1.Design of real-time tele-monitoring system for physiological multi-parameter based on Internet
Xiuqing HAN ; Haomin LI ; Fangfang DU ; Shuicai WU
Chinese Medical Equipment Journal 1989;0(04):-
A real-time tele-monitoring system for physiological multi-parameter based on Internet is introduced,and physiological signals are transmitted by P2P network technology.The experiment results show that the speed of data transfer has been improved greatly using P2P technology,and physiological signals,such as ECG,can be monitored in real-time.
2.Effect of arotinolol on right ventricular function in patients with dilated cardiomyopathy
Hong YANG ; Li XU ; Yongkang TAO ; Zhimin XU ; Xiuqing DU ; Naqing LU ; Jinglin ZHAO ; Xianqi YUAN ; Yanfen ZHAO ; Rongfang SHI ; Chaomei FAN
Journal of Geriatric Cardiology 2007;4(3):170-173
Objective Dilated cardiomyopathy (DCM) is generally considered to be accompanied by both left and right ventricular dysfunction,but most studies only analyze the left ventricular function. In this study, we evaluated the effect of arotinolol on right ventricular function in patients with DCM. Methods Right ventricular ejection fraction (RVEF) and right ventricular diameter (RVD) were measured by two-dimensional echocardiography (2-DE) in 33 DCM patients; RVEF measured by first-pass radionuclide angiography (FPRA) was compared with that by 2-DE. Results The treatment with arotinolol for one year resulted in a reduction in the right ventricular diameter (baseline, 23.0 ± 8.3 mm vs after one-year treatment, 20.7 ± 5.4 mm; P=0.004 ) and an associated increase in ejection fraction (baseline, 36.9 ± 10.3% vs after one-year treatment, 45.8 ± 9.6%; P < 0.001 ); there is a high correlation between the 2-DE method and radionuclide ventriculographic method. The correlation coefficient is 0.933 (P<0.001). Conclusion Arotinolol therapy could not only improve left ventricular function, but also improve right ventricular function in DCM patients.
3.Application of artificial intelligence for community-based diabetic retinopathy detection and referral
Xiuqing DONG ; Shaolin DU ; Huaxiu LIU ; Jiangfeng ZOU ; Minghui LIU
Chinese Journal of Experimental Ophthalmology 2022;40(12):1158-1163
Objective:To evaluate the value of applying an artificial intelligence (AI) system for diabetic retinopathy (DR) detection and referral in community.Methods:A diagnostic test study was conducted.Four hundred and twenty-one patients (812 eyes) diagnosed with diabetes in three Dongguan community healthcare centers from January 1, 2020 to December 31, 2021 were enrolled.There were 267 males, accounting for 63.42% and 154 females, accounting for 36.58%.The subjects were 18-82 years old, with an average age of (51.72±11.28) years.The disease course of the subjects was 0-30 years, with an average course of 3.00 (1.00, 7.00) years.At least one macula-centered 50-degree fundus image was taken for each eye to build a DR image database.All the images were independently analyzed by an AI-assisted diagnostic system for DR, trained and qualified community physicians and ophthalmologists to make diagnosis including with or without DR, referable diabetic retinopathy (RDR) and referral recommendation or not.With diagnoses from ophthalmologists as the standard, sensitivity and specificity of the AI system in detecting DR and RDR were evaluated.The consistency and effective referral rate of the AI system and community physicians in detecting DR, especially in detecting RDR were evaluzted.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Dongguan Tungwah Hospital (No.2019DHLL046).Results:Of 812 eyes, 242 eyes were diagnosed with DR, including 23 with mild nonproliferative diabetic retinopathy (NPDR), 120 with moderate NPDR, 60 with severe NPDR and 39 with proliferative diabetic retinopathy (PDR). The other 570 eyes were diagnosed without DR.The sensitivity/specificity of AI system to detect DR and RDR was 87.60%/97.89% and 90.41%/96.29%, respectively.Compared with the ophthalmologists' diagnosis, the Cohen' s Kappa statistic of AI system to detect DR/RDR was 0.87/0.87, which was lower than 0.93/0.98 of community physicians.Among the referral-recommended cases by ophthalmologists, the effective referral rate of the AI system was 90.87% (199/219), which was higher than 89.50% (196/219) of community physicians, without statistically significant difference ( P=1.000). Conclusions:The AI system shows high sensitivity, specificity and consistency in DR detection, especially in RDR.The AI system is better in recognizing RDR than trained community physicians.
4. Relationship between the offset of a laser-assisted flap using the WaveLight FS200 femtosecond laser and the clinical results
Shaolin DU ; Wenkai ZHENG ; Xiuqing DONG ; Wei ZHOU ; Chao LI
Chinese Journal of Experimental Ophthalmology 2020;38(2):109-113
Objective:
To evaluate the relationship between the offset of a laser-assisted flap using the WaveLight FS200 femtosecond laser and the clinical results after femtosecond laser-assisted laser in situ keratomileusis (FS-LASIK).
Methods:
In this prospective cohort study, 125 patients who underwent FS-LASIK for myopia by WaveLight FS200 femtosecond laser from June 2017 to July 2018 at the Tungwah Ophthalmic Center were divided into two groups according to the offset of the corneal flap from the pupil center: the no-offset group (57 eyes) and the certain-offset group (68 eyes); the baseline data, including age, sex, uncorrected visual acuity(UCVA), spherical degree, and central corneal thickness were matched in the two groups.UCVA, residual astigmatism, spherical degree, corneal curvature and aberration were observed 1 week and 1 month after surgery.The study protocol was approved by the Ethics Committee of Tungwah Hospital of Sun Yat-Sen University (No.2017DHLL004). Written informed consent was obtained from each subject prior to entering the study cohort.
Results:
UCVA, corneal curvature, spherical degree, spherical and corneal aberration between the two groups were not significantly different (all at