Accurate prediction of myopic progression and high myopia by machine learning

Background Myopia is a leading cause of visual impairment in Asia and worldwide.However,accurately predicting the progression of myopia and the high risk of myopia remains a challenge.This study aims to develop a predictive model for the development of myopia.Methods We first retrospectively gathered 612530 medical records from five independent cohorts,encompassing 227543 patients ranging from infants to young adults.Subsequently,we developed a multivariate linear regression algorithm model to predict the progression of myopia and the risk of high myopia.Result The model to predict the progression of myopia achieved an R^(2) value of 0.964 vs a mean absolute error(MAE)of 0.119D[95%confidence interval(CI):0.119,1.146]in the internal validation set.It demonstrated strong generalizability,maintaining consistent performance across external validation sets:R^(2)=0.950 vs MAE=0.119D(95%CI:0.119,1.136)in validation study 1,R^(2)=0.950 vs MAE=0.121D(95%CI:0.121,1.144)in validation study 2,and R^(2)=0.806 vs MAE=−0.066D(95%CI:−0.066,0.569)in the Shanghai Children Myopia Study.In the Beijing Children Eye Study,the model achieved an R^(2) of 0.749 vs a MAE of 0.178D(95%CI:0.178,1.557).The model to predict the risk of high myopia achieved an area under the curve(AUC)of 0.99 in the internal validation set and consistently high area under the curve values of 0.99,0.99,0.96 and 0.99 in the respective external validation sets.Conclusion Our study demonstrates accurate prediction of myopia progression and risk of high myopia providing valuable insights for tailoring strategies to personalize and optimize the clinical management of myopia in children.

Artificial intelligence system for outcome evaluations of human in vitro fertilization-derived embryos

Background:In vitro fertilization(IVF)has emerged as a transformative solution for infertility.However,achieving favorable live-birth outcomes remains challenging.Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods,including static images and temporal videos.However,traditional embryo selection methods,primarily reliant on visual inspection of morphology,exhibit variability and are contingent on the experience of practitioners.Therefore,an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable.Methods:We employed artificial intelligence(AI)for embryo morphological grading,blastocyst embryo selection,aneuploidy prediction,and final live-birth outcome prediction.We developed and validated the AI models using multitask learning for embryo morphological assessment,including pronucleus type on day 1 and the number of blastomeres,asymmetry,and fragmentation of blastomeres on day 3,using 19,201 embryo photographs from 8271 patients.A neural network was trained on embryo and clinical metadata to identify good-quality embryos for implantation on day 3 or day 5,and predict live-birth outcomes.Additionally,a 3D convolutional neural network was trained on 418 time-lapse videos of preimplantation genetic testing(PGT)-based ploidy outcomes for the prediction of aneuploidy and consequent live-birth outcomes.Results:These two approaches enabled us to automatically assess the implantation potential.By combining embryo and maternal metrics in an ensemble AI model,we evaluated live-birth outcomes in a prospective cohort that achieved higher accuracy than experienced embryologists(46.1%vs.30.7%on day 3,55.0%vs.40.7%on day 5).Our results demonstrate the potential for AI-based selection of embryos based on characteristics beyond the observational abilities of human clinicians(area under the curve:0.769,95%confidence interval:0.709-0.820).These findings could potentially provide a noninvasive,high-throughput,and low-cost screening tool to facilitate embryo selection and achieve better outcomes.Conclusions:Our study underscores the AI model’s ability to provide interpretable evidence for clinicians in assisted reproduction,highlighting its potential as a noninvasive,efficient,and cost-effective tool for improved embryo selection and enhanced IVF outcomes.The convergence of cutting-edge technology and reproductive medicine has opened new avenues for addressing infertility challenges and optimizing IVF success rates.