Computer-aided simple triage

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Computer-aided simple triage
Purposecomputer systems that assist in initial interpretation of image

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]

Contents

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:

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:

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.

CAST for Intracranial Hemorrhages

A deep learning system is available for automatic detection of Intracranial Hemorrhages in acute care settings. [8]

See also

Related Research Articles

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Ronald Marc Summers is an American radiologist and senior investigator at the Diagnostic Radiology Department at the NIH Clinical Center in Bethesda, Maryland. He is chief of the Clinical Image Processing Service and directs the Imaging Biomarkers and Computer-Aided Diagnosis (CAD) Laboratory. A researcher in the field of radiology and computer-aided diagnosis, he has co-authored over 500 journal articles and conference proceedings papers and is a coinventor on 12 patents. His lab has conducted research applying artificial intelligence and deep learning to radiology.

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References

  1. 1 2 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.
  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 Ophthalmology & Visual Science. 52 (10): 7470–8. doi:10.1167/iovs.11-7484. PMID   21862651.
  3. ↑ 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.
  4. ↑ 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.
  5. ↑ 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.
  6. ↑ 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.
  7. ↑ 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.
  8. ↑ Zebra Medical Vision Announces CE Approval of Its Newest AI Imaging Algorithm - Automatic Identification of Brain Bleeds
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