• 한국광학회지
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• 제24권6호
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• pp.311-317
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• 2013

작년 6월 20일, 기존의 일반 카메라에 비해 화질이 1,000배 이상인 초고분해능의 기가픽셀카메라가 미국 듀크대의 과학자들에 의해 네이처에 보고되었고 이를 월스트리트 저널이 보도했다. 보도에 따르면, 이 카메라는 미군부의 필요에 의해 공중 및 지상배치감시를 목적으로 개발되었다는 것이다. 이 기술은 향후 국방과 산업분야의 전반에 걸쳐서 기술.경제적인 파급효과가 매우 클 것으로 예상되기 때문에 국내에서도 시급히 개발에 착수할 필요가 있다고 판단된다. 이에 본 연구에서는 슈퍼 분해능을 갖는 상분할 방식의 기가픽셀 카메라를 구현하기 위한 전초작업으로써 카메라 렌즈시스템의 광학적인 구조를 고찰하였고 더불어 구조와 관련된 렌즈사양 값들의 범위를 계산해 내었다.

• 한국광학회지
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• 제27권1호
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• pp.25-31
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• 2016

이 전에 발표한 멀티스케일 기가픽셀카메라에 대한 두 편의 이론과 실험연구를 바탕으로 본 논문에서는 최종적이고 종합적인 28개의 구조방정식들을 도출하였다. 본 논문의 결과 식들을 활용하면 어레이카메라 방식으로 구성되는 멀티스케일 기가픽셀카메라의 광학구조에 대한 통찰적인 이해가 가능하게 될 것이고 완전하고 신속하게 다양한 구조인자 값들을 손쉽게 획득 할 수 있을 것이다.

• The Korean Journal of Pain
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• 제32권4호
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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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• 제21권7호
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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.