Pesticide poisoning is one of the most common diseases in the emergency department, characterized by rapid changes in condition, a high misdiagnosis rate, and a poor prognosis. Measures for early removal of poisons ar...Pesticide poisoning is one of the most common diseases in the emergency department, characterized by rapid changes in condition, a high misdiagnosis rate, and a poor prognosis. Measures for early removal of poisons are crucial, and gastric lavage is one of the important measures. Regarding the post-gastric lavage effect, abdominal CT scanning has an important application value in the assessment of the gastric lavage effect after pesticide poisoning.展开更多
Colorectal cancer,a malignant lesion of the intestines,significantly affects human health and life,emphasizing the necessity of early detection and treatment.Accurate segmentation of colorectal cancer regions directly...Colorectal cancer,a malignant lesion of the intestines,significantly affects human health and life,emphasizing the necessity of early detection and treatment.Accurate segmentation of colorectal cancer regions directly impacts subsequent staging,treatment methods,and prognostic outcomes.While colonoscopy is an effective method for detecting colorectal cancer,its data collection approach can cause patient discomfort.To address this,current research utilizes Computed Tomography(CT)imaging;however,conventional CT images only capture transient states,lacking sufficient representational capability to precisely locate colorectal cancer.This study utilizes enhanced CT images,constructing a deep feature network from the arterial,portal venous,and delay phases to simulate the physician’s diagnostic process and achieve accurate cancer segmentation.The innovations include:1)Utilizing portal venous phase CT images to introduce a context-aware multi-scale aggregation module for preliminary shape extraction of colorectal cancer.2)Building an image sequence based on arterial and delay phases,transforming the cancer segmentation issue into an anomaly detection problem,establishing a pixel-pairing strategy,and proposing a colorectal cancer segmentation algorithm using a Siamese network.Experiments with 84 clinical cases of colorectal cancer enhanced CT data demonstrated an Area Overlap Measure of 0.90,significantly better than Fully Convolutional Networks(FCNs)at 0.20.Future research will explore the relationship between conventional and enhanced CT to further reduce segmentation time and improve accuracy.展开更多
Objective:Coronary artery anatomical variations and anomalies are an important topic due to their potential clinical manifestations.This study aims to investigate the prevalence of coronary artery anatomical variation...Objective:Coronary artery anatomical variations and anomalies are an important topic due to their potential clinical manifestations.This study aims to investigate the prevalence of coronary artery anatomical variations and anomalies in symptomatic patients with coronary computed tomography angiography(CCTA).Methods:This is a retrospective study that included all symptomatic patients who had CCTA in a tertiary care hospital in Saudi Arabia during a period of seven years.Results:The total number of included patients was 507(60%males)with a mean age of 57.4 years.Approximately 41%had luminal stenoses,averaging 49.7%.The total num-ber of patients with coronary anatomical variations(CAV)and coronary artery anomalies(CAA)was 217(43%).CAV prevalence was 26%,which included 14%non-right coronary dominance,5%short left main coronary artery(LMCA),and 7%division variations(trifurcation and quadrifurcarion)of the LMCA.The prevalence of CAA was 29%,which included 5%origin anomalies,22%myocardial bridge,and 2%course anomalies.Conclusions:A high prevalence of coronary artery anatomic variations and anomalies in symptomatic patients is reported in this study.Systematic reviews,meta-analyses,reporting guidelines,and unified definitions and classifications of cor-onary variations and anomalies are lacking in the literature,presenting potential opportunities for future research and publications.展开更多
Objective:To analyze the value of multi-slice spiral computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of carpal joint injury.Methods:A total of 130 patients with suspected wrist injuries admi...Objective:To analyze the value of multi-slice spiral computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of carpal joint injury.Methods:A total of 130 patients with suspected wrist injuries admitted to the Department of Orthopedics of our hospital from January 2023 to January 2024 were selected and randomly divided into a single group(n=65)and a joint group(n=65).The single group was diagnosed using multi-slice spiral CT,and the joint group was diagnosed using multi-slice spiral CT and magnetic resonance imaging,with pathological diagnosis as the gold standard.The diagnostic results of both groups were compared to the gold standard,and the diagnostic energy efficiency of both groups was compared.Results:The diagnostic results of the single group compared with the gold standard were significant(P<0.05).The diagnostic results of the joint group compared with the gold standard were not significant(P>0.05).The sensitivity and accuracy of diagnosis in the joint group were significantly higher than that in the single group(P<0.05).The specificity of diagnosis in the joint group was higher as compared to that in the single group(P>0.05).Conclusion:The combination of multi-slice spiral CT and MRI was highly accurate in diagnosing wrist injuries,and the misdiagnosis rate and leakage rate were relatively low.Hence,this diagnostic program is recommended to be popularized.展开更多
In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.De...In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.展开更多
Accurate extraction of pores and fractures is a prerequisite for constructing digital rocks for physical property simulation and microstructural response analysis.However,fractures in CT images are similar in grayscal...Accurate extraction of pores and fractures is a prerequisite for constructing digital rocks for physical property simulation and microstructural response analysis.However,fractures in CT images are similar in grayscale to the rock matrix,and traditional algorithms have difficulty to achieve accurate segmentation results.In this study,a dataset containing multiscale fracture information was constructed,and a U-Net semantic segmentation model with a scSE attention mechanism was used to classify shale CT images at the pixel level and compare the results with traditional methods.The results showed that the CLAHE algorithm effectively removed noise and enhanced the fracture information in the dark parts,which is beneficial for further fracture extraction.The Canny edge detection algorithm had significant false positives and failed to recognize the internal information of the fractures.The Otsu algorithm only extracted fractures with a significant difference from the background and was not sensitive enough for fine fractures.The MEF algorithm enhanced the edge information of the fractures and was also sensitive to fine fractures,but it overestimated the aperture of the fractures.The U-Net was able to identify almost all fractures with good continuity,with an MIou and Recall of 0.80 and 0.82,respectively.As the image resolution increases,more fine fracture information can be extracted.展开更多
文摘Pesticide poisoning is one of the most common diseases in the emergency department, characterized by rapid changes in condition, a high misdiagnosis rate, and a poor prognosis. Measures for early removal of poisons are crucial, and gastric lavage is one of the important measures. Regarding the post-gastric lavage effect, abdominal CT scanning has an important application value in the assessment of the gastric lavage effect after pesticide poisoning.
基金This work is supported by the Natural Science Foundation of China(No.82372035)National Transportation Preparedness Projects(No.ZYZZYJ).Light of West China(No.XAB2022YN10)The China Postdoctoral Science Foundation(No.2023M740760).
文摘Colorectal cancer,a malignant lesion of the intestines,significantly affects human health and life,emphasizing the necessity of early detection and treatment.Accurate segmentation of colorectal cancer regions directly impacts subsequent staging,treatment methods,and prognostic outcomes.While colonoscopy is an effective method for detecting colorectal cancer,its data collection approach can cause patient discomfort.To address this,current research utilizes Computed Tomography(CT)imaging;however,conventional CT images only capture transient states,lacking sufficient representational capability to precisely locate colorectal cancer.This study utilizes enhanced CT images,constructing a deep feature network from the arterial,portal venous,and delay phases to simulate the physician’s diagnostic process and achieve accurate cancer segmentation.The innovations include:1)Utilizing portal venous phase CT images to introduce a context-aware multi-scale aggregation module for preliminary shape extraction of colorectal cancer.2)Building an image sequence based on arterial and delay phases,transforming the cancer segmentation issue into an anomaly detection problem,establishing a pixel-pairing strategy,and proposing a colorectal cancer segmentation algorithm using a Siamese network.Experiments with 84 clinical cases of colorectal cancer enhanced CT data demonstrated an Area Overlap Measure of 0.90,significantly better than Fully Convolutional Networks(FCNs)at 0.20.Future research will explore the relationship between conventional and enhanced CT to further reduce segmentation time and improve accuracy.
文摘Objective:Coronary artery anatomical variations and anomalies are an important topic due to their potential clinical manifestations.This study aims to investigate the prevalence of coronary artery anatomical variations and anomalies in symptomatic patients with coronary computed tomography angiography(CCTA).Methods:This is a retrospective study that included all symptomatic patients who had CCTA in a tertiary care hospital in Saudi Arabia during a period of seven years.Results:The total number of included patients was 507(60%males)with a mean age of 57.4 years.Approximately 41%had luminal stenoses,averaging 49.7%.The total num-ber of patients with coronary anatomical variations(CAV)and coronary artery anomalies(CAA)was 217(43%).CAV prevalence was 26%,which included 14%non-right coronary dominance,5%short left main coronary artery(LMCA),and 7%division variations(trifurcation and quadrifurcarion)of the LMCA.The prevalence of CAA was 29%,which included 5%origin anomalies,22%myocardial bridge,and 2%course anomalies.Conclusions:A high prevalence of coronary artery anatomic variations and anomalies in symptomatic patients is reported in this study.Systematic reviews,meta-analyses,reporting guidelines,and unified definitions and classifications of cor-onary variations and anomalies are lacking in the literature,presenting potential opportunities for future research and publications.
文摘Objective:To analyze the value of multi-slice spiral computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of carpal joint injury.Methods:A total of 130 patients with suspected wrist injuries admitted to the Department of Orthopedics of our hospital from January 2023 to January 2024 were selected and randomly divided into a single group(n=65)and a joint group(n=65).The single group was diagnosed using multi-slice spiral CT,and the joint group was diagnosed using multi-slice spiral CT and magnetic resonance imaging,with pathological diagnosis as the gold standard.The diagnostic results of both groups were compared to the gold standard,and the diagnostic energy efficiency of both groups was compared.Results:The diagnostic results of the single group compared with the gold standard were significant(P<0.05).The diagnostic results of the joint group compared with the gold standard were not significant(P>0.05).The sensitivity and accuracy of diagnosis in the joint group were significantly higher than that in the single group(P<0.05).The specificity of diagnosis in the joint group was higher as compared to that in the single group(P>0.05).Conclusion:The combination of multi-slice spiral CT and MRI was highly accurate in diagnosing wrist injuries,and the misdiagnosis rate and leakage rate were relatively low.Hence,this diagnostic program is recommended to be popularized.
基金the Deanship for Research Innovation,Ministry of Education in Saudi Arabia,for funding this research work through project number IFKSUDR-H122.
文摘In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.
基金funded by the Natural Science Basis Research Plan in Shaanxi Province of China(No.2022JM-147).
文摘Accurate extraction of pores and fractures is a prerequisite for constructing digital rocks for physical property simulation and microstructural response analysis.However,fractures in CT images are similar in grayscale to the rock matrix,and traditional algorithms have difficulty to achieve accurate segmentation results.In this study,a dataset containing multiscale fracture information was constructed,and a U-Net semantic segmentation model with a scSE attention mechanism was used to classify shale CT images at the pixel level and compare the results with traditional methods.The results showed that the CLAHE algorithm effectively removed noise and enhanced the fracture information in the dark parts,which is beneficial for further fracture extraction.The Canny edge detection algorithm had significant false positives and failed to recognize the internal information of the fractures.The Otsu algorithm only extracted fractures with a significant difference from the background and was not sensitive enough for fine fractures.The MEF algorithm enhanced the edge information of the fractures and was also sensitive to fine fractures,but it overestimated the aperture of the fractures.The U-Net was able to identify almost all fractures with good continuity,with an MIou and Recall of 0.80 and 0.82,respectively.As the image resolution increases,more fine fracture information can be extracted.
