Objective::This study investigates the efficacy of analyzing fetal heart rate (FHR) signals based on Artificial Intelligence to obtain a baseline calculation and identify accelerations/decelerations in the FHR through electronic fetal monitoring during labor.Methods::A total of 43,888 cardiotocograph(CTG) records of female patients in labor from January 2012 to December 2020 were collected from the NanFang Hospital of Southern Medical University. After filtering the data, 2341 FHR records were used for the study. The ObVue fetal monitoring system, manufactured by Lian-Med Technology Co. Ltd., was used to monitor the FHR signals for these pregnant women from the beginning of the first stage of labor to the end of delivery. Two obstetric experts together annotated the FHR signals in the system to determine the baseline as well as accelerations/decelerations of the FHR. Our cardiotocograph network (CTGNet) as well as traditional methods were then used to automatically analyze the baseline and acceleration/deceleration of the FHR signals. The results of calculations were compared with the annotations provided by the obstetric experts, and ten-fold cross-validation was applied to evaluate them. The root-mean-square difference (RMSD) between the baselines, acceleration F-measure (Acc.F-measure), deceleration F-measure (Dec.F-measure), coefficient of synthetic inconsistency (SI) and the morphological analysis discordance index (MADI) were used as evaluation metrics. The data were analyzed by using a paired t-test. Results::The proposed CTGNet was superior to the best traditional method, proposed by Mantel, in terms of the RMSD.BL (1.7935 ± 0.8099 vs. 2.0293 ± 0.9267, t=-3.55 , P=0.004), Acc.F-measure (86.8562 ± 10.9422 vs. 72.2367 ± 14.2096, t= 12.43, P <0.001), Dec.F-measure (72.1038 ± 33.2592 vs. 58.5040 ± 38.0276, t= 4.10, P <0.001), SI (34.8277±20.9595 vs. 54.8049 ± 25.0265, t=-9.39, P <0.001), and MADI (3.1741 ± 1.9901 vs. 3.7289 ± 2.7253, t= -2.74, P= 0.012). The proposed CTGNet thus had significant advantages over the best traditional method on all evaluation metrics. Conclusion::The proposed Artificial Intelligence-based method CTGNet delivers good performance in terms of the automatic analysis of FHR based on cardiotocograph data. It promises to be a key component of smart obstetrics systems of the future.

Objective::This study investigates the efficacy of analyzing fetal heart rate (FHR) signals based on Artificial Intelligence to obtain a baseline calculation and identify accelerations/decelerations in the FHR through electronic fetal monitoring during labor.Methods::A total of 43,888 cardiotocograph(CTG) records of female patients in labor from January 2012 to December 2020 were collected from the NanFang Hospital of Southern Medical University. After filtering the data, 2341 FHR records were used for the study. The ObVue fetal monitoring system, manufactured by Lian-Med Technology Co. Ltd., was used to monitor the FHR signals for these pregnant women from the beginning of the first stage of labor to the end of delivery. Two obstetric experts together annotated the FHR signals in the system to determine the baseline as well as accelerations/decelerations of the FHR. Our cardiotocograph network (CTGNet) as well as traditional methods were then used to automatically analyze the baseline and acceleration/deceleration of the FHR signals. The results of calculations were compared with the annotations provided by the obstetric experts, and ten-fold cross-validation was applied to evaluate them. The root-mean-square difference (RMSD) between the baselines, acceleration F-measure (Acc.F-measure), deceleration F-measure (Dec.F-measure), coefficient of synthetic inconsistency (SI) and the morphological analysis discordance index (MADI) were used as evaluation metrics. The data were analyzed by using a paired t-test. Results::The proposed CTGNet was superior to the best traditional method, proposed by Mantel, in terms of the RMSD.BL (1.7935 ± 0.8099 vs. 2.0293 ± 0.9267, t=-3.55 , P=0.004), Acc.F-measure (86.8562 ± 10.9422 vs. 72.2367 ± 14.2096, t= 12.43, P <0.001), Dec.F-measure (72.1038 ± 33.2592 vs. 58.5040 ± 38.0276, t= 4.10, P <0.001), SI (34.8277±20.9595 vs. 54.8049 ± 25.0265, t=-9.39, P <0.001), and MADI (3.1741 ± 1.9901 vs. 3.7289 ± 2.7253, t= -2.74, P= 0.012). The proposed CTGNet thus had significant advantages over the best traditional method on all evaluation metrics. Conclusion::The proposed Artificial Intelligence-based method CTGNet delivers good performance in terms of the automatic analysis of FHR based on cardiotocograph data. It promises to be a key component of smart obstetrics systems of the future.

Objective:To prepare compound Xiaoshangtong spray films and establish an HPLC method for quality control. Meth-ods:Chitosan hydrochloride and PVP as the main film-forming materials, and HPMC as the film-forming assitant agent, the com-pound Xiaoshangtong spray films were prepared. Lidocaine and mupirocin were simultaneously determined by HPLC. A Hypersil ODS2 column(250 mm × 4. 6 mm, 5 μm)was used. The mobile phase was composed of 0. 5% ammonium dihydrogen phosphate-methanol (40∶60, adjusting pH to 6. 0 ± 0. 5 with sodium hydroxide). The flow rate of mobile phase was 1. 0 ml·min-1 and the temperature of the column was 30 ℃. The detection wavelength was 222nm and the injection volume was 20 μl. Results: The linear range of lido-caine was 25. 0-400. 0 μg·ml-1(r=0. 999 7) and the average recovery was 100. 14% (RSD=1. 21%,n=9). The linear range of mupirocin was 25.0-400.0 μg·ml-1(r=0.999 9)and the average recovery was 101.13%(RSD=0.57%,n=9). Conclusion:The preparation process is reasonable. The established determination method is accurate and reliable, and suitable for the quality con-trol of the compound Xiaoshangtong spray films.