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Computer-aided simple triage
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<h2 id="overview">Overview</h2> <p>Computer-aided simple triage (CAST) is a combination of <a href="/facts/Computer-aided_diagnosis/M88tS5ax">computer-aided diagnosis</a> (CAD) and <a href="/facts/Simple_triage_and_rapid_treatment/CjbK56f0">simple triage and rapid treatment</a> (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.<a class="footnote-ref" id="fnref:2" href="#fn:2"><sup>2</sup></a> </p><p>CAST is primarily intended for <a href="/facts/Emergency_medicine/HmVeC5R9">emergency</a> 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 <a href="/facts/Computer-aided_diagnosis/M88tS5ax">CAD</a> is improving the diagnostic accuracy of a human reader, the CAST addresses two other problems: </p> <ul><li>Low availability of image interpreters – no reader immediately available to read an urgent study – off hours, rural places, under-staffing</li> <li>Reading sequence prioritization – given a large number of studies to read, establish the optimal reading sequence, e.g. urgent cases first; or, given several available readers, distribute the workload in the optimal way, e.g. simple cases to trainees, difficult to experts.</li></ul> <h2 id="use-case-scenario">Use case scenario</h2> <p>As 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: </p> <ul><li>for positive cases – by bringing expert's attention to critical cases faster than it would happen without CAST</li> <li>for negative cases – by letting less experienced staff be the first to deal with simple negative cases, thus releasing pressure from less available experts.</li></ul> <p>Traditional CAD system usually plays the role of a "second reader" and is used <i>after or during</i> the interpretation performed by physician. CAST, on the other hand, analyzes the study <i>before</i> 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. </p> <h2 id="sensitivity-and-specificity">Sensitivity and specificity</h2> <p>Like 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 <a href="/facts/Sensitivity_and_specificity/pzc9qfdz">sensitivity</a> – usually above 90%. Moreover, the need to provide a diagnosis at "per study" level dictates stringent requirements for CAST <a href="/facts/Sensitivity_and_specificity/pzc9qfdz">specificity</a> as well. The average of one or more <a href="/facts/False_alarms/5T2YYusw">false alarms</a> 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. </p> <h2 id="reliability-and-quality-control">Reliability and quality control</h2> <p>Since 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 <a href="/facts/Digital_artifact/AgqnmFVc">artifacts</a>, anatomical anomalies, etc.) that no reliable diagnosis can be automatically achieved, it reports a failure. </p> <h2 id="clinical-applications">Clinical applications</h2> <p>CAST approach is applicable for the automatic detection of acute, life-threatening conditions from diagnostic medical images, such as: </p> <ul><li><a href="/facts/Pulmonary_embolism/LPjKCPba">Pulmonary embolism (PE)</a></li> <li><a href="/facts/Aortic_dissection/xsA2v4Vp">Aortic dissection</a></li> <li><a href="/facts/Coronary_artery_disease/szCt0nVx">Coronary artery disease</a></li> <li><a href="/facts/Stroke/DwpjdzY0">Stroke</a></li> <li><a href="/facts/Bone_fractures/faEv3Ve5">Bone fractures</a></li> <li><a href="/facts/Internal_bleeding/3cj5g8YQ">Internal bleeding</a></li> <li><a href="/facts/Pneumoperitoneum/k9eN6ps5">Pneumoperitoneum</a></li> <li><a href="/facts/Idiopathic_intracranial_hypertension/dGYKYw19">Idiopathic intracranial hypertension</a><a class="footnote-ref" id="fnref:3" href="#fn:3"><sup>3</sup></a></li></ul> <p>CAST system can analyze images acquired with various modalities, including <a href="/facts/Medical_radiography/ab1bvq3q">x-ray</a>, <a href="/facts/X-ray_computed_tomography/NusPJ2S8">CT</a>, <a href="/facts/MRI/Vh9yG9qq">MRI</a>, <a href="/facts/Ultrasonography/31Pa5IH9">ultrasound</a> and others. </p> <h2 id="examples">Examples</h2> <h3>CAST for coronary artery disease</h3> <p>CAST system is available for the detection of significant (>50%) <a href="/facts/Coronary_circulation/SJhPVTgM">coronary</a> <a href="/facts/Stenosis/X0x2tAk7">stenosis</a> in coronary <a href="/facts/CT_angiography/3lsmfSWt">CT angiography</a> (cCTA) studies. The system exhibits "per study" <a href="/facts/Sensitivity_and_specificity/pzc9qfdz">specificity</a> of 60–70%, while keeping the <a href="/facts/Sensitivity_and_specificity/pzc9qfdz">sensitivity</a> above 90%.<a class="footnote-ref" id="fnref:4" href="#fn:4"><sup>4</sup></a><a class="footnote-ref" id="fnref:5" href="#fn:5"><sup>5</sup></a><a class="footnote-ref" id="fnref:6" href="#fn:6"><sup>6</sup></a><a class="footnote-ref" id="fnref:7" href="#fn:7"><sup>7</sup></a><a class="footnote-ref" id="fnref:8" href="#fn:8"><sup>8</sup></a> It can be used for chest pain patient triage in emergency room. </p> <h3>CAST for Intracranial Hemorrhages</h3> <p>A deep learning system is available for automatic detection of Intracranial Hemorrhages in acute care settings.<a class="footnote-ref" id="fnref:9" href="#fn:9"><sup>9</sup></a> </p> <h2 id="see-also">See also</h2> <ul><li><a href="/facts/Aidoc/cvyc1zhk">Aidoc</a></li></ul> <h2 id="references">References</h2> <ol> <li id="fn:1"><p>Goldenberg, R; Peled, N (September 2011). "Computer-aided simple triage". Int J Comput Assist Radiol Surg. 6 (5): 705–11. doi:10.1007/s11548-011-0552-x. PMID 21499779. S2CID 19623223. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li> <li id="fn:2"><p>Goldenberg, R; Peled, N (September 2011). "Computer-aided simple triage". Int J Comput Assist Radiol Surg. 6 (5): 705–11. doi:10.1007/s11548-011-0552-x. PMID 21499779. S2CID 19623223. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li> <li id="fn:3"><p>Echegaray, Sebastian; Zamora, Gilberto; Yu, Honggang; Luo, Wenbin; Soliz, Peter; Kardon, Randy (27 September 2011). "Automated Analysis of Optic Nerve Images for Detection and Staging of Papilledema". Investigative Ophthalmology & Visual Science. 52 (10): 7470–8. doi:10.1167/iovs.11-7484. PMID 21862651. <a href="http://iovs.arvojournals.org/article.aspx?articleid=2165672" target="_blank">http://iovs.arvojournals.org/article.aspx?articleid=2165672</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></p></li> <li id="fn:4"><p>Arnoldi, E; Gebregziabher, M; Schoepf, UJ; et al. (May 2010). "Automated computer-aided stenosis detection at coronary CT angiography: initial experience". Eur Radiol. 20 (5): 1160–7. doi:10.1007/s00330-009-1644-7. PMID 19890640. S2CID 24454070. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:4" class="footnote-back-ref">↩</a></p></li> <li id="fn:5"><p>Halpern, EJ; Halpern, DJ (March 2011). "Diagnosis of coronary stenosis with CT angiography comparison of automated computer diagnosis with expert readings". Acad Radiol. 18 (3): 324–33. doi:10.1016/j.acra.2010.10.014. PMID 21215663. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:5" class="footnote-back-ref">↩</a></p></li> <li id="fn:6"><p>Kang, KW; Chang, HJ; Shim, H; et al. (April 2012). "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. doi:10.1016/j.ejrad.2012.01.017. PMID 22304980. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:6" class="footnote-back-ref">↩</a></p></li> <li id="fn:7"><p>Goldenberg, R; Eilot, D; Begelman, G; Walach, E; Ben-Ishai, E; Peled, N (November 2012). "Computer-aided simple triage (CAST) for coronary CT angiography (CCTA)". Int J Comput Assist Radiol Surg. 7 (6): 819–27. doi:10.1007/s11548-012-0684-7. PMID 22484719. S2CID 5627031. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:7" class="footnote-back-ref">↩</a></p></li> <li id="fn:8"><p>Meyer, M; Schoepf, UJ; Fink, C; Goldenberg, R; Apfaltrer, P; Gruettner, J; Vajcs, D; Schoenberg, SO; Henzler, T (2013). "Diagnostic performance evaluation of a computer-aided simple triage system for coronary CT angiography in patients with intermediate risk for acute coronary syndrome". Acad Radiol. 20 (8): 980–6. doi:10.1016/j.acra.2013.02.014. PMID 23735619. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:8" class="footnote-back-ref">↩</a></p></li> <li id="fn:9"><p>Zebra Medical Vision Announces CE Approval of Its Newest AI Imaging Algorithm - Automatic Identification of Brain Bleeds [1] <a href="https://www.businesswire.com/news/home/20180305005693/en/Zebra-Medical-Vision-Announces-CE-Approval-Newest" target="_blank">https://www.businesswire.com/news/home/20180305005693/en/Zebra-Medical-Vision-Announces-CE-Approval-Newest</a> <a href="#fnref:9" class="footnote-back-ref">↩</a></p></li> </ol>