Objective:We propose a solution that is backed by cloud computing,combines a series of AI neural networks of computer vision;is capable of detecting,highlighting,and locating breast lesions from a live ultrasound vide...Objective:We propose a solution that is backed by cloud computing,combines a series of AI neural networks of computer vision;is capable of detecting,highlighting,and locating breast lesions from a live ultrasound video feed,provides BI-RADS categorizations;and has reliable sensitivity and specificity.Multiple deep-learning models were trained on more than 300,000 breast ultrasound images to achieve object detection and regions of interest classification.The main objective of this study was to determine whether the performance of our Al-powered solution was comparable to that of ultrasound radiologists.Methods:The noninferiority evaluation was conducted by comparing the examination results of the same screening women between our AI-powered solution and ultrasound radiologists with over 10 years of experience.The study lasted for one and a half years and was carried out in the Duanzhou District Women and Children's Hospital,Zhaoqing,China.1,133 females between 20 and 70 years old were selected through convenience sampling.Results:The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value were 93.03%,94.90%,90.71%,92.68%,and 93.48%,respectively.The area under the curve(AUC)for all positives was 0.91569 and the AUC for all negatives was 0.90461.The comparison indicated that the overall performance of the AI system was comparable to that of ultrasound radiologists.Conclusion:This innovative AI-powered ultrasound solution is cost-effective and user-friendly,and could be applied to massive breast cancer screening.展开更多
Breast cancer is the most common malignant tumor in Chinese women.Early screening is the best way to improve the rates of early diagnosis and survival of breast cancer patients.The peak onset age for breast cancer in ...Breast cancer is the most common malignant tumor in Chinese women.Early screening is the best way to improve the rates of early diagnosis and survival of breast cancer patients.The peak onset age for breast cancer in Chinese women is considerably younger than those in European and American women.It is imperative to develop breast cancer screening guideline that is suitable for Chinese women.By summarizing the current evidence on breast cancer screening in Chinese women,and referring to the latest guidelines and consensus on breast cancer screening in Europe,the United States,and East Asia,the China Anti-Cancer Association and National Clinical Research Center for Cancer(Tianjin Medical University Cancer Institute and Hospital)have formulated population-based guideline for breast cancer screening in Chinese women.The guideline provides recommendations on breast cancer screening for Chinese women at average or high risk of breast cancer according to the following three aspects:age of screening,screening methods,and screening interval.This article provides more detailed information to support the recommendations in this guideline and to provide more direction for current breast cancer screening practices in China.展开更多
One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of lesions.Thus,this study proposes a new fully automated method for the segmentation of ovarian and b...One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of lesions.Thus,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images.The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases.In addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion boundaries.This study uses two databases in training and testing the proposed segmentation approach.The breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in Iraq.Results of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound images.The segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data set.For the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.展开更多
Breast cancer(BC)is the most prevalent malignancy worldwide,and a continued upward trend has been predicted in the coming decades.Screening in selected targeted populations,which is effective in reducing cancer-relate...Breast cancer(BC)is the most prevalent malignancy worldwide,and a continued upward trend has been predicted in the coming decades.Screening in selected targeted populations,which is effective in reducing cancer-related mortality,has been widely implemented in many countries.This review summarizes the advances in BC screening techniques,organized or opportunistic BC screening programs across different countries,and screening modalities recommended by different academic authorities.Mammography is the most widely used and effective technique for BC screening.Other complementary techniques include ultrasound,clinical breast examination,and magnetic resonance imaging.Novel screening tests,including digital breast tomosynthesis and liquid biopsies,are still under development.Globally,the implementation status of BC screening programs is uneven,which is reflected by differences in screening modes,techniques,and population coverage.The recommended optimal screening strategies varied according to the authoritative guidelines.The effectiveness of current screening programs is influenced by several factors,including low detection rate,high false-positive rate,and unsatisfactory coverage and uptake rates.Exploration of accurate BC risk prediction models and the development of risk-stratified screening strategies are highly warranted in future research.展开更多
文摘Objective:We propose a solution that is backed by cloud computing,combines a series of AI neural networks of computer vision;is capable of detecting,highlighting,and locating breast lesions from a live ultrasound video feed,provides BI-RADS categorizations;and has reliable sensitivity and specificity.Multiple deep-learning models were trained on more than 300,000 breast ultrasound images to achieve object detection and regions of interest classification.The main objective of this study was to determine whether the performance of our Al-powered solution was comparable to that of ultrasound radiologists.Methods:The noninferiority evaluation was conducted by comparing the examination results of the same screening women between our AI-powered solution and ultrasound radiologists with over 10 years of experience.The study lasted for one and a half years and was carried out in the Duanzhou District Women and Children's Hospital,Zhaoqing,China.1,133 females between 20 and 70 years old were selected through convenience sampling.Results:The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value were 93.03%,94.90%,90.71%,92.68%,and 93.48%,respectively.The area under the curve(AUC)for all positives was 0.91569 and the AUC for all negatives was 0.90461.The comparison indicated that the overall performance of the AI system was comparable to that of ultrasound radiologists.Conclusion:This innovative AI-powered ultrasound solution is cost-effective and user-friendly,and could be applied to massive breast cancer screening.
基金supported by National Key Technology Support Program (Grant No. 2015BAI12B15)
文摘Breast cancer is the most common malignant tumor in Chinese women.Early screening is the best way to improve the rates of early diagnosis and survival of breast cancer patients.The peak onset age for breast cancer in Chinese women is considerably younger than those in European and American women.It is imperative to develop breast cancer screening guideline that is suitable for Chinese women.By summarizing the current evidence on breast cancer screening in Chinese women,and referring to the latest guidelines and consensus on breast cancer screening in Europe,the United States,and East Asia,the China Anti-Cancer Association and National Clinical Research Center for Cancer(Tianjin Medical University Cancer Institute and Hospital)have formulated population-based guideline for breast cancer screening in Chinese women.The guideline provides recommendations on breast cancer screening for Chinese women at average or high risk of breast cancer according to the following three aspects:age of screening,screening methods,and screening interval.This article provides more detailed information to support the recommendations in this guideline and to provide more direction for current breast cancer screening practices in China.
文摘One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of lesions.Thus,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images.The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases.In addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion boundaries.This study uses two databases in training and testing the proposed segmentation approach.The breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in Iraq.Results of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound images.The segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data set.For the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.
基金Natural Science Foundation of Beijing Municipality(Grant/Award Number:7202169)the Beijing Nova Program of Science and Technology(Grant/Award Number:Z191100001119065)。
文摘Breast cancer(BC)is the most prevalent malignancy worldwide,and a continued upward trend has been predicted in the coming decades.Screening in selected targeted populations,which is effective in reducing cancer-related mortality,has been widely implemented in many countries.This review summarizes the advances in BC screening techniques,organized or opportunistic BC screening programs across different countries,and screening modalities recommended by different academic authorities.Mammography is the most widely used and effective technique for BC screening.Other complementary techniques include ultrasound,clinical breast examination,and magnetic resonance imaging.Novel screening tests,including digital breast tomosynthesis and liquid biopsies,are still under development.Globally,the implementation status of BC screening programs is uneven,which is reflected by differences in screening modes,techniques,and population coverage.The recommended optimal screening strategies varied according to the authoritative guidelines.The effectiveness of current screening programs is influenced by several factors,including low detection rate,high false-positive rate,and unsatisfactory coverage and uptake rates.Exploration of accurate BC risk prediction models and the development of risk-stratified screening strategies are highly warranted in future research.
