Rock thin-section identification is an indispensable geological exploration tool for understanding and recognizing the composition of the earth.It is also an important evaluation method for oil and gas exploration and...Rock thin-section identification is an indispensable geological exploration tool for understanding and recognizing the composition of the earth.It is also an important evaluation method for oil and gas exploration and development.It can be used to identify the petrological characteristics of reservoirs,determine the type of diagenesis,and distinguish the characteristics of reservoir space and pore structure.It is necessary to understand the physical properties and sedimentary environment of the reservoir,obtain the relevant parameters of the reservoir,formulate the oil and gas development plan,and reserve calculation.The traditional thin-section identification method has a history of more than one hundred years,which mainly depends on the geological experts'visual observation with the optical microscope,and is bothered by the problems of strong subjectivity,high dependence on experience,heavy workload,long identification cycle,and incapability to achieve complete and accurate quantification.In this paper,the models of particle segmentation,mineralogy identification,and pore type intelligent identification are constructed by using deep learning,computer vision,and other technologies,and the intelligent thinsection identification is realized.This paper overcomes the problem of multi-target recognition in the image sequence,constructs a fine-grained classification network under the multi-mode and multi-light source,and proposes a modeling scheme of data annotation while building models,forming a scientific,quantitative and efficient slice identification method.The experimental results and practical application results show that the thin-section intelligent identification technology proposed in this paper does not only greatly improves the identification efficiency,but also realizes the intuitive,accurate and quantitative identification results,which is a subversive innovation and change to the traditional thin-section identification practice.展开更多
Background:Current lung cancer screening guidelines recommend annual low-dose computed tomography(LDCT)for high-risk individuals.However,the effectiveness of LDCT in non-high-risk individuals remains inadequately expl...Background:Current lung cancer screening guidelines recommend annual low-dose computed tomography(LDCT)for high-risk individuals.However,the effectiveness of LDCT in non-high-risk individuals remains inadequately explored.With the incidence of lung cancer steadily increasing among non-high-risk individuals,this study aims to assess the risk of lung cancer in non-high-risk individuals and evaluate the potential of thin-section LDCT reconstruction combined with artificial intelligence(LDCT-TRAI)as a screening tool.Methods:A real-world cohort study on lung cancer screening was conducted at the West China Hospital of Sichuan University from January 2010 to July 2021.Participants were screened using either LDCT-TRAI or traditional thick-section LDCT without AI(traditional LDCT).The AI system employed was the uAI-ChestCare software.Lung cancer diagnoses were confirmed through pathological examination.Results:Among the 259121 enrolled non-high-risk participants,87260(33.7%)had positive screening results.Within 1 year,728(0.3%)participants were diagnosed with lung cancer,of whom 87.1%(634/728)were never-smokers,and 92.7%(675/728)presented with stage I disease.Compared with traditional LDCT,LDCT-TRAI demonstrated a higher lung cancer detection rate(0.3%vs.0.2%,P<0.001),particularly for stage I cancers(94.4%vs.83.2%,P<0.001),and was associated with improved survival outcomes(5-year overall survival rate:95.4%vs.81.3%,P<0.0001).Conclusion:These findings highlight the importance of expanding lung cancer screening to non-high-risk populations,especially never-smokers.LDCT-TRAI outperformed traditional LDCT in detecting early-stage cancers and improving survival outcomes,underscoring its potential as a more effective screening tool for early lung cancer detection in this population.展开更多
基金supported by the Project of Basic Science Center for the National Natural Science Foundation of China(Grant No.72088101)。
文摘Rock thin-section identification is an indispensable geological exploration tool for understanding and recognizing the composition of the earth.It is also an important evaluation method for oil and gas exploration and development.It can be used to identify the petrological characteristics of reservoirs,determine the type of diagenesis,and distinguish the characteristics of reservoir space and pore structure.It is necessary to understand the physical properties and sedimentary environment of the reservoir,obtain the relevant parameters of the reservoir,formulate the oil and gas development plan,and reserve calculation.The traditional thin-section identification method has a history of more than one hundred years,which mainly depends on the geological experts'visual observation with the optical microscope,and is bothered by the problems of strong subjectivity,high dependence on experience,heavy workload,long identification cycle,and incapability to achieve complete and accurate quantification.In this paper,the models of particle segmentation,mineralogy identification,and pore type intelligent identification are constructed by using deep learning,computer vision,and other technologies,and the intelligent thinsection identification is realized.This paper overcomes the problem of multi-target recognition in the image sequence,constructs a fine-grained classification network under the multi-mode and multi-light source,and proposes a modeling scheme of data annotation while building models,forming a scientific,quantitative and efficient slice identification method.The experimental results and practical application results show that the thin-section intelligent identification technology proposed in this paper does not only greatly improves the identification efficiency,but also realizes the intuitive,accurate and quantitative identification results,which is a subversive innovation and change to the traditional thin-section identification practice.
基金supported by Non-communicable Chronic Diseases-National Science and Technology Major Project(Grant No.2023ZD0506102/2023ZD0506100)the National Natural Science Foundation of China(Grant No.92159302)+5 种基金the Science and Technology Project of Sichuan(Grant No.2022ZDZX0018)1·3·5 project for disciplines of excellence,West China Hospital,Sichuan University(Grant No.ZYGD22009)the Natural Science Foundation of Sichuan Province(Grant No.2023NSFSC1458),1·3·5 Project of State Key Laboratory of Respiratory Health and Multimorbidity,West China Hospital,Sichuan University(Grant No.RHM24204)the Science and Technology Project of Sichuan(Grant No.2020YFS0573)the Major research programs of the Natural Science Foundation of China(Grant No.91859203)Key R&D plan of Sichuan Provincial Department of science and technology(Grant No.2021YFS0072)。
文摘Background:Current lung cancer screening guidelines recommend annual low-dose computed tomography(LDCT)for high-risk individuals.However,the effectiveness of LDCT in non-high-risk individuals remains inadequately explored.With the incidence of lung cancer steadily increasing among non-high-risk individuals,this study aims to assess the risk of lung cancer in non-high-risk individuals and evaluate the potential of thin-section LDCT reconstruction combined with artificial intelligence(LDCT-TRAI)as a screening tool.Methods:A real-world cohort study on lung cancer screening was conducted at the West China Hospital of Sichuan University from January 2010 to July 2021.Participants were screened using either LDCT-TRAI or traditional thick-section LDCT without AI(traditional LDCT).The AI system employed was the uAI-ChestCare software.Lung cancer diagnoses were confirmed through pathological examination.Results:Among the 259121 enrolled non-high-risk participants,87260(33.7%)had positive screening results.Within 1 year,728(0.3%)participants were diagnosed with lung cancer,of whom 87.1%(634/728)were never-smokers,and 92.7%(675/728)presented with stage I disease.Compared with traditional LDCT,LDCT-TRAI demonstrated a higher lung cancer detection rate(0.3%vs.0.2%,P<0.001),particularly for stage I cancers(94.4%vs.83.2%,P<0.001),and was associated with improved survival outcomes(5-year overall survival rate:95.4%vs.81.3%,P<0.0001).Conclusion:These findings highlight the importance of expanding lung cancer screening to non-high-risk populations,especially never-smokers.LDCT-TRAI outperformed traditional LDCT in detecting early-stage cancers and improving survival outcomes,underscoring its potential as a more effective screening tool for early lung cancer detection in this population.
