During last 16 years we have successfully developed the computer assisted vectorcardiogram analysis systems: model TJ Ⅰ, TJ Ⅱ, and TJ Ⅲ, but some technical problems remained unresolved, such as the recognit...During last 16 years we have successfully developed the computer assisted vectorcardiogram analysis systems: model TJ Ⅰ, TJ Ⅱ, and TJ Ⅲ, but some technical problems remained unresolved, such as the recognition accuracy for vectorcardiograms, measurement of the parameters of complicated QRS waves, the ratio of T loop length to width, and the area of spatial vectors etc. A new system, model TJ Ⅳ was designed to resolve these technical problems. The system was equipped with a 586 computer with a CPU of 120 MHz. Special new low noise amplifier was employed and C language was used for programming. Three graph recognition techniques were used to enhance the accuracy of VCG recognition. 32 orthogonal electrocardiograms and vectorcardiograms were displayed and printed, and 566 parameters of vectorcardiograms were calculated. Our results with 150 cases showed that the system had high accuracy of graph recognition, and parameter calculation and the results were essentially consistent with those of manipulative methods. We were led to concluded when compared with TJ Ⅲ system, the new version has higher accuracy of processing and measurement for vectorcardiograms, is able to process more vectorcardiographic parameters, with higher processing speed.展开更多
Stroke and heart attack,which could be led by a kind of cerebrovascular and cardiovascular disease named as atherosclerosis,would seriously cause human morbidity and mortality.It is important for the early stage diagn...Stroke and heart attack,which could be led by a kind of cerebrovascular and cardiovascular disease named as atherosclerosis,would seriously cause human morbidity and mortality.It is important for the early stage diagnosis and monitoring medical intervention of the atherosclerosis.Carotid stenosis is a classical atherosclerotic lesion with vessel wall narrowing down and accumulating plaques burden.The carotid artery of intima-media thickness(IMT)is a key indicator to the disease.With the development of computer assisted diagnosis technology,the imaging techniques,segmentation algorithms,measurement methods,and evaluation tools have made considerable progress.Ultrasound imaging,being real-time,economic,reliable,and safe,now seems to become a standard in vascular assessment methodology especially for the measurement of IMT.This review firstly attempts to discuss the clinical relevance of measurements in clinical practice at first,and then followed by the challenges that one has to face when approaching the segmentation of ultrasound images.Secondly,the commonly used methods for the IMT segmentation and measurement are presented.Thirdly,discussion and evaluation of different segmentation techniques are performed.An overview of summary and future perspectives is given finally.展开更多
Purpose:Intertrochanteric fracture(ITF)classification is crucial for surgical decision-making.However,orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected.The objective of this stud...Purpose:Intertrochanteric fracture(ITF)classification is crucial for surgical decision-making.However,orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected.The objective of this study was to utilize an artificial intelligence(AI)method to improve the accuracy of ITF classification.Methods:We trained a network called YOLOX-SwinT,which is based on the You Only Look Once X(YOLOX)object detection network with Swin Transformer(SwinT)as the backbone architecture,using 762 radiographic ITF examinations as the training set.Subsequently,we recruited 5 senior orthopedic trauma surgeons(SOTS)and 5 junior orthopedic trauma surgeons(JOTS)to classify the 85 original images in the test set,as well as the images with the prediction results of the network model in sequence.Statistical analysis was performed using the SPSS 20.0(IBM Corp.,Armonk,NY,USA)to compare the differences among the SOTS,JOTS,SOTS+AI,JOTS+AI,SOTS+JOTS,and SOTS+JOTS+AI groups.All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field.Based on the actual clinical needs,after discussion,we integrated 8 subgroups into 5 new subgroups,and the dataset was divided into training,validation,and test sets by the ratio of 8:1:1.Results:The mean average precision at the intersection over union(IoU)of 0.5(mAP50)for subgroup detection reached 90.29%.The classification accuracy values of SOTS,JOTS,SOTS+AI,and JOTS+AI groups were 56.24%±4.02%,35.29%±18.07%,79.53%±7.14%,and 71.53%±5.22%,respectively.The paired t-test results showed that the difference between the SOTS and SOTS+AI groups was statistically significant,as well as the difference between the JOTS and JOTS+AI groups,and the SOTS+JOTS and SOTS+JOTS+AI groups.Moreover,the difference between the SOTS+JOTS and SOTS+JOTS+AI groups in each subgroup was statistically significant,with all p<0.05.The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant,while the difference between the SOTS+AI and JOTS+AI groups was not statistically significant.With the assistance of AI,the subgroup classification accuracy of both SOTS and JOTS was significantly improved,and JOTS achieved the same level as SOTS.Conclusion:In conclusion,the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.展开更多
文摘During last 16 years we have successfully developed the computer assisted vectorcardiogram analysis systems: model TJ Ⅰ, TJ Ⅱ, and TJ Ⅲ, but some technical problems remained unresolved, such as the recognition accuracy for vectorcardiograms, measurement of the parameters of complicated QRS waves, the ratio of T loop length to width, and the area of spatial vectors etc. A new system, model TJ Ⅳ was designed to resolve these technical problems. The system was equipped with a 586 computer with a CPU of 120 MHz. Special new low noise amplifier was employed and C language was used for programming. Three graph recognition techniques were used to enhance the accuracy of VCG recognition. 32 orthogonal electrocardiograms and vectorcardiograms were displayed and printed, and 566 parameters of vectorcardiograms were calculated. Our results with 150 cases showed that the system had high accuracy of graph recognition, and parameter calculation and the results were essentially consistent with those of manipulative methods. We were led to concluded when compared with TJ Ⅲ system, the new version has higher accuracy of processing and measurement for vectorcardiograms, is able to process more vectorcardiographic parameters, with higher processing speed.
基金This work is supported by Projects of International Cooperation and Exchanges,National Natural Science Foundation of China(NSFC)(Grant No.:30911120497)the National 973 project Grant No.:2011CB933103.
文摘Stroke and heart attack,which could be led by a kind of cerebrovascular and cardiovascular disease named as atherosclerosis,would seriously cause human morbidity and mortality.It is important for the early stage diagnosis and monitoring medical intervention of the atherosclerosis.Carotid stenosis is a classical atherosclerotic lesion with vessel wall narrowing down and accumulating plaques burden.The carotid artery of intima-media thickness(IMT)is a key indicator to the disease.With the development of computer assisted diagnosis technology,the imaging techniques,segmentation algorithms,measurement methods,and evaluation tools have made considerable progress.Ultrasound imaging,being real-time,economic,reliable,and safe,now seems to become a standard in vascular assessment methodology especially for the measurement of IMT.This review firstly attempts to discuss the clinical relevance of measurements in clinical practice at first,and then followed by the challenges that one has to face when approaching the segmentation of ultrasound images.Secondly,the commonly used methods for the IMT segmentation and measurement are presented.Thirdly,discussion and evaluation of different segmentation techniques are performed.An overview of summary and future perspectives is given finally.
基金supported by the Technological Innovation and Application Development Project of Chongqing(CSTB2021TIADKPX0074)the Chongqing Science and Health Joint Project(2024MSXM084)the Innovative Capability Enhancement programme of Army Medical Center of PLA(ZXYZZKY01).
文摘Purpose:Intertrochanteric fracture(ITF)classification is crucial for surgical decision-making.However,orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected.The objective of this study was to utilize an artificial intelligence(AI)method to improve the accuracy of ITF classification.Methods:We trained a network called YOLOX-SwinT,which is based on the You Only Look Once X(YOLOX)object detection network with Swin Transformer(SwinT)as the backbone architecture,using 762 radiographic ITF examinations as the training set.Subsequently,we recruited 5 senior orthopedic trauma surgeons(SOTS)and 5 junior orthopedic trauma surgeons(JOTS)to classify the 85 original images in the test set,as well as the images with the prediction results of the network model in sequence.Statistical analysis was performed using the SPSS 20.0(IBM Corp.,Armonk,NY,USA)to compare the differences among the SOTS,JOTS,SOTS+AI,JOTS+AI,SOTS+JOTS,and SOTS+JOTS+AI groups.All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field.Based on the actual clinical needs,after discussion,we integrated 8 subgroups into 5 new subgroups,and the dataset was divided into training,validation,and test sets by the ratio of 8:1:1.Results:The mean average precision at the intersection over union(IoU)of 0.5(mAP50)for subgroup detection reached 90.29%.The classification accuracy values of SOTS,JOTS,SOTS+AI,and JOTS+AI groups were 56.24%±4.02%,35.29%±18.07%,79.53%±7.14%,and 71.53%±5.22%,respectively.The paired t-test results showed that the difference between the SOTS and SOTS+AI groups was statistically significant,as well as the difference between the JOTS and JOTS+AI groups,and the SOTS+JOTS and SOTS+JOTS+AI groups.Moreover,the difference between the SOTS+JOTS and SOTS+JOTS+AI groups in each subgroup was statistically significant,with all p<0.05.The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant,while the difference between the SOTS+AI and JOTS+AI groups was not statistically significant.With the assistance of AI,the subgroup classification accuracy of both SOTS and JOTS was significantly improved,and JOTS achieved the same level as SOTS.Conclusion:In conclusion,the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
