Computer assistance for C.T. scan interpretation and cerebral disease diagnosis

Teather, D.; Morton, Blaine A.; Boulay, G. H. du; Wills, K. M.; Plummer, D.; Innocent, Peter R.

doi:10.1002/sim.4780040310

Cited by 15 publications

(10 citation statements)

References 6 publications

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“…The explanatory systems in this and other ES are of particular benefit as training tools by explaining the importance of findings to clinicians who do not hold the domain experience to understand the implicit knowledge inferred from the system's recommendations [15][16][17]. The importance of explanatory facilities in a rule-based ES was first expounded within the domain of neuroradiology by Teather et al [18] and du Boulay et al [19]. The BRAINS expert system was able to give recommendations concerning diagnosis and further imaging on the basis of statistical data with explanatory features ''of benefit to less experienced radiologists'' [18,19].…”

Section: Rule-based Expert Systemsmentioning

confidence: 99%

“…The importance of explanatory facilities in a rule-based ES was first expounded within the domain of neuroradiology by Teather et al [18] and du Boulay et al [19]. The BRAINS expert system was able to give recommendations concerning diagnosis and further imaging on the basis of statistical data with explanatory features ''of benefit to less experienced radiologists'' [18,19]. This system used language-based descriptive inputs from the reporting radiologist.…”

Section: Rule-based Expert Systemsmentioning

confidence: 99%

“…This system used language-based descriptive inputs from the reporting radiologist. Teather et al [18] identified certain features that were essential in providing a clinically acceptable system, features that still hold true today: Figure 1. The decision algorithms in modern decision support system (DSS) are commonly complex and designed with recognition that the available information might be incomplete or poorly defined.…”

Section: Rule-based Expert Systemsmentioning

confidence: 99%

“…BRAINS and MRI advisor [18][19][20][21] highlighted two important concepts. Firstly, DSS perform better with standardised descriptor inputs.…”

Section: Summary: Rule-based Expert Systemsmentioning

confidence: 99%

“…Physicians generally will not accept and act on the advice of a decision system without knowing the basis for the system's diagnosis [18,19,34]. However, recent work has enabled rule generation from neural networks, so this shortcoming might be overcome in the future [35].…”

Section: Summary: Neural Networkmentioning

confidence: 99%

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Decision support systems for clinical radiological practice — towards the next generation

Stivaros¹,

Gledson²,

Nenadić³

et al. 2010

BJR

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ABSTRACT. The huge amount of information that needs to be assimilated in order to keep pace with the continued advances in modern medical practice can form an insurmountable obstacle to the individual clinician. Within radiology, the recent development of quantitative imaging techniques, such as perfusion imaging, and the development of imaging-based biomarkers in modern therapeutic assessment has highlighted the need for computer systems to provide the radiological community with support for academic as well as clinical/translational applications. This article provides an overview of the underlying design and functionality of radiological decision support systems with examples tracing the development and evolution of such systems over the past 40 years. More importantly, we discuss the specific design, performance and usage characteristics that previous systems have highlighted as being necessary for clinical uptake and routine use. Additionally, we have identified particular failings in our current methodologies for data dissemination within the medical domain that must be overcome if the next generation of decision support systems is to be implemented successfully. What is a decision support system? Decision support systems (DSS) are a set of manual or computer-based tools that assist in some decision-making activity. In today's information-driven environment, DSS are commonly understood to be a variety of computerised information management systems, designed to help resolve complicated problems and/or questions by supporting the decision-making process. DSS are gaining increasing popularity in various domains including business, engineering, the military and medicine. These systems are especially valuable in situations where the amount of available information is prohibitive for the intuition of a human decision maker and where precision and optimal performance are of importance [1]. Medical decision support systemsThe huge amount of information that needs to be assimilated in order to keep pace with continued advances in modern medical practice can form an insurmountable obstacle to the individual clinician. In medical applications decision quality is of crucial importance, whilst human decision-making performance can be suboptimal and deteriorate as the complexity of the problem increases.For these reasons, the development of medical DSS is becoming increasingly important [1] and the routine uptake of these ''intelligent'' systems is becoming more common [2]. One of the earliest rule-based expert systems, DENDRAL [3], was implemented in the 1960s and was designed to provide support to organic chemists. This was further developed over the early 1970s by the same team at Stanford University into arguably the first rule-based medical DSS, MYCIN [4]. This system attempted to identify bacteria causing severe infections and recommend appropriate antibiotics. From these early DSS and the subsequent development of knowledge engineering, we now have DSS based on established architectures. Specific radiological cons...

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