| 1 | Risk prediction platform for pancreatic fistula after pancreatoduodenectomy using artificial intelligence显示文摘BACKGROUND Despite advancements in operative technique and improvements in postoperative managements,postoperative pancreatic fistula(POPF)is a life-threatening complication following pancreatoduodenectomy(PD).There are some reports to predict POPF preoperatively or intraoperatively,but the accuracy of those is questionable.Artificial intelligence(AI)technology is being actively used in the medical field,but few studies have reported applying it to outcomes after PD.AIM To develop a risk prediction platform for POPF using an AI model.METHODS Medical records were reviewed from 1769 patients at Samsung Medical Center who underwent PD from 2007 to 2016.A total of 38 variables were inserted into AI-driven algorithms.The algorithms tested to make the risk prediction platform were random forest(RF)and a neural network(NN)with or without recursive feature elimination(RFE).The median imputation method was used for missing values.The area under the curve(AUC)was calculated to examine the discriminative power of algorithm for POPF prediction.RESULTS The number of POPFs was 221(12.5%)according to the International Study Group of Pancreatic Fistula definition 2016.After median imputation,AUCs using 38 variables were 0.68±0.02 with RF and 0.71±0.02 with NN.The maximal AUC using NN with RFE was 0.74.Sixteen risk factors for POPF were identified by AI algorithm:Pancreatic duct diameter,body mass index,preoperative serum albumin,lipase level,amount of intraoperative fluid infusion,age,platelet count,extrapancreatic location of tumor,combined venous resection,co-existing pancreatitis,neoadjuvant radiotherapy,American Society of Anesthesiologists’score,sex,soft texture of the pancreas,underlying heart disease,and preoperative endoscopic biliary decompression.We developed a web-based POPF prediction platform,and this application is freely available at http://popfrisk.smchbp.org.CONCLUSION This study is the first to predict POPF with multiple risk factors using AI.This platform is reliable(AUC 0.74),so it could be used to select patients who need especially intense therapy and to preoperatively establish an effective treatment strategy. | In Woong Han Kyeongwon Cho Youngju Ryu Sang Hyun Shin Jin Seok Heo Dong Wook Choi Myung Jin Chung Oh Chul Kwon Baek Hwan Cho | 2020 | World Journal of Gastroenterology2020，26，30： | 12 |
| 2 | Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network显示文摘The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections.Microbial infections are a major healthcare issue worldwide,as these widespread diseases often develop into deadly symptoms.While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection,this effective treatment is difficult to practice.The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification,which includes time-consuming sample growth.Here,we propose a microscopy-based framework that identifies the pathogen from single to few cells.Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network.We demonstrate the identification of 19 bacterial species that cause bloodstream infections,achieving an accuracy of 82.5%from an individual bacterial cell or cluster.This performance,comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample,underpins the effectiveness of our framework in clinical applications.Furthermore,our accuracy increases with multiple measurements,reaching 99.9%with seven different measurements of cells or clusters.We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections. | Geon Kim Daewoong Ahn Minhee Kang Jinho Park DongHun Ryu YoungJu Jo Jinyeop Song Jea Sung Ryu Gunho Choi Hyun Jung Chung Kyuseok Kim Doo Ryeon Chung In Young Yoo Hee Jae Huh Hyun-seok Min Nam Yong Lee YongKeun Park | 2022 | Light(Science & Applications)2022，11，7： | 2 |
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