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 coll...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.展开更多
The high risk of postoperative mortality in lung adenocarcinoma(LUAD)patients is principally driven by cancer recurrence and low response rates to adjuvant treatment.Here,A combined cohort containing 1,026 stageⅠ-Ⅲp...The high risk of postoperative mortality in lung adenocarcinoma(LUAD)patients is principally driven by cancer recurrence and low response rates to adjuvant treatment.Here,A combined cohort containing 1,026 stageⅠ-Ⅲpatients was divided into the learning(n Z 678)and validation datasets(n Z 348).The former was used to establish a 16-mRNA risk signature for recurrence prediction with multiple statistical algorithms,which was verified in the valida-tion set.Univariate and multivariate analyses confirmed it as an independent indicator for both recurrence-free survival(RFS)and overall survival(OS).Distinct molecular characteristics between the two groups including genomic alterations,and hallmark pathways were compre-hensively analyzed.Remarkably,the classifier was tightly linked to immune infiltrations,high-lighting the critical role of immune surveillance in prolonging survival for LUAD.Moreover,the classifier was a valuable predictor for therapeutic responses in patients,and the low-risk group was more likely to yield clinical benefits from immunotherapy.A transcription factor regulato-ry proteineprotein interaction network(TF-PPI-network)was constructed via weighted gene co-expression network analysis(WGCNA)concerning the hub genes of the signature.The con-structed multidimensional nomogram dramatically increased the predictive accuracy.There-fore,our signature provides a forceful basis for individualized LUAD management with promising potential implications.展开更多
文摘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.
文摘The high risk of postoperative mortality in lung adenocarcinoma(LUAD)patients is principally driven by cancer recurrence and low response rates to adjuvant treatment.Here,A combined cohort containing 1,026 stageⅠ-Ⅲpatients was divided into the learning(n Z 678)and validation datasets(n Z 348).The former was used to establish a 16-mRNA risk signature for recurrence prediction with multiple statistical algorithms,which was verified in the valida-tion set.Univariate and multivariate analyses confirmed it as an independent indicator for both recurrence-free survival(RFS)and overall survival(OS).Distinct molecular characteristics between the two groups including genomic alterations,and hallmark pathways were compre-hensively analyzed.Remarkably,the classifier was tightly linked to immune infiltrations,high-lighting the critical role of immune surveillance in prolonging survival for LUAD.Moreover,the classifier was a valuable predictor for therapeutic responses in patients,and the low-risk group was more likely to yield clinical benefits from immunotherapy.A transcription factor regulato-ry proteineprotein interaction network(TF-PPI-network)was constructed via weighted gene co-expression network analysis(WGCNA)concerning the hub genes of the signature.The con-structed multidimensional nomogram dramatically increased the predictive accuracy.There-fore,our signature provides a forceful basis for individualized LUAD management with promising potential implications.
