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'''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 medicine|emergency]] [[Medical imaging|diagnostic imaging]], where a prompt [[Medical diagnosis|diagnosis]] of critical, life-threatening condition is required.<ref name="CAST">Goldenberg R, Peled N., Computer-aided simple triage, Int J Comput Assist Radiol Surg., 6(5):705-11, Sep 2011, PMID 21499779</ref>

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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|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 medicine|emergency]] [[Medical imaging|diagnostic imaging]], where a prompt [[Medical diagnosis|diagnosis]] of critical, life-threatening condition is required.<ref name="CAST">Goldenberg R, Peled N., Computer-aided simple triage, Int J Comput Assist Radiol Surg., 6(5):705-11, Sep 2011, PMID 21499779</ref>


==Overview==
==Overview==
Computer-aided Simple Triage (CAST) is a combination of [[Computer-aided diagnosis|Computer-Aided Diagnosis]] (CAD) and [[Simple Triage and Rapid Treatment]] (START).
Computer-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.<ref name="CAST" />
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.<ref name="CAST" />


CAST is primarily intended for [[emergency medicine|emergency]] diagnostic imaging. Unlike traditional [[Computer-aided diagnosis|CAD]], mainly used to detect malignant lesions, CAST deals with acute, life-threatening conditions, when a prompt diagnosis is time
CAST is primarily intended for [[emergency medicine|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 [[Computer-aided diagnosis|CAD]] is improving the diagnostic accuracy of a human reader, the CAST addresses two other problems:
critical. While the primary goal of the traditional [[Computer-aided diagnosis|CAD]] is improving the diagnostic accuracy of a human reader, the CAST addresses two other problems:
* Low availability of image interpreters - no reader immediately available to read an urgent study - off hours, rural places, under-staffing
* Low availability of image interpreters – no reader immediately available to read an urgent study – off hours, rural places, under-staffing
* 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.
* 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.


==Use case scenario==
==Use case scenario==
As with the traditional [[Computer-aided diagnosis|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.
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:
The clinical benefit is achieved:
* for positive cases - by bringing expert’s attention to critical cases faster than it would happen without CAST
* for positive cases – by bringing expert's attention to critical cases faster than it would happen without CAST
* for negative cases - by letting less experienced staff be the first to deal with simple negative cases, thus releasing pressure from less available experts.
* for negative cases – by letting less experienced staff be the first to deal with simple negative cases, thus releasing pressure from less available experts.


Traditional [[Computer-aided diagnosis|CAD]] system usually plays the role of a "second reader" and is used ''after or during'' the interpretation
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.
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 specificity==
==Sensitivity and specificity==
Like any [[Computer-aided diagnosis|CAD]] system, CAST, in general, cannot guarantee 100% diagnostic accuracy.
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 [[Sensitivity and specificity|sensitivity]] - usually above 90%. Moreover, the need to provide a diagnosis at "per study" level dictates stringent requirements for CAST [[Sensitivity and specificity|specificity]] as well. The average of one or more [[false alarms]] per study, tolerable for a traditional [[Computer-aided diagnosis|CAD]], is not acceptable for CAST, as almost every study would be reported as positive. Therefore, for most clinical applications, CAST [[Sensitivity and specificity|specificity]] should be higher than 60-70% to make it useful.
Since CAST operates in a fully automated mode, the system is expected to exhibit very high [[Sensitivity and specificity|sensitivity]] – usually above 90%. Moreover, the need to provide a diagnosis at "per study" level dictates stringent requirements for CAST [[Sensitivity and specificity|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 control==
==Reliability and quality control==
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===CAST for coronary artery disease===
===CAST for coronary artery disease===
CAST system is available for the detection of significant (>50%) [[Coronary circulation|coronary]] [[stenosis]] in coronary [[CT angiography]] (cCTA) studies. The system exhibits "per study" [[Sensitivity and specificity|specificity]] of 60-70%, while keeping the [[Sensitivity and specificity|sensitivity]] above 90% <ref>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</ref>
CAST system is available for the detection of significant (>50%) [[Coronary circulation|coronary]] [[stenosis]] in coronary [[CT angiography]] (cCTA) studies. The system exhibits "per study" [[Sensitivity and specificity|specificity]] of 60–70%, while keeping the [[Sensitivity and specificity|sensitivity]] above 90%.<ref>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</ref><ref>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</ref><ref>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</ref><ref>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</ref><ref>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</ref>
<ref>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</ref>
<ref>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</ref>
<ref>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</ref>
<ref>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</ref>
It can be used for chest pain patient triage in emergency room.
It can be used for chest pain patient triage in emergency room.


Revision as of 00:33, 2 February 2017

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]

Overview

Computer-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:

  • Low availability of image interpreters – no reader immediately available to read an urgent study – off hours, rural places, under-staffing
  • 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.

Use case scenario

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:

  • for positive cases – by bringing expert's attention to critical cases faster than it would happen without CAST
  • for negative cases – by letting less experienced staff be the first to deal with simple negative cases, thus releasing pressure from less available experts.

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 specificity

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 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 control

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 artifacts, anatomical anomalies, etc.) that no reliable diagnosis can be automatically achieved, it reports a failure.

Clinical applications

CAST 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.

Examples

CAST for coronary artery disease

CAST 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.

References

  1. ^ a b Goldenberg R, Peled N., Computer-aided simple triage, Int J Comput Assist Radiol Surg., 6(5):705-11, Sep 2011, PMID 21499779
  2. ^ 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 Opthalmology & Visual Science. 52 (10): 7470. doi:10.1167/iovs.11-7484.
  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
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