Automating assessment of human embryo images and time-lapse sequences for IVF treatment

As the number of couples using In Vitro Fertilization (IVF) treatment to give birth increases, so too does the need for robust tools to assist embryologists in selecting the highest quality embryos for implantation. Quality scores assigned to embryonic structures are critical markers for predicting implantation potential of human blastocyst-stage embryos. Timing at which embryos reach certain cell and development stages in vitro also provides valuable information about their development progress and potential to become a positive pregnancy. The current workflow of grading blastocysts by visual assessment is susceptible to subjectivity between embryologists. Visually verifying when embryo cell stage increases is tedious and confirming onset of later development stages is also prone to subjective assessment. This thesis proposes methods to automate embryo image and time-lapse sequence assessment to provide objective evaluation of blastocyst structure quality, cell counting, and timing of development stages

Good practice recommendations for the use of time-lapse technology†

STUDY QUESTION: What recommendations can be provided on the approach to and use of time-lapse technology (TLT) in an IVF laboratory?SUMMARY ANSWER: The present ESHRE document provides 11 recommendations on how to introduce TLT in the IVF laboratory. WHAT IS KNOWN ALREADY: Studies have been published on the use of TLT in clinical embryology. However, a systematic assessmentof how to approach and introduce this technology is currently missing.STUDY DESIGN, SIZE, DURATION: A working group of members of the Steering Committee of the ESHRE Special Interest Group in Embryology and selected ESHRE members was formed in order to write recommendations on the practical aspects of TLT for the IVF laboratory.PARTICIPANTS/MATERIALS, SETTING, METHODS: The working group included 11 members of different nationalities with internationally recognized experience in clinical embryology and basic science embryology, in addition to TLT. This document is developed according to the manual for development of ESHRE recommendations for good practice. Where possible, the statements are supported by studies retrieved from a PUBMED literature search on ‘time-lapse’ and ART.MAIN RESULTS AND THE ROLE OF CHANCE: A clear clinical benefit of the use of TLT, i.e. an increase in IVF success rates, remains to be proven. Meanwhile, TLT systems are being introduced in IVF laboratories. The working group listed 11 recommendations on what to do before introducing TLT in the lab. These statements include an assessment of the pros and cons of acquiring a TLT system, selection of relevant morphokinetic parameters, selection of an appropriate TLT system with technical and customer support, development of an internal checklist and education of staff. All these aspects are explained further here, based on the current literature and expert opinion.LIMITATIONS, REASONS FOR CAUTION: Owing to the limited evidence available, recommendations are mostly based on clinical and technical expertise. The paper provides technical advice, but leaves any decision on whether or not to use TLT to the individual centres.WIDER IMPLICATIONS OF THE FINDINGS: This document is expected to have a significant impact on future developments of clinical embryology, considering the increasing role and impact of TLT

Human oocytes and embryos viewed by time-lapse videography, and the development of an embryo deselection model

Despite its wide application today, in vitro fertilization (IVF) treatment continues to have relatively low efficacy, largely due to inaccuracy in selecting the best quality embryo(s) from the cohort for transfer. Novel methodologies for improved selection are being developed, and time-lapse observation of human embryos is gaining increasing popularity due to the more detailed morphokinetic information obtained plus uninterrupted culture conditions. The morphokinetic information enables the use of quantitative timings in developmental milestones of embryos and qualitative measures of abnormal biological events, to assist embryo selection/deselection. This project aimed to identify current limitations in the use of such measures and to develop recommendations for improvement in clinical application. In the current study, most data were collected retrospectively from infertile couples seeking IVF treatment at a fertility clinic, with consent to use time-lapse incubation (Embryoscope) for embryo culture. Comparisons of time-lapse measures were made between embryos with confirmed implantation and non-implantation outcomes following uterine transfers. Thereafter, an embryo deselection model was proposed based on the retrospective findings, followed by prospective validation. It was found in the current study that the reference starting time point (t0) in certain existing time-lapse systems was inaccurate due to (i) the early biological variations between sibling oocytes, (ii) technical limitations in current equipment and protocols, and (iii) different insemination methods used (Papers 1&2). The above variations may be minimized by using pronuclear fading (PNF, a biological time point) as t0 rather than insemination (a procedural time point) (Paper 2). An example of such application was the comparison of embryo development between patients with high and low serum progesterone levels on the trigger-day (Paper 3). Furthermore, the growth rate of embryos reported in the literature is subject to multiple clinical or laboratory factors, and this was in agreement with the present study where a published time-lapse algorithm emphasizing quantitative timing parameters was shown to lose its discriminatory power in implantation prediction when applied in two different laboratories (Paper 4). Interestingly, the qualitative measures seemed to have better inter-laboratory transferability due to the embryo growth patterns appearing independent of clinical and technical factors (Paper 4). Two novel qualitative measures were reported in the present study, namely reverse cleavage and less than 6 intercellular contact points at the end of the 4-cell stage, showing negative correlations with embryo implantation outcomes (Papers 5&6). A qualitative embryo deselection model was therefore proposed, including several qualitative measures with implantation rates being potentially increased from 22.4% to 33.6% (Paper 6). Finally, an embryo deselection model combining both qualitative and quantitative measures was reported with the use of PNF as t0, showing significant prediction of implantation outcomes in embryos regardless of insemination method (Paper 7). In conclusion, this thesis demonstrates the usefulness of time-lapse embryo selection during IVF treatment in one specific laboratory. However, any new time-lapse parameter or model for embryo selection requires external validation by properly designed large-scale studies. Future clinical research and the development of integrated engineering and computer technology may further improve the efficacy of time-lapse selection of human embryos

Embryo Morphokinetics Based on Time-Lapse Observation

Embryo incubation and evaluation are critical steps in assisted reproductive technology (ART). Conventionally, embryo assessment has been done by embryologists through removing embryos from a conventional incubator during the culture period. Over recent years, time-lapse systems (TLS) have been established which can take digital images of embryos at key points and time intervals. This technique allows embryologists to assess the embryo quality in the steady culture environment. According to TLS studies and prepared algorithm models, it seems that TLS alone or in combination with conventional morphology can be considered as a useful diagnostic tool to determine high-quality embryos and improve embryonic implantation and pregnancy rates. In addition, there were remarkable differences between embryo developmental time points and intervals regarding embryo gender, embryo fragmentation, and type of ovarian stimulation protocol. For confident conclusion, time-lapse imaging should be evaluated in further studies, and the system should be evaluated for cost/benefit ratio effectiveness in individual laboratory

Nuevas estrategias para estimar la calidad embrionaria y el éxito de las embrio-transferencias mediante la evaluación no invasiva y selección automática en sistemas de time-lapse

La introducción de la tecnología de lapso de tiempo en la práctica clínica de fecundación in vitro permitió la supervisión ininterrumpida de los embriones durante todo el período de cultivo. Inicialmente, el objetivo principal era lograr un mejor desarrollo embrionario. Sin embargo, esta tecnología también introdujo el novedoso concepto de la morfocinética, parámetros relacionados con la dinámica de las células embrionarias. La gran cantidad de datos obtenidos permitió definir los rangos óptimos de la duración del ciclo celular en diferentes etapas del desarrollo del embrión, lo cual añadió información complementaria para asistir en la evaluación del embrión antes de la transferencia. Los marcadores cinéticos se convirtieron en parte de la estrategia de evaluación embrionaria con un gran potencial en aumentar las probabilidades de éxito clínico. Sin embargo, la anotación de estos parámetros aún depende de la subjetividad y experiencia de los profesionales que realizan las anotaciones. El uso de algoritmos de aprendizaje profundo para analizar eventos de desarrollo automáticamente es un paso hacia la implementación de Inteligencia Artificial en la evaluación de embriones, que se está convirtiendo en una tendencia importante en el futuro. El objetivo principal de la presente tesis es la optimización de la selección de embriones a través de protocolos no invasivos, con el objetivo de estandarizar la transferencia de un solo embrión en la práctica clínica. Se presta especial atención a la validación de una herramienta desarrollada mediante técnicas de Inteligencia Artificial para la automatización de la anotación de parámetros morfocinéticos y la evaluación de la metabolómica embrionaria mediante el análisis del estrés oxidativo en los medios de cultivo. Para ello, se establecieron tres objetivos específicos principales: Objetivo específico I. Caracterización de la sensibilidad y precisión de detección de los eventos de desarrollo del embrión humano preimplantacional mediante un software automatizado. Objetivo específico II. Comparación de la predicción de los resultados clínicos al aplicar las anotaciones morfocinéticas manuales y automatizadas de embriones con implantación conocida. Objetivo específico III. Análisis no invasivo del estado oxidativo del medio de cultivo de los embriones en combinación con la morfocinética de sistemas de lapso de tiempo. Gracias a la consecución de objetivos específicos, podemos concluir que el software de evaluación de embriones puede ser una alternativa automatizada y objetiva de anotar los parámetros morfocinéticos. Las anotaciones automatizadas aliviarían la carga de trabajo de los embriólogos, especialmente durante los eventos tempranos, los cuales se anotaron con gran precisión. Por otro lado, los eventos tardíos, pese a ser más variables, mostraron una mayor relevancia clínica en la predicción de resultados cuando se utilizaron algoritmos de selección publicados. Los eventos no anotados siempre pueden añadirse manualmente, lo cual aumenta la precisión de los datos. El ensayo de termoquimioluminiscencia (TCL) se validó con éxito como una herramienta no invasiva para realizar el análisis del estrés oxidativo de los medios de cultivo del embrión. La combinación de los parámetros oxidativos de TCL con los criterios morfocinéticos de lapso de tiempo presentaron un mayor poder discriminatorio que la evaluación morfológica en la identificación de embriones de alta calidad, sentando las bases para un método de selección de embriones más objetivo y no invasivo para reducir las transferencias múltiples de embriones.The introduction of time-lapse imaging to clinical in vitro fertilization practice enabled the undisturbed monitoring of embryos throughout the entire culture period. Initially, the main objective was to achieve a better embryo development. However, this technology also provided an insight into the novel concept of morphokinetics, parameters regarding embryo cell dynamics. The vast amount of data obtained defined the optimal ranges in the cell-cycle lengths at different stages of embryo development, which added valuable information to embryo assessment prior to transfer. Kinetic markers became part of embryo evaluation strategies with the potential to increase the chances of clinical success. However, the annotation of these parameters still depend on the subjectivity and experience of the professionals performing the annotations. The use of deep learning algorithms to analyze developmental events automatically is a step towards implementation of Artificial Intelligence into embryo assessment, which is becoming a significant trend in the future. The present thesis major research target is the optimization of embryo selection though non-invasive protocols, aiming for the standardization of single embryo transfer as gold standard. Special focus being given to the validation of an Artificial Intelligence developed tool for the automatization of the morphokinetic parameter annotation and assessment of embryo metabolomics through the analysis of oxidative stress in the spent culture media. For this, three main specific objectives were stablished: Specific objective I. Characterization of the detection sensitivity & accuracy when performing the annotation of the human preimplantation embryo developmental events with an automated software. Specific objective II. Clinical result prediction comparison when applying the manual and automated morphokinetic annotations of known implantation data embryos. Specific objective III. Non-invasive oxidative status analysis of the spent embryo culture medium in combination with Time-Lapse morphokinetics. Thanks to our specific objective attainment, we can conclude the embryo assessment software can be an automated and objective alternative of annotating the morphokinetic parameters. Automated annotations would ease the embryologists’ workload, especially during early events, which can be annotated with high accuracy. On the other hand, the more variable later events showed more clinical relevance in outcome prediction when using published embryo selection algorithms. Non-annotated events can still be annotated manually, increasing the accuracy of the data. The thermochemiluminescence (TCL) assay was successfully validated as a non-invasive tool to perform the analysis of the oxidative stress of the spent culture media of the embryo. The combination of the TCL oxidative parameters with the time-lapse morphokinetic criteria presented a greater discriminatory power than morphological assessment in the identification of high-quality embryos, providing the foundation for a more objective and non-invasive embryo selection method to reduce multiple embryo transfers

Exploring the complexities of AI-Assisted Embryo Selection: a comprehensive review

In-Vitro Fertilization is among the most widespread and successful treatments for infertility. One of its main challenges is the evaluation and selection of embryos for implantation, a process which suffers from large inter- and intra-observer variability. Due to the advancements in time-lapse imaging, Deep Learning (DL) methods are gaining attention to address this issue, raising both technical and ethical questions. The published works on the topic either fail to address the generality of the problem by focusing on a particular approach or compare different approaches in a misleading manner. In this master thesis, we present and compare different DL-based alternatives delving into technical characteristics, explainability aspects, ethical considerations and clinical applications. Moreover, we propose a set of guidelines for the development of an AI model for embryo selection based on the previous analysis of the literature. Our ultimate goal is to offer a better understanding of the complexities involved in this problem as a necessary first step while working in such a sensitive domain

Seleção de embriões pela análise de imagens: uma abordagem Deep Learning

Infertility affects about 186 million people worldwide and 9-10% of couples in Portugal, causing financial, social and medical problems. Evaluation of embryo quality based morphological features is the standard in vitro fertilization (IVF) clinics around the world. This process is subjective and time-consuming, and results in discrepant classifications among embryologists and clinics, leading to fail in predict accurately embryo implantation and live birth potential. Although assisted reproductive technologies (ART) such as IVF coupled with time lapse elimination of periodic transfer to microscopy assessment and stable embryo culture conditions for embryos development, has alleviated the infertility problem, there are significant limitations even considering morphokinetic analysis. Likewise, many patients require multiple IVF cycles to achieve pregnancy, making the selection of single embryo for transfer a critical challenge. Here, we demonstrate the reliability of machine learning, especially deep learning based on TensorFlow open source and Keras libraries for embryo raw TLI images features extraction and classification in clinical practice. Equally, we present a follow up pipeline for clinicians and researchers, with no expertise in machine learning, to easily, rapid and accurately utilize deep learning as a clinical decision support tool in embryos viability studies, as well in other medical field where the analysis of images is preeminentA infertilidade afeta cerca de 186 milhões de pessoas em todo o mundo e 9-10% dos casais em Portugal, causando problemas financeiros, sociais e de saúde. Constitui procedimento padrão a avaliação da qualidade dos embriões baseadas em características morfológicas. No entanto, tais avaliações são subjetivas e demoradas e resultam em classificações discrepantes entre embriologistas e clínicas causando problemas na avaliação do potencial do embrião. Embora as tecnologias de reprodução medicamente assistida, como a fertilização in vitro, acoplada à tecnologia time-lapse, tenham diminuído o problema da infertilidade, existem limitações significativas, mesmo considerando a análise morfocinética. Outrossim, muitas pacientes necessitam de múltiplos ciclos de fertilização para alcançar a gravidez, tornando a seleção do embrião com maior potencial de implantação e geração de nados vivos um desafio crítico. No presente projeto demonstramos a prova do conceito da confiabilidade de Machine Learning (aprendizagem automática), especialmente Deep Learning baseado em TensorFlow e Keras, para extrair e discriminar caraterísticas associadas ao potencial embrionário, em imagens time-lapse. Igualmente, apresentamos um pipeline para que clínicos e investigadores, sem experiência em Machine Learning, possam utilizar com facilidade, rapidez e precisão Deep Learning como ferramenta de apoio à decisão clínica em estudos de viabilidade de embriões, bem como noutras áreas médicas onde a análise de imagens seja proeminenteMestrado em Biologia Molecular e Celula