Objective To examine the clinical application of pulsed Doppler tissue imaging(DTI)for regional left ventricular function assessment in normal subjects. Methods We examined 50 healthy subjects(range 12-42 years,mean a...Objective To examine the clinical application of pulsed Doppler tissue imaging(DTI)for regional left ventricular function assessment in normal subjects. Methods We examined 50 healthy subjects(range 12-42 years,mean age 28.3 ± 6.9 years)using pulsed Doppler tissue imaging to characterize the diastolic and systolic velocity profiles of mitral annulus. Recordings were made along the long axis in the apical 4-chamber, 2-chamber, and long apical views of 6 sites(posterior-septum, lateral, anterior, inferior, anterior-septum, posterior)at the mitral annulus. Myocardial velocities were determined with use of variance F statistical analysis. Correlation analysis was employed to test the relationship between age and mitral annular velocities. Results Both early diastolic and systolic velocities at the septum were lower than other sites. There were no differences in mitral annulus late diastolic velocities. Mean early diastolic and systolic velocities was negatively correlated with age. Conclusions Doppler tissue imaging can directly reflect regional left ventricular function.展开更多
Ovarian cancer is one of the three most common gynecological cancers in the world,and is regarded as a priority in terms of women’s cancer.In the past few years,many researchers have attempted to develop and apply ar...Ovarian cancer is one of the three most common gynecological cancers in the world,and is regarded as a priority in terms of women’s cancer.In the past few years,many researchers have attempted to develop and apply artificial intelligence(AI)techniques to multiple clinical scenarios of ovarian cancer,especially in the field of medical imaging.AI-assisted imaging studies have involved computer tomography(CT),ultrasonography(US),and magnetic resonance imaging(MRI).In this review,we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer,and bring up the advances in terms of four clinical aspects,including medical diagnosis,pathological classification,targeted biopsy guidance,and prognosis prediction.Meanwhile,current status and existing issues of the researches on AI application in ovarian cancer are discussed.展开更多
文摘Objective To examine the clinical application of pulsed Doppler tissue imaging(DTI)for regional left ventricular function assessment in normal subjects. Methods We examined 50 healthy subjects(range 12-42 years,mean age 28.3 ± 6.9 years)using pulsed Doppler tissue imaging to characterize the diastolic and systolic velocity profiles of mitral annulus. Recordings were made along the long axis in the apical 4-chamber, 2-chamber, and long apical views of 6 sites(posterior-septum, lateral, anterior, inferior, anterior-septum, posterior)at the mitral annulus. Myocardial velocities were determined with use of variance F statistical analysis. Correlation analysis was employed to test the relationship between age and mitral annular velocities. Results Both early diastolic and systolic velocities at the septum were lower than other sites. There were no differences in mitral annulus late diastolic velocities. Mean early diastolic and systolic velocities was negatively correlated with age. Conclusions Doppler tissue imaging can directly reflect regional left ventricular function.
文摘Ovarian cancer is one of the three most common gynecological cancers in the world,and is regarded as a priority in terms of women’s cancer.In the past few years,many researchers have attempted to develop and apply artificial intelligence(AI)techniques to multiple clinical scenarios of ovarian cancer,especially in the field of medical imaging.AI-assisted imaging studies have involved computer tomography(CT),ultrasonography(US),and magnetic resonance imaging(MRI).In this review,we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer,and bring up the advances in terms of four clinical aspects,including medical diagnosis,pathological classification,targeted biopsy guidance,and prognosis prediction.Meanwhile,current status and existing issues of the researches on AI application in ovarian cancer are discussed.
