Automatized Detection of Crohn's Disease in Intestinal Ultrasound Using Convolutional Neural Network.
Introduction The use of intestinal ultrasound (IUS) for the diagnosis and follow-up of inflammatory bowel disease is steadily growing. Although access to educational platforms of IUS is feasible, novice ultrasound operators lack experience in performing and interpreting IUS. An artificial intelligen...
| Publicado en: | Inflammatory Bowel Diseases Vol. 29; no. 12; pp. 1901 - 1907 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Dec2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=174444688&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174444688 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10780998 N0V jtl: Inflammatory Bowel Diseases issn: 10780998 maglogo: N pubinfo: dt: Dec2023 vid: 29 iid: 12 pid: 622 pub: Oxford University Press / USA artinfo: ui: 174444688 174444688 174444688 10.1093/ibd/izad014 174444688 ppf: 1901 ppct: 6 formats: tig: atl: Automatized Detection of Crohn's Disease in Intestinal Ultrasound Using Convolutional Neural Network. aug: au: Carter, Dan Albshesh, Ahmed Shimon, Carmi Segal, Batel Yershov, Alex Kopylov, Uri Meyers, Adele Brzezinski, Rafael Y Horin, Shomron Ben Hoffer, Oshrit affil: Institute of Gastroenterology, Chaim Sheba Medical Center , Ramat Gan , Israel sug: subj: Crohn Disease Diagnosis Automation Methods Intestines Ultrasonography Neural Networks (Computer) Utilization Human Artificial Intelligence Inflammation Diagnosis Image Interpretation, Computer Assisted Methods Sensitivity and Specificity Descriptive Statistics ROC Curve ab: Introduction The use of intestinal ultrasound (IUS) for the diagnosis and follow-up of inflammatory bowel disease is steadily growing. Although access to educational platforms of IUS is feasible, novice ultrasound operators lack experience in performing and interpreting IUS. An artificial intelligence (AI)–based operator supporting system that automatically detects bowel wall inflammation may simplify the use of IUS by less experienced operators. Our aim was to develop and validate an artificial intelligence module that can distinguish bowel wall thickening (a surrogate of bowel inflammation) from normal bowel images of IUS. Methods We used a self-collected image data set to develop and validate a convolutional neural network module that can distinguish bowel wall thickening >3 mm (a surrogate of bowel inflammation) from normal bowel images of IUS. Results The data set consisted of 1008 images, distributed uniformly (50% normal images, 50% abnormal images). Execution of the training phase and the classification phase was performed using 805 and 203 images, respectively. The overall accuracy, sensitivity, and specificity for detection of bowel wall thickening were 90.1%, 86.4%, and 94%, respectively. The network exhibited an average area under the ROC curve of 0.9777 for this task. Conclusions We developed a machine-learning module based on a pretrained convolutional neural network that is highly accurate in the recognition of bowel wall thickening on intestinal ultrasound images in Crohn's disease. Incorporation of convolutional neural network to IUS may facilitate the use of IUS by inexperienced operators and allow automatized detection of bowel inflammation and standardization of IUS imaging interpretation. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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