| 词条 | Computer-aided simple triage |
| 释义 |
| name = Computer-aided simple triage | image = | alt = | caption = | pronounce = | purpose =computer systems that assist in initial interpretation of image | test of = | based on = | synonyms = | reference_range = | calculator = | DiseasesDB = | ICD10 = | ICD9 = | ICDO = | MedlinePlus = | eMedicine = | MeshID = | OPS301 = | LOINC = }}Computer-aided simple triage (CAST) are computerized methods or systems that assist physicians in initial interpretation and classification of medical images. CAST is a sub-class of computer-aided diagnosis (CAD). CAST software systems perform a fully automatic initial triage (classification) of diagnostic medical imaging studies. CAST is primarily intended for emergency diagnostic imaging, where a prompt diagnosis of critical, life-threatening condition is required.[1] OverviewComputer-aided simple triage (CAST) is a combination of computer-aided diagnosis (CAD) and simple triage and rapid treatment (START). CAST performs a fully automatic initial interpretation of a study – a "wet read". Studies are automatically classified into some meaningful categories, e.g. positive/negative, critical/minor/normal, difficult/simple/non-diagnostic, etc.[1] CAST is primarily intended for emergency diagnostic imaging. Unlike traditional CAD, mainly used to detect malignant lesions, CAST deals with acute, life-threatening conditions, when a prompt diagnosis is time critical. While the primary goal of the traditional CAD is improving the diagnostic accuracy of a human reader, the CAST addresses two other problems:
Use case scenarioAs with the traditional CAD, CAST does not substitute the physician. It only alerts about the possibility of acute, critical condition, or suggests that the study is free of severe disease. In both cases, the diagnosis should be verified by a trained physician. The clinical benefit is achieved:
Traditional CAD system usually plays the role of a "second reader" and is used after or during the interpretation performed by physician. CAST, on the other hand, analyzes the study before the physician, in a background, fully automatic mode. By the time physician comes to read the study, the initial triage or "wet read" prepared by CAST is already available. CAST system can send a message to a physician to report an urgent case requiring immediate attention. Sensitivity and specificityLike any CAD system, CAST, in general, cannot guarantee 100% diagnostic accuracy. Since CAST operates in a fully automated mode, the system is expected to exhibit very high sensitivity – usually above 90%. Moreover, the need to provide a diagnosis at "per study" level dictates stringent requirements for CAST specificity as well. The average of one or more false alarms per study, tolerable for a traditional CAD, is not acceptable for CAST, as almost every study would be reported as positive. Therefore, for most clinical applications, CAST specificity should be higher than 60-70% to make it useful. Reliability and quality controlSince CAST operates in a fully automatic mode, it should be able to deal with any study, regardless of image quality, patient anatomy, etc. Therefore, CAST systems should implement a quality control mechanism to ensure the high confidence level of the diagnosis. If the system decides (based on the evaluated image quality, detected artifacts, anatomical anomalies, etc.) that no reliable diagnosis can be automatically achieved, it reports a failure. Clinical applicationsCAST approach is applicable for the automatic detection of acute, life-threatening conditions from diagnostic medical images, such as:
CAST system can analyze images acquired with various modalities, including x-ray, CT, MRI, ultrasound and others. ExamplesCAST for coronary artery diseaseCAST system is available for the detection of significant (>50%) coronary stenosis in coronary CT angiography (cCTA) studies. The system exhibits "per study" specificity of 60–70%, while keeping the sensitivity above 90%.[3][4][5][6][7] It can be used for chest pain patient triage in emergency room. CAST for Intracranial HemorrhagesA deep learning system is available for automatic detection of Intracranial Hemorrhages in acute care settings [8]. References1. ^1 Goldenberg R, Peled N., Computer-aided simple triage, Int J Comput Assist Radiol Surg., 6(5):705-11, Sep 2011, {{PMID|21499779}} 2. ^{{cite journal|last1=Echegaray|first1=Sebastian|last2=Zamora|first2=Gilberto|last3=Yu|first3=Honggang|last4=Luo|first4=Wenbin|last5=Soliz|first5=Peter|last6=Kardon|first6=Randy|title=Automated Analysis of Optic Nerve Images for Detection and Staging of Papilledema|journal=Investigative Ophthalmology & Visual Science|date=27 September 2011|volume=52|issue=10|pages=7470|doi=10.1167/iovs.11-7484|url=http://iovs.arvojournals.org/article.aspx?articleid=2165672}} 3. ^E. Arnoldi, M. Gebregziabher, U. J. Schoepf, R. Goldenberg, L. Ramos-Duran, P. L. Zwerner, K. Nikolaou, M. F. Reiser, P. Costello and C. Thilo, Automated computer-aided stenosis detection at coronary CT angiography: initial experience, European Radiology, 20(5):1160-7, May 2010, {{PMID|19890640}} 4. ^E. J. Halpern, D. J. Halpern, Diagnosis of coronary stenosis with CT angiography: comparison of automated computer diagnosis with expert readings, Academic Radiology, 18(3):324-33, Mar 2011, {{PMID|21215663}} 5. ^Kang KW, Chang HJ, Shim H, Kim YJ, Choi BW, Yang WI, Shim JY, Ha J, Chung N., Feasibility of an automatic computer-assisted algorithm for the detection of significant coronary artery disease in patients presenting with acute chest pain., Eur J Radiol, 81(4):e640-6, Apr 2012, {{PMID|22304980}} 6. ^Goldenberg R, Eilot D, Begelman G, Walach E, Ben-Ishai E, Peled N., Computer-aided simple triage (CAST) for coronary CT angiography (CCTA), Int J Comput Assist Radiol Surg., Apr 2012, {{PMID|22484719}} 7. ^Meyer M, Schoepf UJ, Fink C, Goldenberg R, Apfaltrer P, Gruettner J, Vajcs D, Schoenberg SO, Henzler T, Diagnostic Performance Evaluation of a Computer-Aided Simple Triage System for Coronary CT Angiography in Patients with Intermediate Risk for Acute Coronary Syndrome, Academic Radiology, Aug 2013, {{PMID|23735619}} 8. ^Zebra Medical Vision Announces CE Approval of Its Newest AI Imaging Algorithm - Automatic Identification of Brain Bleeds[https://www.businesswire.com/news/home/20180305005693/en/Zebra-Medical-Vision-Announces-CE-Approval-Newest] 3 : Medical expert systems|Radiology|Health informatics |
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