We applied Lempel-Ziv complexity (LZC) combined with brain electrical activity mapping (BEAM) to study the change of alertness under sleep deprivation in our research. Ten subjects were involved in 36 hours sleep deprivation (SD), during which spontaneous electroencephalogram (EEG) experiments and auditory evoked EEG experiments-Oddball were recorded once every 6 hours. Spontaneous and evoked EEG data were calculated and BEAMs were structured. Results showed that during the 36 hours of SD, alertness could be divided into three stages, i.e. the first 12 hours as the high stage, the middle 12 hours as the rapid decline stage and the last 12 hours as the low stage. During the period SD, LZC of Spontaneous EEG decreased over the whole brain to some extent, but remained consistent with the subjective scales. By BEAMs of event related potential, LZC on frontal cortex decreased, but kept consistent with the behavioral responses. Therefore, LZC can be effective to reflect the change of brain alertness. At the same time LZC could be used as a practical index to monitor real-time alertness because of its simple computation and fast calculation.
As the most important refraction part in the optical system, cornea possesses characteristics which are important parameters in ophthalmology clinical surgery. During the measurement of the cornea in our study, we acquired the corneal data of Orbscan Ⅱ corneal topographer in real time using the Hook technology under Windows, and then took the data into the corneal analysis software. We then further analyzed and calculated the data to obtain individual Q-value of overall corneal 360 semi-meridian. The corneal analysis software took Visual C++6.0 as development environment, used OpenGL graphics technology to draw three-dimensional individual corneal morphological map and the distribution curve of the Q-value, and achieved real-time corneal data query. It could be concluded that the analysis would further extend the function of the corneal topography system, and provide a solid foundation for the further study of automatic screening of corneal diseases.
In the evaluation of tear film stability based on corneal topography, a pretreatment algorithm for tear film video was proposed for eye movement, eyelash reflection and background interference. First, Sobel operator was used to detect the blur image. Next, the target image with highlighted ring pattern was obtained by the morphological open operation performed on the grayscale image. Then the ring pattern frequency of the target image was extracted through the Hough circle detection and fast Fourier transform, and a band-pass filter was applied to the target image according to the ring pattern frequency. Finally, binarization and morphological closed operation were used for the localization of the ring pattern. Ten tear film videos were randomly selected from the database and processed frame by frame through the above algorithm. The experimental results showed that the proposed algorithm was effective in removing the invalid images in the video sequence and positioning the ring pattern, which laid a foundation for the subsequent evaluation of tear film stability.
With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.