• Korean Journal of Optics and Photonics
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• v.24 no.6
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• pp.311-317
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• 2013

It was reported in Nature and the Wall Street Journal on June 20th, 2012 that scientists at Duke university have developed a gigapixel camera, capable of over 1,000 times the resolution of a normal camera. According to the reports, this super-resolution camera was motivated by the need of US military authorities to surveil ground and sky. We notice the ripple effect of this technology has spread into the area of national defense and industry, so that this research has started to realize the super-resolution camera as a frontier research topic. As a result, we can understand the optical structure of a super-resolution camera's lens system to be composed of a front, monocentric objective of a single lens plus 98 rear, multiscale camera lenses. We can also obtain the numerical ranges of specification factors related to the optical structure, such as the diameter of the aperture, and the focal length.

• Korean Journal of Optics and Photonics
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• v.27 no.1
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• pp.25-31
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• 2016

We derive 28 optical structural equations based on our two previous theoretical and experimental papers about a gigapixel camera, which were published in 2013 and 2015 respectively. Utilizing these 28 equations, we are able to obtain an integrated understanding of optical structure for a multiscale gigapixel camera system, in addition to obtaining numerical values for structural parameters very directly and easily.

• The Korean Journal of Pain
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• v.32 no.4
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• pp.286-291
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• 2019

Background: Breast cancer is complicated by a high incidence of chronic postoperative pain (25%-60%). Regional anesthesia might play an important role in lowering the incidence of chronic pain; however it is not known if the pectoral nerve block (PECS block), which is commonly used for breast surgery, is able to prevent this complication. Our main objective was therefore to detect any association between the PECS block and chronic pain at 3, 6, 9, and 12 months in patients undergoing breast surgery. Methods: We conducted a prospective, monocentric, observational study. We enrolled 140 consecutive patients undergoing breast surgery and divided them in patients receiving a PECS block and general anesthesia (PECS group) and patients receiving only general anesthesia (GA group). Then we considered both intraoperative variables (intravenous opioids administration), postoperative data (pain suffered by the patients during the first 24 postoperative hours and the need for additional analgesic administration) and development and persistence of chronic pain (at 3, 6, 9, and 12 mo). Results: The PECS group had a lower incidence of chronic pain at 3 months (14.9% vs. 31.8%, P = 0.039), needed less intraoperative opioids (fentanyl $1.61{\mu}g/kg/hr$ vs. $3.3{\mu}g/kg/hr$, P < 0.001) and had less postoperative pain (3 vs. 4, P = 0.017). Conclusions: The PECS block might play an important role in lowering incidence of chronic pain, but further studies are needed.

• Korean Journal of Radiology
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• v.21 no.7
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• pp.869-879
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• 2020

Objective: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and Methods: This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. Results: A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 x 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. Conclusion: Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.