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Automatic Fetal Segmentation Designed on computer-aided detection with Ultrasound Images

computers, Materials & Continua 2024年第11期81卷 2967-2986页

作者： Mohana Priya Govindarajan Sangeetha Subramaniam Karuppaiya Bharathi Department of computer Science and Engineering SRM Institute of Science and TechnologyVadapalaniChennaiTamil Nadu600026India

In the present research,we describe a computer-aided detection(CAD)method aimed at automatic fetal head circumference(HC)measurement in 2D ultrasonography pictures during all trimesters of *** HC might be utilized toward determining gestational age and tracking fetal *** automated approach is particularly valuable in low-resource settings where access to trained sonographers is *** CAD system is divided into two steps:to begin,Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal *** identified the HC using dynamic programming,an elliptical fit,and a Hough *** computer-aided detection(CAD)program was well-trained on 999 pictures(HC18 challenge data source),and then verified on 335 photos from all trimesters in an independent test set.A skilled sonographer and an expert in medicine personally marked the test *** used the crown-rump length(CRL)measurement to calculate the reference gestational age(GA).In the first,second,and third trimesters,the median difference between the standard GA and the GA calculated by the skilled sonographer stayed at 0.7±2.7,0.0±4.5,and 2.0±12.0 days,*** regular duration variance between the baseline GA and the health investigator’s GA remained 1.5±3.0,1.9±5.0,and 4.0±14 a couple of *** mean variance between the standard GA and the CAD system’s GA remained between 0.5 and 5.0,with an additional variation of 2.9 to 12.5 *** outcomes reveal that the computer-aided detection(CAD)program outperforms an expert *** paired with the classifications reported in the literature,the provided system achieves results that are comparable or even *** have assessed and scheduled this computerized approach for HC evaluation,which includes information from all trimesters of gestation.

关键词： Fetal growth segmentation ultrasound images computer-aided detection gestational age crown-rump length head circumference

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Improved computer-aided detection of pulmonary nodules via deep learning in the sinogram domain

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Visual Computing for Industry,Biomedicine,and Art 2019年第1期2卷 129-137页

作者： Yongfeng Gao Jiaxing Tan Zhengrong Liang Lihong Li Yumei Huo Department of Radiology State University of New YorkStony BrookNY 11794USA Departments of computer Science City University of New York/CSIStaten IslandNY 10314USA Engineering and Environmental Science City University of New York/CSIStaten IslandNY 10314USA

computer aided detection(CADe)of pulmonary nodules plays an important role in assisting radiologists’diagnosis and alleviating interpretation burden for lung *** CADe systems,aiming at simulating radiologists’examination procedure,are built upon computer tomography(CT)images with feature extraction for detection and *** visual perception in CT image is reconstructed from sinogram,which is the original raw data acquired from CT *** this work,different from the conventional image based CADe system,we propose a novel sinogram based CADe system in which the full projection information is used to explore additional effective features of nodules in the sinogram *** the challenges of limited research in this concept and unknown effective features in the sinogram domain,we design a new CADe system that utilizes the self-learning power of the convolutional neural network to learn and extract effective features from *** proposed system was validated on 208 patient cases from the publicly available online Lung Image Database Consortium database,with each case having at least one juxtapleural nodule *** results demonstrated that our proposed method obtained a value of 0.91 of the area under the curve(AUC)of receiver operating characteristic based on sinogram alone,comparing to 0.89 based on CT image ***,a combination of sinogram and CT image could further improve the value of AUC to *** study indicates that pulmonary nodule detection in the sinogram domain is feasible with deep learning.

关键词： computer-aided detection Computed tomography Deep learning Lung Sinogram

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Colonic polyps： application value of computer-aided detection in computed tomographic colonography

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Chinese Medical Journal 2011年第3期124卷 380-384页

作者： ZHANG Hui-mao GUO Wei LIU Gui-feng AN Dong-hong GAO Shuo-hui SUN Li-bo YANG Hai-shan Department of Radiology China-Japan Union Hospital Jilin UniversityChangchun Jilin 130000 China Department of Gastrointestinal Surgery China-Japan Union Hospital Jilin UniversityChangchun Jilin 130000 China

Background Colonic polyps are frequently encountered in clinics. Computed tomographic colonography （CTC）, as a painless and quick detection, has high values in clinics. In this study, we evaluated the application value of computer-aided detection （CAD） in CTC detection of colonic polyps in the Chinese *** CTC was performed with a GE 64-row multidetector computed tomography （MDCT） scanner. Data of 50 CTC patients （39 patients positive for at least one polyp of ≥0.5 cm in size and the other 11 patients negative by endoscopic detection） were retrospectively reviewed first without computer-aided detection （CAD） and then with CAD by four radiologists （two were experienced and another two inexperienced） blinded to colonoscopy findings. The sensitivity,specificity, positive predictive value, negative predictive value, and accuracy of detected colonic polyps, as well as the areas under the ROC curves （Az value） with and without CAD were *** CAD increased the overall sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the colonic polyps detected by experienced and inexperienced readers. The sensitivity in detecting small polyps （5-9 mm） with CAD in experienced and inexperienced readers increased from 82% and 44% to 93% and 82%,respectively （P 〉0.05 and P 〈0.001）. With the use of CAD, the overall false positive rate and false negative rate for the detection of polyps by experienced and inexperienced readers decreased in different degrees. Among 13 sessile polyps not detected by CAD, two were 〉1.0 cm, eleven were 5-9 mm in diameter, and nine were fiat-shaped *** The application of CAD in combination with CTC can increase the ability to detect colonic polyps,particularly for inexperienced readers. However, CAD is of limited value for the detection of flat polyps.

关键词： computed tomography colonography computer-aided detection

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Artificial intelligence empowers the second-observer strategy for colonoscopy:a randomized clinical trial

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Gastroenterology Report 2023年第1期11卷 216-223页

作者： Pu Wang Xiao-Gang Liu Min Kang Xue Peng Mei-Ling Shu Guan-Yu Zhou Pei-Xi Liu Fei Xiong Ming-Ming Deng Hong-Fen Xia Jian-Jun Li Xiao-Qi Long Yan Song Liang-Ping Li Department of Gastroenterology Sichuan Provincial People’s HospitalUniversity of Electronic Science and Technology of ChinaChengduSichuanP.R.China Department of Gastroenterology The Affiliated Hospital of Southwest Medical UniversityLuzhouSichuanP.R.China Department of Gastroenterology Xinqiao HospitalThird Military Medical UniversityChongqingP.R.China Department of Gastroenterology Suining Central HospitalSuiningSichuanP.R.China

Background:In colonoscopy screening for colorectal cancer,human vision limitationsmay lead to highermiss rate of lesions;artificial intelligence(AI)assistance has been demonstrated to improve polyp ***,there still lacks direct evidence to demonstrate whether AI is superior to trainees or experienced nurses as a second observer to increase adenoma detection during *** this study,we aimed to compare the effectiveness of assistance fromAI and human observer during ***:A prospective multicenter randomized study was conducted from 2 September 2019 to 29 May 2020 at four endoscopy centers in *** patients were randomized to either computer-aided detection(CADe)-assisted group or observer-assisted *** primary outcome was adenoma per colonoscopy(APC).Secondary outcomes included polyp per colonoscopy(PPC),adenoma detection rate(ADR),and polyp detection rate(PDR).We compared continuous variables and categorical variables by using R studio(version 3.4.4).Results:A total of 1,261(636 in the CADe-assisted group and 625 in the observer-assisted group)eligible patients were ***(0.42 vs 0.35,P=0.034),PPC(1.13 vs 0.81,Pdetections of the CADe system were lower than those of the human observer(122 vs 191,P<0.001).Conclusions:Compared with the human observer,the CADe system may improve the clinical outcome of colonoscopy and reduce disturbance to routine practice(*** No.:ChiCTR1900025235).

关键词： artificial intelligence colon cancer screening adenoma early detection computer-aided detection

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Breast Calcifications and Histopathological Analysis on Tumour detection by CNN

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computer Systems Science & Engineering 2023年第1期44卷 595-612页

作者： D.Banumathy Osamah Ibrahim Khalaf Carlos Andrés Tavera Romero P.Vishnu Raja Dilip Kumar Sharma Department of computer Science and Engineering Paavai Engineering CollegePachalNamakkal637018Tamil NaduIndia Al-Nahrain University Al-Nahrain Nanorenewable Energy Research CenterBaghdadIraq COMBA R&D Laboratory Faculty of EngineeringUniversidad Santiago de Cali76001CaliColombia Department of computer Science and Engineering Kongu Engineering CollegePerundurai638060Tamil NaduIndia Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226Madhya PradeshIndia

The most salient argument that needs to be addressed universally is Early Breast Cancer detection(EBCD),which helps people live longer *** computer-aided detection(CADs)/computer-aided Diagnosis(CADx)sys-tem is indeed a software automation tool developed to assist the health profes-sions in Breast Cancer detection and Diagnosis(BCDD)and minimise mortality by the use of medical histopathological image classification in much less *** paper purposes of examining the accuracy of the Convolutional Neural Network(CNN),which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early iden-tification of breast cell malignancies formation of masses and Breast microcalci-fications on the *** we have insufficient data for a new domain that is desired to be handled by a pre-trained Convolutional Neural Network of Residual Network(ResNet50)for Breast Cancer detection and Diagnosis,to obtain the Discriminative Localization,Convolutional Neural Network with Class Activation Map(CAM)has also been used to perform breast microcalcifications detection tofind a specific class in the Histopathological *** test results indicate that this method performed almost 225.15%better at determining the exact location of disease(Discriminative Localization)through breast microcalci-fications ***50 seems to have the highest level of accuracy for images of Benign Tumour(BT)/Malignant Tumour(MT)cases at 97.11%.ResNet50’s average accuracy for pre-trained Convolutional Neural Network is 94.17%.

关键词： computer-aided detection breast cancer detection convolutional neural network class activation map computer-aided diagnosis

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Comparative study on artificial intelligence systems for detecting early esophageal squamous cell carcinoma between narrow-band and white-light imaging

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World Journal of Gastroenterology 2021年第3期27卷 281-293页

作者： Bing Li Shi-Lun Cai Wei-Min Tan Ji-Chun Li Ayimukedisi Yalikong Xiao-Shuang Feng Hon-Ho Yu Pin-Xiang Lu Zhen Feng Li-Qing Yao Ping-Hong Zhou Bo Yan Yun-Shi Zhong Department of Endoscopy Center Zhongshan Hospital of Fudan UniversityShanghai 200032China School of computer Science Fudan UniversityShanghai 200433China Clinical Statistical Center Shanghai Cancer Center of Fudan UniversityShanghai 200032China Department of Gastroenterology Kiang Wu HospitalMacao SAR 999078China Department of Endoscopy Center Xuhui HospitalZhongshan Hospital of Fudan UniversityShanghai 200031China

BACKGROUND Non-magnifying endoscopy with narrow-band imaging(NM-NBI)has been frequently used in routine screening of esophagus squamous cell carcinoma(ESCC).The performance of NBI for screening of early ESCC is,however,significantly affected by operator *** intelligence may be a unique approach to compensate for the lack of operator *** To construct a computer-aided detection(CAD)system for application in NMNBI to identify early ESCC and to compare it with our previously reported CAD system with endoscopic white-light imaging(WLI).METHODS A total of 2167 abnormal NM-NBI images of early ESCC and 2568 normal images were collected from three institutions(Zhongshan Hospital of Fudan University,Xuhui Hospital,and Kiang Wu Hospital)as the training dataset,and 316 pairs of images,each pair including images obtained by WLI and NBI(same part),were collected for *** endoscopists participated in this study to review the validation images with or without the assistance of the CAD *** diagnostic results of the two CAD systems and improvement in diagnostic efficacy of endoscopists were compared in terms of sensitivity,specificity,accuracy,positive predictive value,and negative predictive *** The area under receiver operating characteristic curve for CAD-NBI was *** the validation dataset,the sensitivity,specificity,accuracy,positive predictive value,and negative predictive value of CAD-NBI were 91.0%,96.7%,94.3%,95.3%,and 93.6%,respectively,while those of CAD-WLI were 98.5%,83.1%,89.5%,80.8%,and 98.7%,***-NBI showed superior accuracy and specificity than CAD-WLI(P=0.028 and P≤0.001,respectively),while CAD-WLI had higher sensitivity than CAD-NBI(P=0.006).By using both CAD-WLI and CAD-NBI,the endoscopists could improve their diagnostic efficacy to the highest level,with accuracy,sensitivity,and specificity of 94.9%,92.4%,and 96.7%,*** The CAD-NBI system for screening early ESCC has

关键词： computer-aided detection Esophageal squamous cell carcinoma Endoscopy Screening Narrow-band imaging White-light imaging

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Scoping out the future:The application of artificial intelligence to gastrointestinal endoscopy

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World Journal of Gastrointestinal Oncology 2022年第5期14卷 989-1001页

作者： Scott B Minchenberg Trent Walradt Jeremy R Glissen Brown Department of Internal Medicine Beth Israel Deaconess Medical CenterBostonMA 02130United States Division of Gastroenterology Beth Israel Deaconess Medical CenterBostonMA 02130United States

Artificial intelligence(AI)is a quickly expanding field in gastrointestinal *** there are a myriad of applications of AI ranging from identification of bleeding to predicting outcomes in patients with inflammatory bowel disease,a great deal of research has focused on the identification and classification of gastrointestinal *** of the initial randomized,prospective trials utilizing AI in clinical medicine have centered on polyp detection during screening *** addition to work focused on colorectal cancer,AI systems have also been applied to gastric,esophageal,pancreatic,and liver *** promising results in initial studies,the generalizability of most of these AI systems have not yet been *** this article we review recent developments in the field of AI applied to gastrointestinal oncology.

关键词： Artificial intelligence Oncology Gastroenterology Endoscopy Machine learning computer-assisted decision making computer-aided detection computer-aided diagnosis

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Artificial intelligence in colonoscopy

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World Journal of Gastroenterology 2021年第29期27卷 4802-4817页

作者： Joel Joseph Ella Marie LePage Catherine Phillips Cheney Rishi Pawa Department of Internal Medicine Wake Forest Baptist Medical CenterWinston SalemNC 27157United States Department of Internal Medicine Wake Forest School of MedicineWinston SalemNC 27157United States Department of Internal Medicine Section of Gastroenterology and HepatologyWake Forest Baptist Medical CenterWinston-SalemNC 27157United States

Colorectal cancer remains a leading cause of morbidity and mortality in the United *** in artificial intelligence(AI),specifically computer aided detection and computer-aided diagnosis offer promising methods of increasing adenoma detection rates with the goal of removing more pre-cancerous ***,these methods also may allow for smaller non-cancerous lesions to be diagnosed in vivo and left in place,decreasing the risks that come with unnecessary *** review will provide an overview of current advances in the use of AI in colonoscopy to aid in polyp detection and characterization as well as areas of developing research.

关键词： Colonoscopy Artificial intelligence computer-aided detection detection Characterization computer-aided diagnosis

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Human-artificial intelligence interaction in gastrointestinal endoscopy

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World Journal of Gastrointestinal Endoscopy 2024年第3期16卷 126-135页

作者： John R Campion Donal B O'Connor Conor Lahiff Department of Gastroenterology Mater Misericordiae University HospitalDublin D07 AX57Ireland School of Medicine University College DublinDublin D04 C7X2Ireland Department of Surgery Trinity College DublinDublin D02 R590Ireland

The number and variety of applications of artificial intelligence(AI)in gastr-ointestinal(GI)endoscopy is growing *** technologies based on machine learning(ML)and convolutional neural networks(CNNs)are at various stages of development and deployment to assist patients and endoscopists in preparing for endoscopic procedures,in detection,diagnosis and classification of pathology during endoscopy and in confirmation of key performance *** based on ML and CNNs require regulatory approval as medical *** between humans and the technologies we use are complex and are influenced by design,behavioural and psychological *** to the substantial differences between AI and prior technologies,important differences may be expected in how we interact with advice from AI ***-AI interaction(HAII)may be optimised by developing AI algorithms to minimise false positives and designing platform interfaces to maximise *** factors influencing HAII may include automation bias,alarm fatigue,algorithm aversion,learning effect and *** of these areas merits further study in the specific setting of AI applications in GI endoscopy and professional societies should engage to ensure that sufficient emphasis is placed on human-centred design in development of new AI technologies.

关键词： Artificial intelligence Machine learning Human factors computer-aided detection Colonoscopy Adenoma detection rate

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Artificial intelligence in gastroenterology:A narrative review

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Artificial Intelligence in Gastroenterology 2022年第5期3卷 117-141页

作者： Jonathan S Galati Robert J Duve Matthew O'Mara Seth A Gross Department of Medicine NYU Langone HealthNew YorkNY 10016United States Department of Internal Medicine Jacobs School of Medicine and Biomedical SciencesUniversity at BuffaloBuffaloNY 14203United States Division of Gastroenterology NYU Langone HealthNew YorkNY 10016United States

Artificial intelligence(AI)is a complex concept,broadly defined in medicine as the development of computer systems to perform tasks that require human *** has the capacity to revolutionize medicine by increasing efficiency,expediting data and image analysis and identifying patterns,trends and associations in large *** gastroenterology,recent research efforts have focused on using AI in esophagogastroduodenoscopy,wireless capsule endoscopy(WCE)and colonoscopy to assist in diagnosis,disease monitoring,lesion detection and therapeutic *** main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy,WCE and colonoscopy.

关键词： Artificial intelligence Colonoscopy computer-aided detection Deep learning Endoscopy Machine learning

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