Pancreatoscopy plays a significant role in the diagnosis and treatment of pancreatic diseases.However,the risk of pancreatoscopy is remarkably greater than that of other endoscopic procedures,such as gastroscopy and b...Pancreatoscopy plays a significant role in the diagnosis and treatment of pancreatic diseases.However,the risk of pancreatoscopy is remarkably greater than that of other endoscopic procedures,such as gastroscopy and bronchoscopy,owing to its severe invasiveness.In comparison,virtual pancreatoscopy(VP)has shown notable advantages.However,because of the low resolution of current computed tomography(CT)technology and the small diameter of the pancreatic duct,VP has limited clinical use.In this study,an optimal path algorithm and super-resolution technique are investigated for the development of an open-source software platform for VP based on 3D Slicer.The proposed segmentation of the pancreatic duct from the abdominal CT images reached an average Dice coefficient of 0.85 with a standard deviation of 0.04.Owing to the excellent segmentation performance,a fly-through visualization of both the inside and outside of the duct was successfully reconstructed,thereby demonstrating the feasibility of VP.In addition,a quantitative analysis of the wall thickness and topology of the duct provides more insight into pancreatic diseases than a fly-through visualization.The entire VP system developed in this study is available at https://github.com/gaoyi/VirtualEndoscopy.git.展开更多
The mortality rate in the intensive care unit(ICU)is a key metric of hospital clinical quality.To enhance hospital performance,many methods have been proposed for the stratification of patients’different risk categor...The mortality rate in the intensive care unit(ICU)is a key metric of hospital clinical quality.To enhance hospital performance,many methods have been proposed for the stratification of patients’different risk categories,such as severity scoring systems and machine learning models.However,these methods make capturing time sequence information difficult,posing challenges to the continuous assessment of a patient’s severity during their hospital stay.Therefore,we built a predictive model that can make predictions throughout the patient’s stay and obtain the patient’s risk of death in real time.Our proposed model performed much better than other machine learning methods,including logistic regression,random forest,and XGBoost,in a full set of performance evaluation processes.Thus,the proposed model can support physicians’decisions by allowing them to pay more attention to high-risk patients and anticipate potential complications to reduce ICU mortality.展开更多
基金This work is partially supported by the Key-Area Research and Development Program of Guangdong Province,No.2021B0101420005the Key Technology Development Program of Shenzhen,No.JSGG20210713091811036+4 种基金the Department of Education of Guangdong Province,No.2017KZDXM072the National Natural Science Foundation of China,No.61601302the Shenzhen Key Laboratory Foundation,No.ZDSYS20200811143757022the Shenzhen Peacock Plan,No.KQTD2016053112051497the SZU Top Ranking Project,No.86000000210.
文摘Pancreatoscopy plays a significant role in the diagnosis and treatment of pancreatic diseases.However,the risk of pancreatoscopy is remarkably greater than that of other endoscopic procedures,such as gastroscopy and bronchoscopy,owing to its severe invasiveness.In comparison,virtual pancreatoscopy(VP)has shown notable advantages.However,because of the low resolution of current computed tomography(CT)technology and the small diameter of the pancreatic duct,VP has limited clinical use.In this study,an optimal path algorithm and super-resolution technique are investigated for the development of an open-source software platform for VP based on 3D Slicer.The proposed segmentation of the pancreatic duct from the abdominal CT images reached an average Dice coefficient of 0.85 with a standard deviation of 0.04.Owing to the excellent segmentation performance,a fly-through visualization of both the inside and outside of the duct was successfully reconstructed,thereby demonstrating the feasibility of VP.In addition,a quantitative analysis of the wall thickness and topology of the duct provides more insight into pancreatic diseases than a fly-through visualization.The entire VP system developed in this study is available at https://github.com/gaoyi/VirtualEndoscopy.git.
基金This research was supported by the Key Discipline Fund of Shenzhen Hospital of Southern Medical University(No.2021-2023ICU)the New-Generation Information Technology by the Scientific Research Platform of Institutions of Higher Education of the Education Department of Guangdong Province(No.2021ZDZX1014)the Shenzhen University(SZU)Top Ranking Project(No.86000000210)。
文摘The mortality rate in the intensive care unit(ICU)is a key metric of hospital clinical quality.To enhance hospital performance,many methods have been proposed for the stratification of patients’different risk categories,such as severity scoring systems and machine learning models.However,these methods make capturing time sequence information difficult,posing challenges to the continuous assessment of a patient’s severity during their hospital stay.Therefore,we built a predictive model that can make predictions throughout the patient’s stay and obtain the patient’s risk of death in real time.Our proposed model performed much better than other machine learning methods,including logistic regression,random forest,and XGBoost,in a full set of performance evaluation processes.Thus,the proposed model can support physicians’decisions by allowing them to pay more attention to high-risk patients and anticipate potential complications to reduce ICU mortality.
