Tear film instability is one of the major characteristics of dry eye syndrome. However, traditional diagnostic methods, such as the fluorescein tear film break-up time (FTBUT) test, are limited by the subjective interpretation of results. The test needs to manually identify break-up areas in the fluorescent image, thus producing variable diagnosis results. This paper proposes an automatic method to detect the fluorescent tear film break-up area using a deep convolutional neural network (CNN) model and to define its appearance as CNN-BUT. A digital slit-lamp recorded the standard FTBUT measurement for each of 80 study participants. Fifty participants were used to train the CNN model to identify the tear film break-up area, while the remaining 30 were used to validate the proposed CNN-BUT test. Among six normal controls and 24 dry eye patients enrolled in this paper, CNN-BUT was significantly lower in dry eye patients (p <;0.05). The correlation between CNN-BUT and FTBUT was also significant (r =0.9; p <;0.05). Using 5 s as the cutoff value, the CNN-BUT offered acceptable sensitivity and specificity to screen dry eye patients (0.83 and 0.95, respectively). These results indicate that CNN-BUT may be used to evaluate tear film stability and to assess the status of dry eye syndrome automatically.

Ocular surface damage is a major characteristic of dry eye syndrome. Ocular surface damage caused from dry eye refers to that there is superficial punctate keratitis (SPK), or also called the punctate dots, on the ocular surface. In the current diagnostic methods such as the ocular surface fluorescein-staining test, ophthalmologists dye the ocular surface to visualize punctate dots and then identify as well as count them for grading. Based on the grading results, ophthalmologists conduct a further diagnosis. For an expert, it is hard to achieve consistent results. Method: This study proposed to train a deep CNN (convolutional neural network) model to automatically detect punctate dots. Then we obtain a value, called the CNN-SPK value, which represents the coverage of punctate dots. Standard fluorescein-staining images from 101 participants were collected. Results: The correlation between the estimated CNN-SPK values of the rest 81 participants and the clinical grades were significant (r = 0.85; ${p} < 0.05$ ). Based on this observation, we suggest a statistical approach for the final grading. Using CNN-SPK values from 81 participants, as well as the corresponding clinical grades, we find CNN-SPK thresholds between any two adjacent grades. Also, we obtain the threshold between with- and without- SPK-symptoms, leading to 0.94 in sensitivity, and 0.79 in specificity. Conclusion: Our automatic method may be used to reliably grade the severity of punctate dots, to improve the efficiency of the dry diagnosis.

Electric cell–substrate impedance sensing (ECIS) is an attractive method for monitoring cell behaviors in tissue culture in real time. The time series impedance fluctuations of the cell-covered electrodes measured by ECIS are the phenomena accompanying cellular micromotion as cells continually rearrange their cell–cell and cell–substrate adhesion sites. Accurate assessment of these fluctuations to extract useful information from raw data is important for both scientific and practical purposes. In this study, we apply discrete wavelet transform (DWT) to analyze the concentration-dependent effect of cytochalasin B on human umbilical vein endothelial cells (HUVECs). The sampling rate of the impedance time series is 1 Hz and each data set consists of 2048 points. Our results demonstrate that, in the Daubechies (db) wavelet family, db1 is the optimal mother wavelet function for DWT-based analysis to assess the effect of cytochalasin B on HUVEC micromotion. By calculating the energy, standard deviation, variance, and signal magnitude area of DWT detail coefficients at level 1, we are able to significantly distinguish cytotoxic concentrations of cytochalasin B as low as 0.1 μM, and in a concentration-dependent manner. Furthermore, DWT-based analysis indicates the possibility to decrease the sampling rate of the micromotion measurement from 1 Hz to 1/16 Hz without decreasing the discerning power. The statistical measures of DWT detail coefficients are effective methods for determining both the sampling rate and the number of individual samples for ECIS-based micromotion assays.

Purpose: To test a cataract shadow projection theory and validate it by developing a deep learning algorithm that enables automatic and stable posterior polar cataract (PPC) screening using fundus images. Setting: Department of Ophthalmology, Far Eastern Memorial Hospital, New Taipei, Taiwan. Design: Retrospective chart review. Methods: A deep learning algorithm to automatically detect PPC was developed based on the cataract shadow projection theory. Retrospective data (n = 546) with ultra-wide field fundus images were collected, and various model architectures and fields of view were tested for optimization. Results: The final model achieved 80% overall accuracy, with 88.2% sensitivity and 93.4% specificity in PPC screening on a clinical validation dataset (n = 103). Conclusions: This study established a significant relationship between PPC and the projected shadow, which may help surgeons to identify potential PPC risks preoperatively and reduce the incidence of posterior capsular rupture during cataract surgery.

In clinical environments, orthopedic implants are associated with a risk of infection during implantation. However, the growth paths of bacteria on metal, which is nontransparent, are difficult to observe. In this study, we visualized the DH5-alpha Escherichia coli bacterial growth path on the surface of magnesium by using scanning electron microscope (SEM) images and constructed a convolutional neural network-based artificial intelligence (AI) system to identify metal surfaces, bacteria, and its generated products to grade the growth stage of the bacteria implanted on the magnesium. The detection result of the E. coli growth stage by the AI system was close to that manually marked by experts, and it may greatly accelerate the investigation of the bacterial growth process in various types of metallic material.