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수술 전 관상피내암으로 진단되었던 환자에서 침윤성 유방암이 발견될 위험 인자
신선형(Sun Hyoung Shin),김병천(Byung Chun Kim),송영주(Young Ju Song),윤현철(Hyun Chul Yoon),조진성(Jin Seong Cho),박민호(Min Ho Park),윤정한(Jung Han Yoon),제갈영종(Young Jong Jegal) 대한외과학회 2011 Annals of Surgical Treatment and Research(ASRT) Vol.80 No.2
Purpose: Ductal carcinoma in situ (DCIS), unlike invasive ductal carcinoma, does not require sentinel lymph node biopsy or axillary lymph node dissection because the possibility of axillary lymph node metastasis is low. However, occasionally, despite preoperative diagnosis of DCIS, invasive ductal carcinoma can be diagnosed by postoperative biopsy. Therefore, a study of the associated risk factors is necessary. Methods: 198 patients with an initial diagnosis of DCIS, treated between February 2005 and December 2009, were retrospectively analyzed. Associations between clinical and pathologic factors were analyzed for significance using univariate and multivariate analyses. Results: Of the 198 patients, 57 (28.8%) were found to have invasive disease on final pathology. Multivariate analysis revealed 4 independent predictors of invasive cancer upon final pathology: diagnosis by needle biopsy (OR, 3.165; P=0.008), positive p53 on preoperative biopsy (OR, 2.494; P=0.019) DCIS size (>2 ㎝) on microscopic finding (OR, 2.683; P=0.014), and relatively young age (OR, 0.958, P=0.046). Of the 13 patients with positive axillary lymph nodes, 11 (84.6%) were shown to have invasive cancer on final pathology (P<0.001). Conclusion: In cases of preoperative diagnosis based on needle biopsy, positive p53, large tumor, and relatively young age, an SLNB procedure can be considered because in almost 30% of the patients an invasive carcinoma is found after surgery.
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봉약침(蜂藥鍼)을 위주로 한 요추추간판탈출증(腰椎椎間板脫出症)의 돌출형(突出型) 환자(患者)(protrusion disc patients)에 대한 임상적(臨床的) 고찰(考察)
이건목,이길숭,염승철,장재호,윤주영,황병천,국우석,장지연,최정선,김양중,박종운,조남근,Lee, Geon-mok,Lee, Kil-soong,Yeom, Seong-chul,Jang, Jae-ho,Yun, Ju-young,Hwang, Byung-chun,Kug, Yu-suk,Jang, Ji-yeon,Choi, Jeong-seon,Kim, Yang-jung,Par 대한침구의학회 2004 대한침구의학회지 Vol.21 No.5
Objective : Herniation of Nucleus Pulpous(HNP) of Lumbar is the most important reason that causes low back pain. The aim of this study is to investigate the effectiveness of Bee-venom acua-acupuncture therapy for protrusion disc patients. Methods : To evaluate the effectiveness of Bee-Venom Acupuncture Therapy, 20 patients were treated by bee-venom acua-acupuncture therapy. To estimate the efficacy of treatment, we used Quardruple Visual Analog Scale(QVAS). Results : 1. As a objectivity treatment record, they test treatment record good 60%, fair 25%, excellent 15%. 2. After bee-venom therapy, pain rate changed from 8.25 to 2.15. 3. By the results which puts out the statistics in sex, a pain rate of male changed from 8.75 to 2.50, a pain rate of female 7.92 to 1.92. Intentional difference is none as a therapy. By the results which puts out the statistics in age, after forties changed from 7.78 to 2.22 and before forties changed from 7.90 to 1.92. By the results which puts out the statistics in disc herniation, pain rate of central type changed from 8.29 to 2.29, pain rate of left type changed from 8.20 to 1.40, pain rate of Right type changed from 8.00 to 4.00.
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시험관 시술 성공률 개선을 위한 배아 등급 판별 인공지능 기반 대시보드
이우진(Woojin Lee),김설화(Seolhwa Kim),이대승(Daeseung Lee),이다현(Dahyun Kim),조병천(Byeongcheon Cho),서왕덕(Wangduk Seo) 한국정보기술학회 2025 Proceedings of KIIT Conference Vol.2025 No.6
현재 국내 의료계는 초혼 연령 증가와 노산으로 인한 난임 문제를 완화하기 위해 시험관 시술 연구에 관심이 높아지고 있다. 시험관 시술을 위해서는 배아를 배양하고, 이를 현미경으로 관찰하여 건강한 배아를 선별한다. 하지만, 의사의 직관으로 판정한 배아의 시험관 시술의 정확도는 37%로 난임 문제를 해결하기에 현저히 낮다. 이에 본 연구에서는 AI 배아 등급 판정 모델을 활용하여 배아 데이터를 수치화하여 평가하고, 시험관 시술의 정확도를 높이고자 하였다. 본 연구에서 제안하는 AI 배아 등급 판정 모델은 핵심적인 특징에 기반한 일관되고 객관적인 기준을 통해 시험관 시술의 정확도를 향상하고 대시보드 기반의 시각화 도구를 함께 제공함으로써 이용자의 접근성과 결과 해석의 직관성 또한 제고하였다. 실험 결과, 제안한 배아 등급 판정 시스템은 기존 기술보다 시험관 시술의 정확도에서 향상에 도움을 줄 수 있다. In response to the increasing prevalence of infertility caused by delayed marriages and advanced maternal age in South Korea, there has been growing interest in research on in vitro fertilization (IVF). A key step in IVF is the selection of viable embryos through microscopic observation after cultivation. However, the current embryo selection process—largely dependent on clinicians’ visual assessment—yields a success rate of only 37%, which is insufficient to effectively address infertility challenges. In this study, we propose an AI-driven embryo grading system that quantifies key morphological features to provide consistent and objective evaluations. By incorporating a deep learning-based model, the system enhances IVF outcome prediction and includes a dashboard interface that improves user accessibility and the ability for interpretation of results. Experimental evaluations show that the proposed system can help improving IVF accuracy compared to conventional assessment methods.
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