A Multicenter Clinical Study of the Automated Fundus Screening Algorithm.

Purpose: To evaluate the effectiveness of automated fundus screening software in detecting eye diseases by comparing the reported results against those given by human experts. Results: There were 1585 subjects who completed the procedure and yielded qualified images. The prevalence of referable diabetic retinopathy (RDR), glaucoma suspect (GCS), and referable macular diseases (RMD) were 20.4%, 23.2%, and 49.0%, respectively. The overall sensitivity values for RDR, GCS, and RMD diagnosis are 0.948 (95% confidence interval [CI], 0.918-0.967), 0.891 (95% CI, 0.855-0.919), and 0.901 (95% CI-0.878, 0.920), respectively. The overall specificity values for RDR, GCS, and RMD diagnosis are 0.954 (95% CI, 0.915-0.965), 0.993 (95% CI-0.986, 0.996), and 0.955 (95% CI-0.939, 0.968), respectively. Methods: We prospectively enrolled 1743 subjects at seven hospitals throughout China. At each hospital, an operator records the subjects' information, takes fundus images, and submits the images to the Image Reading Center of Zhongshan Ophthalmic Center, Sun Yat-Sen University (IRC). The IRC grades the images according to the study protocol. Meanwhile, these images will also be automatically screened by the artificial intelligence algorithm. Then, the analysis results of automated screening algorithm are compared against the grading results of IRC. The end point goals are lower bounds of 95% CI of sensitivity values that are greater than 0.85 for all three target diseases, and lower bounds of 95% CI of specificity values that are greater than 0.90 for RDR and 0.85 for GCS and RMD. Conclusions: Automated fundus screening software demonstrated a high sensitivity and specificity in detecting RDR, GCS, and RMD from color fundus imaged captured using various cameras. Translational Relevance: These findings suggest that automated software can improve the screening effectiveness for eye diseases, especially in a primary care context, where experienced ophthalmologists are scarce.

A chiral stationary phase coated by surface molecularly imprinted polymer for separating 1,1'-binaphthalene-2,2'-diamine enantiomer by high performance liquid chromatography.

This report describes novel pathway for the development of a new type of CSP. A chiral stationary phase (CSP) prepared by coating a molecularly imprinted polymer (MIP) on the surface of silica gel (SMIP-CSP) showed a high separation factor (3.39) in separating racemic 1,1'-binaphthalene-2,2'-diamine (DABN) by high performance liquid chromatography (HPLC). Being the crosslinking agent, ethylene glycol dimethacrylate (EGDMA) was copolymerized with the monomer, methacrylic acid (MAA), in the presence of (R)-DABN as the template molecules on the surface of the silica gel particles to produce the SMIP-CSPs. The effects of the pretreatment temperature, the (R)-DABN content and the type of silica gel and monomer in the SMIP-CSPs on the separation of the racemic DABN were systematically investigated.

Detection and prediction of sudden cardiac death (SCD) for personal healthcare.

Sudden Cardiac Death (SCD) is one of continuing challenges to the modern clinician. It is responsible for an estimated 400,000 deaths per year in the United States and millions of deaths worldwide. This research developed a personal cardiac homecare system by sensing Lead-I ECG signals for detecting and predicting SCD events, which also builds in ECG identity verification. A MIT/BIH SCD Holter Database plus our ECG database were investigated. The system includes a self-made ECG amplifier, a NI DAQ card, a laptop computer, LabView and MatLab programs. The wavelet analysis was applied to detect SCD and the overall performance is 87.5% correct detection rate. In addition, artificial neural networks (ANN) were used to predict SCD events. The correct prediction rates by applying least mean square (LMS), decision based neural network (DBNN), and back propagation (BP) neural network were 67.44%, 58.14% and 55.81% respectively.