregnancy outcome ( Diakiw et al , 2022 ; Park et al , 2022 ), embryologist-defined quality evaluations do not necessarily correlate to the outcome of ongoing pregnancy ( Zhao et al , 2018 ; Kirillova et al , 2020 ). This is particularly relevant as there is a high degree of subjectivity in embryo quality assessment ( Fishel et al , 2020 ; Bormann et al , 2020b ; Fordham et al , 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Embryo selection through artificial intelligence versus embryologists: a systematic review

Salih,

Austin,

Warty

et al. 2023

Human Reproduction Open

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STUDY QUESTION What is the present performance of artificial intelligence (AI) decision support during embryo selection compared to the standard embryo selection by embryologists? SUMMARY ANSWER AI consistently outperformed the clinical teams in all the studies focused on embryo morphology and clinical outcome prediction during embryo selection assessment. WHAT IS KNOWN ALREADY The ART success rate is ∼30%, with a worrying trend of increasing female age correlating with considerably worse results. As such, there have been ongoing efforts to address this low success rate through the development of new technologies. With the advent of AI, there is potential for machine learning to be applied in such a manner that areas limited by human subjectivity, such as embryo selection, can be enhanced through increased objectivity. Given the potential of AI to improve IVF success rates, it remains crucial to review the performance between AI and embryologists during embryo selection. STUDY DESIGN, SIZE, DURATION The search was done across PubMed, EMBASE, Ovid Medline, and IEEE Xplore from 1 June 2005 up to and including 7 January 2022. Included articles were also restricted to those written in English. Search terms utilized across all databases for the study were: (‘Artificial intelligence’ OR ‘Machine Learning’ OR ‘Deep learning’ OR ‘Neural network’) AND (‘IVF’ OR ‘in vitro fertili*’ OR ‘assisted reproductive techn*’ OR ‘embryo’), where the character ‘*’ refers the search engine to include any auto completion of the search term. PARTICIPANTS/MATERIALS, SETTING, METHODS A literature search was conducted for literature relating to AI applications to IVF. Primary outcomes of interest were accuracy, sensitivity, and specificity of the embryo morphology grade assessments and the likelihood of clinical outcomes, such as clinical pregnancy after IVF treatments. Risk of bias was assessed using the Modified Down and Black Checklist. MAIN RESULTS AND THE ROLE OF CHANCE Twenty articles were included in this review. There was no specific embryo assessment day across the studies—Day 1 until Day 5/6 of embryo development was investigated. The types of input for training AI algorithms were images and time-lapse (10/20), clinical information (6/20), and both images and clinical information (4/20). Each AI model demonstrated promise when compared to an embryologist’s visual assessment. On average, the models predicted the likelihood of successful clinical pregnancy with greater accuracy than clinical embryologists, signifying greater reliability when compared to human prediction. The AI models performed at a median accuracy of 75.5% (range 59–94%) on predicting embryo morphology grade. The correct prediction (Ground Truth) was defined through the use of embryo images according to post embryologists’ assessment following local respective guidelines. Using blind test datasets, the embryologists’ accuracy prediction was 65.4% (range 47–75%) with the same ground truth provided by the original local respective assessment. Similarly, AI models had a median accuracy of 77.8% (range 68–90%) in predicting clinical pregnancy through the use of patient clinical treatment information compared to 64% (range 58–76%) when performed by embryologists. When both images/time-lapse and clinical information inputs were combined, the median accuracy by the AI models was higher at 81.5% (range 67–98%), while clinical embryologists had a median accuracy of 51% (range 43–59%). LIMITATIONS, REASONS FOR CAUTION The findings of this review are based on studies that have not been prospectively evaluated in a clinical setting. Additionally, a fair comparison of all the studies were deemed unfeasible owing to the heterogeneity of the studies, development of the AI models, database employed and the study design and quality. WIDER IMPLICATIONS OF THE FINDINGS AI provides considerable promise to the IVF field and embryo selection. However, there needs to be a shift in developers’ perception of the clinical outcome from successful implantation towards ongoing pregnancy or live birth. Additionally, existing models focus on locally generated databases and many lack external validation. STUDY FUNDING/COMPETING INTERESTS This study was funded by Monash Data Future Institute. All authors have no conflicts of interest to declare. REGISTRATION NUMBER CRD42021256333

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Section: Discussionmentioning

confidence: 99%

Embryo selection through artificial intelligence versus embryologists: a systematic review

Salih,

Austin,

Warty

et al. 2023

Human Reproduction Open

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“…Thus, high-quality embryo transfer is the most critical factor in live birth rates. Our previous study also demonstrated that goodquality embryos exhibit high clinical characteristics, and their importance has been continually emphasized (17)(18)(19). However, recent studies have revealed that embryo selection based solely on morphological characteristics is limited in predicting the possibility of pregnancy (45).…”

Section: Blastocyst Morphologymentioning

confidence: 93%

“…previously described (18,19). Post-warming blastocyst survival was confirmed by assessing the morphological integrity using an inverted microscope (Vitrolife).…”

Section: Ivf and Laboratory Proceduresmentioning

confidence: 99%

Development and validation of a nomogram for predicting ongoing pregnancy in single vitrified-warmed blastocyst embryo transfer cycles

Park,

Bang

et al. 2023

Front. Endocrinol.

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IntroductionThe global adoption of the “freeze-all strategy” has led to a continuous increase in utilization of single vitrified-warmed blastocyst embryo transfer (SVBT) owing to its clinical effectiveness. Accurate prediction of clinical pregnancy is crucial from a patient-centered perspective. However, this remains challenging, with inherent limitations due to the absence of precise and user-friendly prediction tools. Thus, this study primarily aimed to develop and assess a nomogram based on quantitative clinical data to optimize the efficacy of personalized prognosis assessment.Materials and methodsWe conducted a retrospective cohort analysis of ongoing pregnancy data from 658 patients with infertility who underwent SVBT at our center between October 17, 2017, and December 18, 2021. Patients were randomly assigned to the training (n=461) or validation (n=197) cohort for nomogram development and testing, respectively. A nomogram was constructed using the results of the multivariable logistic regression (MLR), which included clinical covariates that were assessed for their association with ongoing pregnancy.ResultsThe MLR identified eight significant variables that independently predicted ongoing pregnancy outcomes in the study population. These predictors encompassed maternal physiology, including maternal age at oocyte retrieval and serum anti-Müllerian hormone levels; uterine factors, such as adenomyosis; and various embryo assessment parameters, including the number of fertilized embryos, blastocyst morphology, blastulation day, blastocyst re-expansion speed, and presence of embryo string. The area under the receiver operating characteristic curve in our prediction model was 0.675 (95% confidence interval [CI], 0.622–0.729) and 0.656 (95% CI, 0.573–0.739) in the training and validation cohorts, respectively, indicating good discrimination performance in both cohorts.ConclusionsOur individualized nomogram is a practical and user-friendly tool that can provide accurate and useful SVBT information for patients and clinicians. By offering this model to patients, clinical stakeholders can alleviate uncertainty and confusion about fertility treatment options and enhance patients’ confidence in making informed decisions.

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“…Since then, live-cell imaging has been improved to allow an extended culture period with less damage to the cells and embryos 11 and is now used for many other species [12][13][14][15] . Live-cell imaging is commonly used for analyzing the developmental speed and morphology of embryos in human assisted reproductive technology and allows for prediction of embryo fate at the preimplantation stage, which can aid in selecting the most viable embryos for transfer 16,17 .…”

Section: Introductionmentioning

confidence: 99%

Live-cell imaging of mouse preimplantation embryos using a simple closed glass capillary method

Kikuchi

Ito

Wakayama

et al. 2023

Preprint

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Live-cell imaging is a popular method for analyzing mammalian preimplantation embryos, but it often requires expensive equipment and skilled techniques. We previously developed a simply and costly embryo-culture system in a sealed tube that does not require a CO2 incubator. In the present study, we developed a new live-cell imaging system using our previous culture method and a glass capillary. Zygotes were placed in a glass capillary and sunk in oil for observation under a stereomicroscope. Warming the capillary using a thermoplate enabled most of the zygotes to develop into blastocysts and produce healthy offspring. This live-cell imaging system captured images every 30 min for up to 5 days, which confirmed that the developmental speed and quality of the embryos were not affected, even with fluorescence. Overall, this new system is a reasonable, cost-effective, live-cell imaging method for preimplantation embryos that does not require dedicated machines and advanced techniques.

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Clinical Usability of Embryo Development Using a Combined Qualitative and Quantitative Approach in a Single Vitrified-Warmed Blastocyst Transfer: Assessment of Pre-Vitrified Blastocyst Diameter and Post-Warmed Blastocyst Re-Expansion Speed

Cited by 8 publications

References 51 publications

Embryo selection through artificial intelligence versus embryologists: a systematic review

Embryo selection through artificial intelligence versus embryologists: a systematic review

Development and validation of a nomogram for predicting ongoing pregnancy in single vitrified-warmed blastocyst embryo transfer cycles

Live-cell imaging of mouse preimplantation embryos using a simple closed glass capillary method

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