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Biomedical subjects

D Teather

Publications and source records attributed to D Teather.

At least 19 recordsLinked to original sources
PubMed2000

Structured computer-based training in the interpretation of neuroradiological images.

Computer-based systems may be able to address a recognised need throughout the medical profession for a more structured approach to training. We describe a combined training system for neuroradiology, the MR Tutor that differs from previous approaches to computer-assisted training in radiology in that it provides case-based tuition whereby the system and user communicate in terms of a well-founded Image Description Language. The system implements a novel method of visualisation and interaction with a library of fully described cases utilising statistical models of similarity, typicality and disease categorisation of cases. We describe the rationale, knowledge representation and design of the system, and provide a formative evaluation of its usability and effectiveness.

Artificial Intelligence↗
PubMed2000

Statistical support for uncertainty in radiological diagnosis.

Radiological interpretation and diagnosis involves the comparison and classification of complex medical images and is typical of the categorisation tasks that have been the subject of observational studies in Cognitive Science. This paper considers the affinity between statistical modelling and theories of categorisation for naturally occurring categories. Statistical based measures of similarity and typicality with a probabilistic interpretation are derived. The utilisation of these measures in the support of diagnosis under uncertainty via interactive overview plots is described. The application of the methodology to magnetic resonance imaging of the head is considered. The methods detailed have application to other fields involving archiving and retrieving of image data.

Decision Support Techniques↗
PubMed1990

Comparing the cost of spinal MR with conventional myelography and radiculography.

All spinal magnetic resonance imaging examinations carried out during a three month period were analysed retrospectively in order to determine the clinical reasons for the scan requests. Technical details of the examinations they received and the clinical profiles formed a data set which revealed 10 separate "Clinical groups" for management purposes. Hardware, salary and expendables were costed as though the imaging unit had been sited within a National Health Service radiology department. A spread sheet was designed capable of calculating costs per patient for a variety of types of working week and of different staffing structures, sensitive to the mixture of clinical groups referred for examination. The spreadsheet also accomodated straight line depreciation for hardware value and interest rates for borrowed capital. A second, prospectively observed, sample of spinal MR examinations was used to improve the accuracy of the timing of the length of patient examinations. Costs were compared with those for patients submitted for myelography and radiculography at the adjacent hospital during the same period. The comparison indicated that spinal MR was less costly than myelography and radiculography. The most important element of the extra cost of myelography related to the need to admit patients to hospital for at least one night for this examination because of the likelihood of headache and other common (though usually minor) complications following lumbar puncture and/or the injection of contrast medium. From the limited information that it was possible to obtain in the period of follow up, it appeared that MR had either been superior or equivalent to myelography or radiculography in all the clinical groups of patients where both could be tested. There were a number of groups in which no myelograms had been requested, presumably because clinical suspicions had pointed toward conditions like tumours, developmental abnormalities and demyelinating diseases in which neurologists and neurosurgeons have already made up their minds about the superiority of MR.

Cost-Benefit Analysis↗
PubMed1988

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

This paper describes the evaluation of a computer advisor system (BRAINS), which was constructed to aid in the interpretation of CT images of the head. It was developed at the National Hospital for Nervous Diseases, Queen Square, London. The system was transferred, without difficulty, to an 'external', that is previously unassociated, site (the Department of Diagnostic Radiology, University of Manchester) for an external evaluation. Response of external users to the system was mixed. Many were unfamiliar with the concept of formal description of images and the evaluation demonstrated the need for a person to person training programme. Users who accessed the HELP facilities most frequently were the most successful in obtaining accurate descriptions and hence satisfactory diagnostic advice. An objective appraisal of user's success in describing images to obtain the correct diagnosis as first choice indicated that, in general, the system performed well.

Brain Diseases↗
PubMed1988

Towards an advisor for MRI.

This paper discusses the role of a computer advisor in MR image acquisition, interpretation and the diagnosis of cerebral disease. The development of an image and statistical database for use in providing advice is described.

Brain Diseases↗
PubMed1984

Statistical modelling and diagnostic aids.

This paper considers the problems involved in constructing operational aids for diagnosis. A collaborative approach is proposed which involves utilisation of the expertise of clinician and statistician, working jointly on the analysis of hard data for a particular diagnostic application. The application of this approach to a problem involving cerebral disease diagnosis based on CT scan data is described.

Bayes Theorem↗
PubMed1982

An improvement in computer-aided diagnosis of meningiomas after CT.

CT examination of 497 patients with subsequently proven pathology have been analysed and subjected to computer-aided diagnosis using Bayes' theorem. Our previous work was as accurate and reliable for diagnosing metastases and gliomas as that achieved by skilled radiologists in reporting. The present paper reports an alteration to the programme that has improved the diagnosis of meningioma.

Bayes Theorem↗
PubMed1981

Initial findings in the computer-aided diagnosis of cerebral tumours using CT scan results.

This paper describes the initial findings of computer-assisted diagnosis of cerebral tumours using Bayes' Theorem and CT scans. For various sets of signs, tables of accuracy and reliability have been constructed, and a computer versus radiologist analysis for three types of tumour has been made. Based only on the results of CT scans, the computer has produced diagnoses relatively quickly, and overall these are of reasonable accuracy when compared with the radiologists' reports. The worst results occurred in the prediction of meningiomas, the reasons for which will be examined in subsequent work.

Bayes Theorem↗
PubMed1977

Improvement in the computer-assisted diagnosis of cerebral tumours.

Most applications of Bayes theorem in computer-aided diagnosis have been to situations involving the differential diagnosis of a small list of disease categories. In the case of the diagnosis of cerebral tumours, if each tumour type in each main anatomical situation is counted as a single diagnosis, there are about 100 possible disease categories. This paper investigates a method of incorporating expert prior information into the computer-aided diagnosis process so that this large number of categories can be handled.

Adenoma↗
PubMedDate not supplied

A comparison of various estimators of a treatment difference for a multi-centre clinical trial.

When a clinical trial is conducted at more than one centre it is likely that the true treatment effect will not be identical at each centre. In other words there will be some degree of treatment-by-centre interaction. A number of alternative approaches for dealing with this have been suggested in the literature. These include frequentist approaches with a fixed or random effects model for the observed data and Bayesian approaches. In the fixed effects model, there are two common competing estimators of the treatment difference, based on weighted or unweighted estimates from individual centres. Which one of these should be used is the subject of some controversy and we do not intend to take a particular methodological position in this paper. Our intention is to provide some insight into the relative merits of the indicated range of possible estimators of the treatment effect. For the fixed effects model, we also look at the merits of using a preliminary test for interaction assuming a 10 per cent significance level for the test. In order to make comparisons we have simulated a 'typical' trial which compares an active drug with a placebo in the treatment of hypertension, using systolic blood pressure as the primary variable. As well as allowing the treatment effect to vary between centres, we have concentrated on the particular case where one centre is out of line with the others in terms of its true treatment difference. The various estimators that result from the different approaches are compared in terms of mean squared error and power to reject the null hypothesis of no treatment difference. Overall, the approach that uses the fixed effects weighted estimator of overall treatment difference is recommended as one that has much to offer.

Analysis of Variance↗