Artificial intelligence is the new generation of productivity. Currently, there have been significant breakthroughs in the application of artificial intelligence in the field of medicine. Artificial intelligence models have been applied to support clinical decision. In the field of assisted reproductive medicine, artificial intelligence models have been established for predicting pregnant outcomes, automating ultrasound monitoring, monitoring ovulation induction processes, and automating semen analysis and embryo selection. This article reviews the research progress of artificial intelligence in reproductive clinicals, discusses future issues related to quality control and management of artificial intelligence in reproductive clinical practice, and provides recommendations for the further widespread application of artificial intelligence in assisted reproductive clinical activities.

Artificial intelligence is the new generation of productivity. Currently, there have been significant breakthroughs in the application of artificial intelligence in the field of medicine. Artificial intelligence models have been applied to support clinical decision. In the field of assisted reproductive medicine, artificial intelligence models have been established for predicting pregnant outcomes, automating ultrasound monitoring, monitoring ovulation induction processes, and automating semen analysis and embryo selection. This article reviews the research progress of artificial intelligence in reproductive clinicals, discusses future issues related to quality control and management of artificial intelligence in reproductive clinical practice, and provides recommendations for the further widespread application of artificial intelligence in assisted reproductive clinical activities.

Based on algorithm, machine learning could dig information from data and learn the rules between data, following by predicting and analyzing new data. Machine learning can be used for the establishment of pregnancy outcome prediction model, as well as for the selection of embryos with the highest implantation potential. This review identified 30 models, among which 28 were based on traditional machine learning and 2 were based on deep learning. Area under the receiver operating characteristic curve (AUC) was adopted for the estimation of model performance. On the whole, models based on traditional algorithm were of low to medium performance (0.600.90). Prediction and estimation models may improve treatment efficiency and standardize embryo selection process.

Based on algorithm, machine learning could dig information from data and learn the rules between data, following by predicting and analyzing new data. Machine learning can be used for the establishment of pregnancy outcome prediction model, as well as for the selection of embryos with the highest implantation potential. This review identified 30 models, among which 28 were based on traditional machine learning and 2 were based on deep learning. Area under the receiver operating characteristic curve (AUC) was adopted for the estimation of model performance. On the whole, models based on traditional algorithm were of low to medium performance (0.600.90). Prediction and estimation models may improve treatment efficiency and standardize embryo selection process.

OBJECTIVETo study the severe acute respiratory syndrome (SARS)-associated coronavirus genotype and its characteristics.

METHODSA SARS-associated coronavirus isolate named ZJ01 was obtained from throat swab samples taken from a patient in Hangzhou, Zhejing province. The complete genome sequence of ZJ01 consisted of 29,715 bp (GenBank accession: AY297028, version: gi: 30910859). Seventeen SARS-associated coronavirus genome sequences in GenBank were compared to analyze the common sequence variations and the probability of co-occurrence of multiple polymorphisms or mutations. Phylogenetic analysis of those sequences was done.

RESULTSBy bioinformatics processing and analysis, the 5 loci nucleotides at ZJ01 genome were found being T, T, G, T and T, respectively. Compared with other SARS-associated coronavirus genomes in the GenBank database, an A/G mutation was detected besides the other 4 mutation loci (C:G:C:C/T:T:T:T) involved in this genetic signature. Therefore a new definition was put forward according to the 5 mutation loci. SARS-associated coronavirus strains would be grouped into two genotypes (C:G:A:C:C/T:T:G:T:T), and abbreviated as SARS coronavirus C genotype and T genotype. On the basis of this new definition, the ZJ01 isolate belongs to SARS-associated coronavirus T genotype, first discovered and reported in mainland China. Phylogenetic analysis of the spike protein gene fragments of these SARS-associated coronavirus strains showed that the GZ01 isolate was phylogenetically distinct from other isolates, and compared with groups F1 and F2 of the T genotype, the isolates of BJ01 and CUHK-W1 were more closely related to the GZ01 isolate. It was interesting to find that two (A/G and C/T) of the five mutation loci occurred in the spike protein gene, which caused changes of Asp to Gly and Thr to Ile in the protein, respectively.

CONCLUSIONAttention should be paid to whether these genotype and mutation patterns are related to the virus's biological activities,epidemic characteristics and host clinical symptoms.

Genotype ; Humans ; Middle Aged ; Mutation ; SARS Virus ; genetics