With the continuous improvement of Synthetic Aperture Radar(SAR) resolution, interpreting the small targets like aircraft in SAR images becomes possible and turn out to be a hot spot in SAR application research. Howev...With the continuous improvement of Synthetic Aperture Radar(SAR) resolution, interpreting the small targets like aircraft in SAR images becomes possible and turn out to be a hot spot in SAR application research. However, due to the complexity of SAR imaging mechanism, interpreting targets in SAR images is a tough problem. This paper presents a new aircraft interpretation method based on the joint time-frequency analysis and multi-dimensional contrasting of basic structures. Moreover, SAR data acquisition experiment is designed for interpreting the aircraft. Analyzing the experiment data with our method, the result shows that the proposed method largely makes use of the SAR data information. The reasonable results can provide some auxiliary support for the SAR images manual interpretation.展开更多
Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classi...Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classification,object detection,land-cover/land-use classification,change detection,and multi-view stereo reconstruction.Large-scale training samples are essential for ML/DL models to achieve optimal performance.However,the current organization of training samples is ad-hoc and vendor-specific,lacking an integrated approach that can effectively manage training samples from different vendors to meet the demands of various RS AI tasks.This article proposes a solution to address these challenges by designing and implementing LuoJiaSET,a large-scale training sample database system for intelligent interpretation of RS imagery.LuoJiaSET accommodates over five million training samples,providing support for cross-dataset queries and serving as a comprehensive training data store for RS AI model training and calibration.It overcomes challenges related to label semantic categories,structural heterogeneity in label representation,and interoperable data access.展开更多
Meta-learning of dental X-rays is a machine learning technique that can be used to train models to perform new tasks quickly and with minimal input.Instead of just memorizing a task,this is accomplished through teachi...Meta-learning of dental X-rays is a machine learning technique that can be used to train models to perform new tasks quickly and with minimal input.Instead of just memorizing a task,this is accomplished through teaching a model how to learn.Algorithms for meta-learning are typically trained on a collection of training problems,each of which has a limited number of labelled instances.Multiple Xray classification tasks,including the detection of pneumonia,coronavirus disease 2019,and other disorders,have demonstrated the effectiveness of meta-learning.Meta-learning has the benefit of allowing models to be trained on dental X-ray datasets that are too few for more conventional machine learning methods.Due to the high cost and lengthy collection process associated with dental imaging datasets,this is significant for dental X-ray classification jobs.The ability to train models that are more resistant to fresh input is another benefit of meta-learning.展开更多
Artificial intelligence(AI)-augmented contrast-enhanced ultrasonography(CEUS)is emerging as a powerful tool in liver imaging,particularly in enhancing the accuracy of Liver Imaging Reporting and Data System(known as L...Artificial intelligence(AI)-augmented contrast-enhanced ultrasonography(CEUS)is emerging as a powerful tool in liver imaging,particularly in enhancing the accuracy of Liver Imaging Reporting and Data System(known as LI-RADS)classi-fication.This review synthesized published data on the integration of machine learning and deep learning techniques into CEUS,revealing that AI algorithms can improve the detection and quantification of contrast enhancement patterns.Such improvements led to more consistent LI-RADS categorization,reduced interoperator variability,and enabled real-time analysis that streamlined work-flow.The enhanced sensitivity of AI tools facilitated better differentiation between benign and malignant lesions,ultimately optimizing patient management.These advances suggest that AI-augmented CEUS could transform liver imaging by providing rapid,reliable,and objective assessments.However,the review also highlighted the need for further large-scale,multicenter studies to fully validate these findings and ensure the safe integration of AI into routine clinical practice.INTRODUCTION International hepatology society guidelines have established contrast-enhanced computed tomography(CT)and contrast-enhanced magnetic resonance imaging(MRI)as the imaging modalities of choice for diagnosing hepatocellular carcinoma(HCC)lesions larger than 1 cm.MRI remains the gold standard for detecting small HCC nodules in cirrhotic livers due to its superior soft-tissue contrast and functional imaging capabilities.However,early or atypical presentations remain challenging for differential diagnosis,staging,and treatment planning.In these scenarios contrast-enhanced ultrasonography(CEUS)is a valuable second-line tool,offering real-time,radiation-free evaluation and repeatability for follow-up.A recent meta-analysis of head-to-head studies reported comparable diagnostic performance between CEUS and CT/MRI with pooled sensitivities and specificities of 0.67/0.88 for CEUS vs 0.60/0.98 for CT/MRI in non-HCC malignancies,and similar specificities for HCC diagnosis(0.70 for CEUS vs 0.59 for CT;0.81 for CEUS vs 0.79 for MRI)[1].Given the limitations of individual imaging modalities,hybrid techniques and multimodal approaches are gaining traction for improving lesion detection,especially in cases where standard methods fall short.Artificial intelligence(AI)has emerged as a powerful tool in medical imaging,enhancing diagnostic accuracy and reliability across platforms.In CEUS liver imaging dynamic enhancement patterns often challenge consistent interpretation across observers.AI holds particular promise for standardizing assessments.The growing complexity of liver tumor evaluation has also driven interest in approaches that integrate serum bio-markers with advanced imaging.However,no single strategy currently meets all the diagnostic and prognostic re-quirements.Recent studies highlighted the potential of AI to bridge this gap by enabling precise image interpretation and facilitating the integration of heterogeneous clinical and imaging data[2].Altogether the convergence of CEUS with AI and radiomics offers a dynamic,quantitative,and potentially reproducible paradigm for liver lesion assessment,comple-menting traditional imaging methods.This review aimed to provide an overview of current advances in AI-driven CEUS for liver lesion assessment with a particular focus on automated Liver Imaging Reporting and Data System(LI-RADS)classification,radiomics-based models,and future clinical integration.While another recent systematic review[3]provided a comprehensive analysis of AI applications in CEUS,our approach offers a targeted perspective,emphasizing LI-RADS-centered scoring,automated lesion characterization,and clinical utility,particularly in the context of HCC diagnosis and management.In the methodological process of this narrative mini-review,the literature selection was primarily based on targeted PubMed searches.ChatGPT-4o(OpenAI)[4]was employed to assist in refining query parameters and identifying relevant,up-to-date peer-reviewed sources on CEUS-based AI applications.展开更多
BACKGROUND A recently developed method enables automated measurement of the hallux valgus angle(HVA)and the first intermetatarsal angle(IMA)from weightbearing foot radiographs.This approach employs bone segmentation t...BACKGROUND A recently developed method enables automated measurement of the hallux valgus angle(HVA)and the first intermetatarsal angle(IMA)from weightbearing foot radiographs.This approach employs bone segmentation to identify anatomical landmarks and provides standardized angle measurements based on established guidelines.While effective for HVA and IMA,preoperative radiograph analysis remains complex and requires additional measurements,such as the hallux interphalangeal angle(IPA),which has received limited research attention.AIM To expand the previous method,which measured HVA and IMA,by incorporating the automatic measurement of IPA,evaluating its accuracy and clinical relevance.METHODS A preexisting database of manually labeled foot radiographs was used to train a U-Net neural network for segmenting bones and identifying landmarks necessary for IPA measurement.Of the 265 radiographs in the dataset,161 were selected for training and 20 for validation.The U-Net neural network achieves a high mean Sørensen-Dice index(>0.97).The remaining 84 radiographs were used to assess the reliability of automated IPA measurements against those taken manually by two orthopedic surgeons(OA and OB)using computer-based tools.Each measurement was repeated to assess intraobserver(OA1 and OA2)and interobserver(O_(A2) and O_(B))reliability.Agreement between automated and manual methods was evaluated using the Intraclass Correlation Coefficient(ICC),and Bland-Altman analysis identified systematic differences.Standard error of measurement(SEM)and Pearson correlation coefficients quantified precision and linearity,and measurement times were recorded to evaluate efficiency.RESULTS The artificial intelligence(AI)-based system demonstrated excellent reliability,with ICC3.1 values of 0.92(AI vs OA2)and 0.88(AI vs O_(B)),both statistically significant(P<0.001).For manual measurements,ICC values were 0.95(OA2 vs OA1)and 0.95(OA2 vs OB),supporting both intraobserver and interobserver reliability.Bland-Altman analysis revealed minimal biases of:(1)1.61°(AI vs O_(A2));and(2)2.54°(AI vs O_(B)),with clinically acceptable limits of agreement.The AI system also showed high precision,as evidenced by low SEM values:(1)1.22°(O_(A2) vs O_(B));(2)1.77°(AI vs O_(A2));and(3)2.09°(AI vs O_(B)).Furthermore,Pearson correlation coefficients confirmed strong linear relationships between automated and manual measurements,with r=0.85(AI vs O_(A2))and r=0.90(AI vs O_(B)).The AI method significantly improved efficiency,completing all 84 measurements 8 times faster than manual methods,reducing the time required from an average 36 minutes to just 4.5 minutes.CONCLUSION The proposed AI-assisted IPA measurement method shows strong clinical potential,effectively corresponding with manual measurements.Integrating IPA with HVA and IMA assessments provides a comprehensive tool for automated forefoot deformity analysis,supporting hallux valgus severity classification and preoperative planning,while offering substantial time savings in high-volume clinical settings.展开更多
In this study, Xinjiang Karamaili Mountain ungulate wildlife nature reserve is taken as the research area, the vegetation coverage in 2020 is calculated and mapped by pixel dichotomy model, and the spatial and tempora...In this study, Xinjiang Karamaili Mountain ungulate wildlife nature reserve is taken as the research area, the vegetation coverage in 2020 is calculated and mapped by pixel dichotomy model, and the spatial and temporal distribution of vegetation coverage is analyzed. Combined with satellite image data since 2015, the vegetation change trend in recent 5 years is obtained by univariate linear regression analysis, which reflects the overall restoration of ecological environment in Karamaili Mountain nature reserve. The results showed that in 2020, the vegetation cover of Kashan Nature Reserve was low (0.1NDVI0.2) and medium-low (0.2NDVI0.4), which was in line with the vegetation cover characteristics of arid areas. The change of vegetation cover in Kashan Nature Reserve has been on the rise in the past five years, especially in the northwest and southwest of the reserve, which are far away from human activities. On the whole, the ecological environment of the reserve has been effectively improved in recent years and the reserve management center has made unremitting efforts to achieve a remarkable ecological restoration effect.展开更多
The detailed structures of the plumbing system of the early Permian Tarim flood basalt were investigated by 3-D seismic imaging.The images show that the Tarim flood basalt mainly erupted from central volcanoes distrib...The detailed structures of the plumbing system of the early Permian Tarim flood basalt were investigated by 3-D seismic imaging.The images show that the Tarim flood basalt mainly erupted from central volcanoes distributed展开更多
BACKGROUND Gastrointestinal bleeding(GIB)is a severe and potentially life-threatening condition,especially in cases of delayed treatment.Computed tomography angiography(CTA)plays a pivotal role in the early identifica...BACKGROUND Gastrointestinal bleeding(GIB)is a severe and potentially life-threatening condition,especially in cases of delayed treatment.Computed tomography angiography(CTA)plays a pivotal role in the early identification of upper and lower GIB and in the prompt treatment of the haemorrhage.AIM To determine whether a volumetric estimation of the extravasated contrast at CTA in GIB may be a predictor of subsequent positive angiographic findings.METHODS In this retrospective single-centre study,35 patients(22 men;median age 69 years;range 16-92 years)admitted to our institution for active GIB detected at CTA and further submitted to catheter angiography between January 2018 and February 2022 were enrolled.Twenty-three(65.7%)patients underwent endoscopy before CTA.Bleeding volumetry was evaluated in both arterial and venous phases via a semi-automated dedicated software.Bleeding rate was obtained from volume change between the two phases and standardised for unit time.Patients were divided into two groups,according to the angiographic signs and their concordance with CTA.RESULTS Upper bleeding accounted for 42.9%and lower GIB for 57.1%.Mean haemoglobin value at the admission was 7.7 g/dL.A concordance between positive CTA and direct angiographic bleeding signs was found in 19(54.3%)cases.Despite no significant differences in terms of bleeding volume in the arterial phase(0.55 mL vs 0.33 mL,P=0.35),a statistically significant volume increase in the venous phase was identified in the group of patients with positive angiography(2.06 mL vs 0.9 mL,P=0.02).In the latter patient group,a significant increase in bleeding rate was also detected(2.18 mL/min vs 0.19 mL/min,P=0.02).CONCLUSION In GIB of any origin,extravasated contrast volumetric analysis at CTA could be a predictor of positive angiography and may help in avoiding further unnecessary procedures.展开更多
Remote sensing mapping is an important research direction in the development of geographic surveying and mapping.In order to successfully implement the project of Mapping Western China(MWC),a technical mapping system ...Remote sensing mapping is an important research direction in the development of geographic surveying and mapping.In order to successfully implement the project of Mapping Western China(MWC),a technical mapping system has been established.In this project,many problems have been solved through technological innovation,such as block adjustment with scarce control points,large-scale aerial/satellite image mapping,and intelligent interpretation of multi-source images.Several softwares were developed,e.g.PixelGrid for aerial/satellite image mapping in a large area,FeatureStation for the integration of multi-source data in the complex terrain areas,and an airborne multi-band and multi-polarization interferometric data acquisition system for SAR mapping.For the first time,full coverage of 1:50,000 topographic data of China’s land territory has been produced,which means the geospatial framework of digital China is basically completed.With the implementation of other key national plans and projects(i.e.national geographic conditions monitoring and national remote sensing mapping),the focus has changed from MWC to national dynamic mapping.Accordingly,a dynamic mapping system is established.The data acquisition capability has developed from a single source to multiple sources and multiple modalities.The mapping capability has developed into dynamic mapping,and the capability for database update shows the characteristics of collaboration.The national geographic condition monitoring creates a multi-scale index system for statistical analysis for various needs.A multi-level and multi-dimensional technical system for statistical computing and decision-making service is developed for the transformation from dynamic monitoring to information service.In this paper,we give a brief introduction about the recent development of remote sensing mapping in China with respect to data acquisition,map production,and information service.The purpose of this paper is to motivate the establishment of theory and method for remote sensing mapping,technical and equipment in the smart mapping era,to improve the capability of perceiving,analyzing,mining,and applying geographic data,and to promote the intelligent development of geographic surveying and mapping.展开更多
Beginning with the analysis of the behavior of natural ants, this paper illuminates the principle and method that, by adopting image texture energy as pheromone and finding their way on the track of the pheromone, art...Beginning with the analysis of the behavior of natural ants, this paper illuminates the principle and method that, by adopting image texture energy as pheromone and finding their way on the track of the pheromone, artificial ants have the ability to identify and remember through similar measurement of pheromone. Based on the quantity of experiments, this paper analyzes some factors that influence the ability of artificial ants and draws some conclusions about the law of ant perception.展开更多
Monuments and historical centers, because of their particular importance, are studied in multiple ways. The study concerns different scientific disciplines and technology. Photogrammetry and remote sensing contribute ...Monuments and historical centers, because of their particular importance, are studied in multiple ways. The study concerns different scientific disciplines and technology. Photogrammetry and remote sensing contribute essentially to this study, because of the valuable qualitative and quantitative information they offer. In this paper we search through the possibilities of very high resolution satellite imagery on historical centers study, referring to Delphi historical center. The study concerns image enhancement techniques and visual interpretation of Ikonos satellite imagery. Image enhancement techniques facilitate visual interpretation, detection and recognition, of the physiognomy and spatial arrangement of Delphi historical center and offer information about physical and architectural features in the wide area of the historical center.展开更多
Artificial intelligence(AI)has been entwined with the field of radiology ever since digital imaging began replacing films over half a century ago.These algorithms,ranging from simplistic speech-to-text dictation progr...Artificial intelligence(AI)has been entwined with the field of radiology ever since digital imaging began replacing films over half a century ago.These algorithms,ranging from simplistic speech-to-text dictation programs to automated interpretation neural networks,have continuously sought to revolutionize medical imaging.With the number of imaging studies outpacing the amount of trained of readers,AI has been implemented to streamline workflow efficiency and provide quantitative,standardized interpretation.AI relies on massive amounts of data for its algorithms to function,and with the wide-spread adoption of Picture Archiving and Communication Systems(PACS),imaging data is accumulating rapidly.Current AI algorithms using machine-learning technology,or computer aided-detection,have been able to successfully pool this data for clinical use,although the scope of these algorithms remains narrow.Many systems have been developed to assist the workflow of the radiologist through PACS optimization and imaging study triage,however interpretation has generally remained a human responsibility for now.In this review article,we will summarize the current successes and limitations of AI in radiology,and explore the exciting prospects that deep-learning technology offers for the future.展开更多
This paper reviewed the developments of the last ten years in the field of international high-resolution earth observation, and introduced the developmental status and plans for China's high-resolution earth obser...This paper reviewed the developments of the last ten years in the field of international high-resolution earth observation, and introduced the developmental status and plans for China's high-resolution earth observation program. In addition, this paper expounded the transformation mechanism and procedure from earth observation data to geospatial information and geographical knowledge, and examined the key scientific and technological issues, including earth observation networks, high-precision image positioning, image understanding, automatic spatial information extraction, and focus services. These analyses provide a new impetus for pushing the application of China's high-resolution earth observation system from a "quantity" to "quality" change, from China to the world, from providing products to providing online service.展开更多
文摘With the continuous improvement of Synthetic Aperture Radar(SAR) resolution, interpreting the small targets like aircraft in SAR images becomes possible and turn out to be a hot spot in SAR application research. However, due to the complexity of SAR imaging mechanism, interpreting targets in SAR images is a tough problem. This paper presents a new aircraft interpretation method based on the joint time-frequency analysis and multi-dimensional contrasting of basic structures. Moreover, SAR data acquisition experiment is designed for interpreting the aircraft. Analyzing the experiment data with our method, the result shows that the proposed method largely makes use of the SAR data information. The reasonable results can provide some auxiliary support for the SAR images manual interpretation.
基金supported by the National Natural Science Foundation of China[grant number 42071354]supported by the Fundamental Research Funds for the Central Universities[grant number 2042022dx0001]supported by the Fundamental Research Funds for the Central Universities[grant number WUT:223108001].
文摘Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classification,object detection,land-cover/land-use classification,change detection,and multi-view stereo reconstruction.Large-scale training samples are essential for ML/DL models to achieve optimal performance.However,the current organization of training samples is ad-hoc and vendor-specific,lacking an integrated approach that can effectively manage training samples from different vendors to meet the demands of various RS AI tasks.This article proposes a solution to address these challenges by designing and implementing LuoJiaSET,a large-scale training sample database system for intelligent interpretation of RS imagery.LuoJiaSET accommodates over five million training samples,providing support for cross-dataset queries and serving as a comprehensive training data store for RS AI model training and calibration.It overcomes challenges related to label semantic categories,structural heterogeneity in label representation,and interoperable data access.
文摘Meta-learning of dental X-rays is a machine learning technique that can be used to train models to perform new tasks quickly and with minimal input.Instead of just memorizing a task,this is accomplished through teaching a model how to learn.Algorithms for meta-learning are typically trained on a collection of training problems,each of which has a limited number of labelled instances.Multiple Xray classification tasks,including the detection of pneumonia,coronavirus disease 2019,and other disorders,have demonstrated the effectiveness of meta-learning.Meta-learning has the benefit of allowing models to be trained on dental X-ray datasets that are too few for more conventional machine learning methods.Due to the high cost and lengthy collection process associated with dental imaging datasets,this is significant for dental X-ray classification jobs.The ability to train models that are more resistant to fresh input is another benefit of meta-learning.
文摘Artificial intelligence(AI)-augmented contrast-enhanced ultrasonography(CEUS)is emerging as a powerful tool in liver imaging,particularly in enhancing the accuracy of Liver Imaging Reporting and Data System(known as LI-RADS)classi-fication.This review synthesized published data on the integration of machine learning and deep learning techniques into CEUS,revealing that AI algorithms can improve the detection and quantification of contrast enhancement patterns.Such improvements led to more consistent LI-RADS categorization,reduced interoperator variability,and enabled real-time analysis that streamlined work-flow.The enhanced sensitivity of AI tools facilitated better differentiation between benign and malignant lesions,ultimately optimizing patient management.These advances suggest that AI-augmented CEUS could transform liver imaging by providing rapid,reliable,and objective assessments.However,the review also highlighted the need for further large-scale,multicenter studies to fully validate these findings and ensure the safe integration of AI into routine clinical practice.INTRODUCTION International hepatology society guidelines have established contrast-enhanced computed tomography(CT)and contrast-enhanced magnetic resonance imaging(MRI)as the imaging modalities of choice for diagnosing hepatocellular carcinoma(HCC)lesions larger than 1 cm.MRI remains the gold standard for detecting small HCC nodules in cirrhotic livers due to its superior soft-tissue contrast and functional imaging capabilities.However,early or atypical presentations remain challenging for differential diagnosis,staging,and treatment planning.In these scenarios contrast-enhanced ultrasonography(CEUS)is a valuable second-line tool,offering real-time,radiation-free evaluation and repeatability for follow-up.A recent meta-analysis of head-to-head studies reported comparable diagnostic performance between CEUS and CT/MRI with pooled sensitivities and specificities of 0.67/0.88 for CEUS vs 0.60/0.98 for CT/MRI in non-HCC malignancies,and similar specificities for HCC diagnosis(0.70 for CEUS vs 0.59 for CT;0.81 for CEUS vs 0.79 for MRI)[1].Given the limitations of individual imaging modalities,hybrid techniques and multimodal approaches are gaining traction for improving lesion detection,especially in cases where standard methods fall short.Artificial intelligence(AI)has emerged as a powerful tool in medical imaging,enhancing diagnostic accuracy and reliability across platforms.In CEUS liver imaging dynamic enhancement patterns often challenge consistent interpretation across observers.AI holds particular promise for standardizing assessments.The growing complexity of liver tumor evaluation has also driven interest in approaches that integrate serum bio-markers with advanced imaging.However,no single strategy currently meets all the diagnostic and prognostic re-quirements.Recent studies highlighted the potential of AI to bridge this gap by enabling precise image interpretation and facilitating the integration of heterogeneous clinical and imaging data[2].Altogether the convergence of CEUS with AI and radiomics offers a dynamic,quantitative,and potentially reproducible paradigm for liver lesion assessment,comple-menting traditional imaging methods.This review aimed to provide an overview of current advances in AI-driven CEUS for liver lesion assessment with a particular focus on automated Liver Imaging Reporting and Data System(LI-RADS)classification,radiomics-based models,and future clinical integration.While another recent systematic review[3]provided a comprehensive analysis of AI applications in CEUS,our approach offers a targeted perspective,emphasizing LI-RADS-centered scoring,automated lesion characterization,and clinical utility,particularly in the context of HCC diagnosis and management.In the methodological process of this narrative mini-review,the literature selection was primarily based on targeted PubMed searches.ChatGPT-4o(OpenAI)[4]was employed to assist in refining query parameters and identifying relevant,up-to-date peer-reviewed sources on CEUS-based AI applications.
文摘BACKGROUND A recently developed method enables automated measurement of the hallux valgus angle(HVA)and the first intermetatarsal angle(IMA)from weightbearing foot radiographs.This approach employs bone segmentation to identify anatomical landmarks and provides standardized angle measurements based on established guidelines.While effective for HVA and IMA,preoperative radiograph analysis remains complex and requires additional measurements,such as the hallux interphalangeal angle(IPA),which has received limited research attention.AIM To expand the previous method,which measured HVA and IMA,by incorporating the automatic measurement of IPA,evaluating its accuracy and clinical relevance.METHODS A preexisting database of manually labeled foot radiographs was used to train a U-Net neural network for segmenting bones and identifying landmarks necessary for IPA measurement.Of the 265 radiographs in the dataset,161 were selected for training and 20 for validation.The U-Net neural network achieves a high mean Sørensen-Dice index(>0.97).The remaining 84 radiographs were used to assess the reliability of automated IPA measurements against those taken manually by two orthopedic surgeons(OA and OB)using computer-based tools.Each measurement was repeated to assess intraobserver(OA1 and OA2)and interobserver(O_(A2) and O_(B))reliability.Agreement between automated and manual methods was evaluated using the Intraclass Correlation Coefficient(ICC),and Bland-Altman analysis identified systematic differences.Standard error of measurement(SEM)and Pearson correlation coefficients quantified precision and linearity,and measurement times were recorded to evaluate efficiency.RESULTS The artificial intelligence(AI)-based system demonstrated excellent reliability,with ICC3.1 values of 0.92(AI vs OA2)and 0.88(AI vs O_(B)),both statistically significant(P<0.001).For manual measurements,ICC values were 0.95(OA2 vs OA1)and 0.95(OA2 vs OB),supporting both intraobserver and interobserver reliability.Bland-Altman analysis revealed minimal biases of:(1)1.61°(AI vs O_(A2));and(2)2.54°(AI vs O_(B)),with clinically acceptable limits of agreement.The AI system also showed high precision,as evidenced by low SEM values:(1)1.22°(O_(A2) vs O_(B));(2)1.77°(AI vs O_(A2));and(3)2.09°(AI vs O_(B)).Furthermore,Pearson correlation coefficients confirmed strong linear relationships between automated and manual measurements,with r=0.85(AI vs O_(A2))and r=0.90(AI vs O_(B)).The AI method significantly improved efficiency,completing all 84 measurements 8 times faster than manual methods,reducing the time required from an average 36 minutes to just 4.5 minutes.CONCLUSION The proposed AI-assisted IPA measurement method shows strong clinical potential,effectively corresponding with manual measurements.Integrating IPA with HVA and IMA assessments provides a comprehensive tool for automated forefoot deformity analysis,supporting hallux valgus severity classification and preoperative planning,while offering substantial time savings in high-volume clinical settings.
文摘In this study, Xinjiang Karamaili Mountain ungulate wildlife nature reserve is taken as the research area, the vegetation coverage in 2020 is calculated and mapped by pixel dichotomy model, and the spatial and temporal distribution of vegetation coverage is analyzed. Combined with satellite image data since 2015, the vegetation change trend in recent 5 years is obtained by univariate linear regression analysis, which reflects the overall restoration of ecological environment in Karamaili Mountain nature reserve. The results showed that in 2020, the vegetation cover of Kashan Nature Reserve was low (0.1NDVI0.2) and medium-low (0.2NDVI0.4), which was in line with the vegetation cover characteristics of arid areas. The change of vegetation cover in Kashan Nature Reserve has been on the rise in the past five years, especially in the northwest and southwest of the reserve, which are far away from human activities. On the whole, the ecological environment of the reserve has been effectively improved in recent years and the reserve management center has made unremitting efforts to achieve a remarkable ecological restoration effect.
文摘The detailed structures of the plumbing system of the early Permian Tarim flood basalt were investigated by 3-D seismic imaging.The images show that the Tarim flood basalt mainly erupted from central volcanoes distributed
文摘BACKGROUND Gastrointestinal bleeding(GIB)is a severe and potentially life-threatening condition,especially in cases of delayed treatment.Computed tomography angiography(CTA)plays a pivotal role in the early identification of upper and lower GIB and in the prompt treatment of the haemorrhage.AIM To determine whether a volumetric estimation of the extravasated contrast at CTA in GIB may be a predictor of subsequent positive angiographic findings.METHODS In this retrospective single-centre study,35 patients(22 men;median age 69 years;range 16-92 years)admitted to our institution for active GIB detected at CTA and further submitted to catheter angiography between January 2018 and February 2022 were enrolled.Twenty-three(65.7%)patients underwent endoscopy before CTA.Bleeding volumetry was evaluated in both arterial and venous phases via a semi-automated dedicated software.Bleeding rate was obtained from volume change between the two phases and standardised for unit time.Patients were divided into two groups,according to the angiographic signs and their concordance with CTA.RESULTS Upper bleeding accounted for 42.9%and lower GIB for 57.1%.Mean haemoglobin value at the admission was 7.7 g/dL.A concordance between positive CTA and direct angiographic bleeding signs was found in 19(54.3%)cases.Despite no significant differences in terms of bleeding volume in the arterial phase(0.55 mL vs 0.33 mL,P=0.35),a statistically significant volume increase in the venous phase was identified in the group of patients with positive angiography(2.06 mL vs 0.9 mL,P=0.02).In the latter patient group,a significant increase in bleeding rate was also detected(2.18 mL/min vs 0.19 mL/min,P=0.02).CONCLUSION In GIB of any origin,extravasated contrast volumetric analysis at CTA could be a predictor of positive angiography and may help in avoiding further unnecessary procedures.
基金This work is supported by the National Natural Science Foundation of China[grant numbers 41701506 and 41671440].
文摘Remote sensing mapping is an important research direction in the development of geographic surveying and mapping.In order to successfully implement the project of Mapping Western China(MWC),a technical mapping system has been established.In this project,many problems have been solved through technological innovation,such as block adjustment with scarce control points,large-scale aerial/satellite image mapping,and intelligent interpretation of multi-source images.Several softwares were developed,e.g.PixelGrid for aerial/satellite image mapping in a large area,FeatureStation for the integration of multi-source data in the complex terrain areas,and an airborne multi-band and multi-polarization interferometric data acquisition system for SAR mapping.For the first time,full coverage of 1:50,000 topographic data of China’s land territory has been produced,which means the geospatial framework of digital China is basically completed.With the implementation of other key national plans and projects(i.e.national geographic conditions monitoring and national remote sensing mapping),the focus has changed from MWC to national dynamic mapping.Accordingly,a dynamic mapping system is established.The data acquisition capability has developed from a single source to multiple sources and multiple modalities.The mapping capability has developed into dynamic mapping,and the capability for database update shows the characteristics of collaboration.The national geographic condition monitoring creates a multi-scale index system for statistical analysis for various needs.A multi-level and multi-dimensional technical system for statistical computing and decision-making service is developed for the transformation from dynamic monitoring to information service.In this paper,we give a brief introduction about the recent development of remote sensing mapping in China with respect to data acquisition,map production,and information service.The purpose of this paper is to motivate the establishment of theory and method for remote sensing mapping,technical and equipment in the smart mapping era,to improve the capability of perceiving,analyzing,mining,and applying geographic data,and to promote the intelligent development of geographic surveying and mapping.
基金Founded by the National Science Foundation of China (No.42071094) .
文摘Beginning with the analysis of the behavior of natural ants, this paper illuminates the principle and method that, by adopting image texture energy as pheromone and finding their way on the track of the pheromone, artificial ants have the ability to identify and remember through similar measurement of pheromone. Based on the quantity of experiments, this paper analyzes some factors that influence the ability of artificial ants and draws some conclusions about the law of ant perception.
文摘Monuments and historical centers, because of their particular importance, are studied in multiple ways. The study concerns different scientific disciplines and technology. Photogrammetry and remote sensing contribute essentially to this study, because of the valuable qualitative and quantitative information they offer. In this paper we search through the possibilities of very high resolution satellite imagery on historical centers study, referring to Delphi historical center. The study concerns image enhancement techniques and visual interpretation of Ikonos satellite imagery. Image enhancement techniques facilitate visual interpretation, detection and recognition, of the physiognomy and spatial arrangement of Delphi historical center and offer information about physical and architectural features in the wide area of the historical center.
文摘Artificial intelligence(AI)has been entwined with the field of radiology ever since digital imaging began replacing films over half a century ago.These algorithms,ranging from simplistic speech-to-text dictation programs to automated interpretation neural networks,have continuously sought to revolutionize medical imaging.With the number of imaging studies outpacing the amount of trained of readers,AI has been implemented to streamline workflow efficiency and provide quantitative,standardized interpretation.AI relies on massive amounts of data for its algorithms to function,and with the wide-spread adoption of Picture Archiving and Communication Systems(PACS),imaging data is accumulating rapidly.Current AI algorithms using machine-learning technology,or computer aided-detection,have been able to successfully pool this data for clinical use,although the scope of these algorithms remains narrow.Many systems have been developed to assist the workflow of the radiologist through PACS optimization and imaging study triage,however interpretation has generally remained a human responsibility for now.In this review article,we will summarize the current successes and limitations of AI in radiology,and explore the exciting prospects that deep-learning technology offers for the future.
基金supported by National Basic Research Program of China(Grant No. 2012CB719906)
文摘This paper reviewed the developments of the last ten years in the field of international high-resolution earth observation, and introduced the developmental status and plans for China's high-resolution earth observation program. In addition, this paper expounded the transformation mechanism and procedure from earth observation data to geospatial information and geographical knowledge, and examined the key scientific and technological issues, including earth observation networks, high-precision image positioning, image understanding, automatic spatial information extraction, and focus services. These analyses provide a new impetus for pushing the application of China's high-resolution earth observation system from a "quantity" to "quality" change, from China to the world, from providing products to providing online service.
