Evaluation of computer advisor in the interpretation of CT images of the head

Teather, D.; Teather, B. A.; Wills, K. M.; Boulay, G. H. du; Plummer, D.; Isherwood, I.; Gholkar, A

doi:10.1007/bf00339692

Cited by 13 publications

(6 citation statements)

References 6 publications

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“…For a distribution of the tumour types derived from the Iiterature it is 85%, for our sample 82% were calcu lated. Despite the small number of patients this result is comparable to that of other authors [2,3], although a prac tically applicable system would have to cope with more than only 7 alternative diagnoses [3].…”

Section: Results and Conclusionsupporting

confidence: 75%

Classification of brain tumours by CT image Walsh spectra

et al. 1990

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Section: Results and Conclusionsupporting

confidence: 75%

Classification of brain tumours by CT image Walsh spectra

et al. 1990

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“…This finding was partly related to the fact that variations in the language used for radiological descriptors meant that users had difficulty entering image description data in a reliable, consistent and repeatable manner [20].…”

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%

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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“…A previous study in the area of CT brain scans led to the development of a structured description process for CT images and a menu-based computer advisor (BRAINS) to aid in image interpretation and cerebral disease diagnosis 2 • 14 • Diagnostic advice is based on a statistical comparison of the feature values of the image with the image descriptions of cases of known pathology entered by an experienced radiologist. An independent evaluation 15 identified the training benefits of a structured approach to reporting linked to a reference set of annotated example images and diagrams.…”

Section: Structured Description and Statistical Modelling For Diagnosismentioning

confidence: 99%

Statistical Modelling and Structured Image Description for Intelligent Tutoring in MR Imaging of the Head

Teather

Sharples

Jeffery

et al. 1994

Rivista di Neuroradiologia

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si sta sviluppando una ricerca in cooperazione per esaminare la fattibilita dell'uso di principi su base statistica e di linguaggio strutturato nella descrizione degli esami per l'insegnamento dell'interpretazione e diagnosi dei quadri radiologici.Questo lavoro descrive la costruzione di un sistema didattico sofisticato (ITS) per l'interpretazione di tomografia RM nella diagnosi delle malattie neurologiche cerebrali.L'efficacia del «tutore» dipende totalmente dall'esistenza di una banca dati costituita da esami interpretati ed analizzati e da un linguaggio di descrizione dei reperti che sono stati costruiti a Leicester e Londra. Parte di questa banca dati verri inserita dal programma che permettera di scorrere gli esami, ampiamente commentati e descritti, per fare cosi pratica nell'interpretazione diagnostica.Una valutazione del sistema prenderi in considerazione il risultato della didattica assistita da computer a confronto con la didattica convenzionale. 11 sistema esperto dovrebbe offrire un piu sistematico approccio all'interpretazione dei quadri e una piu facile consultazione di un importante archivio di esami commentati. SUMMARY -A collaborative research project between the Medical Systems Research Group, De Montfort University Leicester, and the School of Cognitive and Computing Sciences, University of Sussex, is examining the feasibility of utilising statistical-based principles and a structured image description language for tutoring about radiological image interpretation and diagnosis. This paper describes the construe tion of a sophisticated intelligent tutoring system (ITS) for the interpretation of magnetic resonance images in the diagnosis of cerebral disease.The outcome of the project will be a tutoring system and diagnostic aid for M R images which can be evaluated in a realistic educational setting. An evaluation of the syStem will show whether computer-assisted tutoring can be effective in supplementing conventional teaching, by teaching a systematic approach to image interpretation and by providing exposure to a large archive of annotated images. 29 Statistical Modelling and Structured Image Description for Intelligent Tutoring B.A. Teather

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Evaluation of computer advisor in the interpretation of CT images of the head

Cited by 13 publications

References 6 publications

Classification of brain tumours by CT image Walsh spectra

Classification of brain tumours by CT image Walsh spectra

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

Statistical Modelling and Structured Image Description for Intelligent Tutoring in MR Imaging of the Head

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