Deep Learning for Automated Detection and Localization of Traumatic Abdominal Solid Organ Injuries on CT Scans.
Computed tomography (CT) is the most commonly used diagnostic modality for blunt abdominal trauma (BAT), significantly influencing management approaches. Deep learning models (DLMs) have shown great promise in enhancing various aspects of clinical practice. There is limited literature available on t...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 3; pp. 1113 - 1124 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2024
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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=178678178&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178678178 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2024 vid: 37 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178678178 178678178 178678178 10.1007/s10278-024-01038-5 178678178 ppf: 1113 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning for Automated Detection and Localization of Traumatic Abdominal Solid Organ Injuries on CT Scans. aug: au: Cheng, Chi-Tung Lin, Hou-Hsien Hsu, Chih-Po Chen, Huan-Wu Huang, Jen-Fu Hsieh, Chi-Hsun Fu, Chih-Yuan Chung, I-Fang Liao, Chien-Hung affil: Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan sug: subj: Deep Learning Algorithms Automation Tomography, X-Ray Computed Abdominal Injuries Health Personnel Human ROC Curve Sensitivity and Specificity Predictive Value of Tests Adult Middle Age Aged Female Male Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Female Male ab: Computed tomography (CT) is the most commonly used diagnostic modality for blunt abdominal trauma (BAT), significantly influencing management approaches. Deep learning models (DLMs) have shown great promise in enhancing various aspects of clinical practice. There is limited literature available on the use of DLMs specifically for trauma image evaluation. In this study, we developed a DLM aimed at detecting solid organ injuries to assist medical professionals in rapidly identifying life-threatening injuries. The study enrolled patients from a single trauma center who received abdominal CT scans between 2008 and 2017. Patients with spleen, liver, or kidney injury were categorized as the solid organ injury group, while others were considered negative cases. Only images acquired from the trauma center were enrolled. A subset of images acquired in the last year was designated as the test set, and the remaining images were utilized to train and validate the detection models. The performance of each model was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value based on the best Youden index operating point. The study developed the models using 1302 (87%) scans for training and tested them on 194 (13%) scans. The spleen injury model demonstrated an accuracy of 0.938 and a specificity of 0.952. The accuracy and specificity of the liver injury model were reported as 0.820 and 0.847, respectively. The kidney injury model showed an accuracy of 0.959 and a specificity of 0.989. We developed a DLM that can automate the detection of solid organ injuries by abdominal CT scans with acceptable diagnostic accuracy. It cannot replace the role of clinicians, but we can expect it to be a potential tool to accelerate the process of therapeutic decisions for trauma care. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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