Lens Identification to Prevent Radiation-Induced Cataracts Using Convolutional Neural Networks.
Exposure of the lenses to direct ionizing radiation during computed tomography (CT) examinations predisposes patients to cataract formation and should be avoided when possible. Avoiding such exposure requires positioning and other maneuvers by technologists that can be challenging. Continuous feedba...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 4; pp. 644 - 651 |
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| Autor principal: | |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2019
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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=137642041&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137642041 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2019 vid: 32 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137642041 137642041 137642041 10.1007/s10278-019-00242-y 137642041 ppf: 644 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Lens Identification to Prevent Radiation-Induced Cataracts Using Convolutional Neural Networks. aug: au: Filice, Ross affil: MedStar Georgetown University Hospital, 3800 Reservoir Road NW, CG201, 20007, Washington, DC, USA sug: subj: Cataract Neural Networks (Computer) Utilization Radiation Injuries Prevention and Control Lens, Crystalline Human Radiation Injuries Complications Feedback Radiologic Technologists Tomography, X-Ray Computed Quality of Health Care Patient Positioning Image Processing, Computer Assisted ab: Exposure of the lenses to direct ionizing radiation during computed tomography (CT) examinations predisposes patients to cataract formation and should be avoided when possible. Avoiding such exposure requires positioning and other maneuvers by technologists that can be challenging. Continuous feedback has been shown to sustain quality improvement and can remind and encourage technologists to comply with these methods. Previously, for use cases such as this, cumbersome manual techniques were required for such feedback. Modern deep learning methods utilizing convolutional neural networks (CNNs) can be used to develop models that can detect lenses in CT examinations. These models can then be used to facilitate automatic and continuous feedback to sustain technologist performance for this task, thus contributing to higher quality patient care. This continuous evaluation for quality purposes also surfaces other operational or process-based challenges that can be addressed. Given high-performance characteristics, these models could also be used for other tasks such as population health research. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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