Visual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts for transfer after in vitro fertilization (IVF). However, the assessment produces different results between embryologists and as a result, the success rate of IVF remains low. To overcome uncertainties in embryo quality, multiple embryos are often implanted resulting in undesired multiple pregnancies and complications. Unlike in other imaging fields, human embryology and IVF have not yet leveraged artificial intelligence (AI) for unbiased, automated embryo assessment. We postulated that an AI approach trained on thousands of embryos can reliably predict embryo quality without human intervention. We implemented an AI approach based on deep neural networks (DNNs) to select highest quality embryos using a large collection of human embryo time-lapse images (about 50,000 images) from a high-volume fertility center in the United States. We developed a framework (STORK) based on Google’s Inception model. STORK predicts blastocyst quality with an AUC of >0.98 and generalizes well to images from other clinics outside the US and outperforms individual embryologists. Using clinical data for 2182 embryos, we created a decision tree to integrate embryo quality and patient age to identify scenarios associated with pregnancy likelihood. Our analysis shows that the chance of pregnancy based on individual embryos varies from 13.8% (age ≥41 and poor-quality) to 66.3% (age <37 and good-quality) depending on automated blastocyst quality assessment and patient age. In conclusion, our AI-driven approach provides a reproducible way to assess embryo quality and uncovers new, potentially personalized strategies to select embryos.

High-throughput next-generation sequencing has provided enormous insight into the genomic landscape of several tumor types, illuminating molecularly defined tumor subtypes, identifying new druggable targets, and providing insights into the heterogeneity of many tumors.¹ Metastatic tumors often undergo genomic evolution during progression and resistance, and therefore genomic drivers may not always be evident in the primary tumor. Furthermore, no specific guidelines exist to help clinicians interpret and contextualize individual patients’ genomic information when making therapeutic decisions. Herein, we describe an evidence-based precision medicine trial for patients with metastatic or treatment-resistant disease using a whole-exome sequencing (WES) clinical test called EXaCT-1, developed and validated by our group. Unique aspects include analysis of more than 21 000 genes of the cancer exome rather than a targeted hot-spot gene approach, complete disclosure of results through a WES clinical report, incorporation of metastatic and serial biopsies, use of fresh/frozen and formalin-fixed tissue, and development of patient-derived organoids and xenografts for co-clinical trials. Integral to the study are a comprehensive computational pipeline capable of categorizing mutations and generating a report for discussion in a multidisciplinary precision medicine tumor board and clinical follow-up to determine the clinical impact of mutations on subsequent response to therapies and patient outcomes. The overarching goals of our trial are to understand how WES affects therapeutic decision making in the context of advanced cancer care and to identify novel biomarkers of response.

Assessing fertilized human embryos is crucial for in vitro fertilization, a task being revolutionized by artificial intelligence. Existing models used for embryo quality assessment and ploidy detection could be significantly improved by effectively utilizing time-lapse imaging to identify critical developmental time points for maximizing prediction accuracy. Addressing this, we develop and compare various embryo ploidy status prediction models across distinct embryo development stages. We present BELA, a state-of-the-art ploidy prediction model that surpasses previous image- and video-based models without necessitating input from embryologists. BELA uses multitask learning to predict quality scores that are thereafter used to predict ploidy status. By achieving an area under the receiver operating characteristic curve of 0.76 for discriminating between euploidy and aneuploidy embryos on the Weill Cornell dataset, BELA matches the performance of models trained on embryologists’ manual scores. While not a replacement for preimplantation genetic testing for aneuploidy, BELA exemplifies how such models can streamline the embryo evaluation process.