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Technology and Health Care is intended to serve as a forum for the presentation of original articles and technical notes, observing rigorous scientific standards. Furthermore, upon invitation, reviews, tutorials, discussion papers and minisymposia are featured.
The following types of contributions and areas are considered:
1. Original articles:
Technology development in medicine: New concepts, procedures and devices associated with the use of technology in medical research and clinical practice are presented to a readership with a widespread background in engineering and/or medicine.
Significance of medical technology and informatics for healthcare: The appropriateness, efficacy and usefulness deriving from the application of engineering methods, devices and informatics in medicine and with respect to public health are discussed.
2. Technical notes:
Short communications on novel technical developments with relevance for clinical medicine.
3. Reviews and tutorials (upon invitation only):
Tutorial and educational articles for persons with a primarily medical background on principles of engineering with particular significance for biomedical applications and vice versa are presented.
4. Minisymposia (upon invitation only):
Under the leadership of a Special Editor, controversial issues relating to healthcare are highlighted and discussed by various authors.
Abstract: This paper discusses the problems in assigning a precise value to an item (or group of items) of clinical information. Historical viewpoints are reviewed; the paper illustrates how determinist descriptive thinking has been overtaken by concepts of uncertainty and probability. Four equations are proposed outlining the factors which affect the value of clinical information. The validation of these equations and their implications is discussed. Parallels are drawn between the situation in theoretical physics a century ago and medical diagnostics today; and the central theme of uncertainty in both is emphasised. Finally, the need for further radical thinking is stressed.
Keywords: Diagnosis, uncertainty, probability, quantum theory
Abstract: Breast cancer is the most common malignancy affecting the female population in industrialized countries. Prognostic factors, such as steroid receptors visualized in biopsy slides, provide critical information to oncologists regarding the hormonal status of the individual tumors. These factors influence the choice of treatment and help in predicting patient survival and probability of recurrence. The objective of this paper is to introduce a new computer-aided system for the classification of breast cancer nuclei based on neural networks. Currently, medical experts assess steroid receptors in breast cancer biopsy slides mostly manually using four- or five-level grading schemes. These schemes are…based on the assessment of two parameters: number of nuclei positive and their staining intensity. Available computerized systems define their own grading schemes based on automated measurements of low-level features, such as optical density, texture, area, and others. However, the findings produced by these systems may not be readily comprehensible by the majority of medical experts who have been accustomed to manual assessment schemes. Moreover, findings from one system cannot be directly compared to findings obtained from other computerized systems. To date, no standardized assessment scheme exists for computerized systems, while interobserver and intraobserver variabilities limit the utility of the routinely used manual assessment schemes. In this paper a new system for computer-aided biopsy analysis is introduced. Here, we focus on the systeJ;Il’s nuclear classification module. The input to this module consists of a set of six local and global features: optical density, two chromaticity indices, a variance based texture measure, global nuclei density mean, and variance. The output of the nuclei classification module consists of a membership label in a zero to four grading scheme for each detected nucleus. The classification module is based on a feedforward neural network trained in a supervised fashion to classify the nuclear feature vectors. The sample data comprises 3015 nuclei from 28 images that were classified by a human expert. A Sammon plot visualization of the six dimensional input feature space shows that the classification problem is quite difficult. The neural network used in the classification module achieved 72% accuracy. Our results indicate that by using a nuclear classification module such as the one introduced in this paper it is possible to translate low-level system measurements into a vocabulary that is familiar to medical experts. Thus, a contribution is made to the standardization of grading schemes in addition to improving the accuracy in grading breast cancer nuclei.
Keywords: Breast cancer nuclei, immunocytochemistry, steroid receptors, image analysis, neural networks
Abstract: Breast cancer size determination in a preoperative stage is of great importance, as it is taking into account the classification of the tumour. In this paper a simple method of the improved determination of the actual tumour size is presented. The method is based on the calculation of correction factors taking into account the magnification of the tumour image during the mammographic examination. The method was applied for evaluation on forty cases of breast cancer. In all cases the final approximation of tumour maximum diameter with the presented method is almost equal to this obtained from the pathologists’ reports. The…possibility of wrong stage characterisation of a tumour, which is a problem arising from the measurement of the maximum diameter directly from the mammogram, is also minimised, especially in case that tumour size is between two stages.
Keywords: Mammography, breast cancer, classification
Abstract: A method for the analysis of variability of EEG signals is described. We examined simulated signals and real EEGs obtained from a normal subject and two epileptic patients. The first step of the method is based on autoregressive (AR) modelling of short EEG epochs. Prediction coefficients of the AR model were computed as a function of time from partially-overlapping moving windows of 2 s duration. The temporal behaviour of these coefficients was analysed to detect variability: quasi-stationary activity causes only smooth changes in the coefficients while variations in the amplitude and/or the frequency content of the signal are shown to…produce sharp changes in the coefficients. A segmentation algorithm was developed to detect and quantify with a numerical value (Difference Measure, DM) the AR coefficients variations.
Abstract: Tele-medical systems have been recently introduced in the field of networking as promising applications that can significantly improve the offering of medical treatment by providing services such as tele-advising, tele-surgery and remote monitoring in places where the presence of doctors or any medical specialists is difficult or time consuming. Some already existent networking models can be used for the establishment of a connection between the communicating sides. The offered network’s security is also a significant factor. The present paper describes a software environment implementing a particular aspect of a tele-medical system. The developed system includes features such as direct communication…between doctors and medical assistants, medical information acquisition and storing and high band information transfer in real-time. TCP/IP point-to-point protocol has been used for the implementation of the non bandwidth-critical connections. The application introduces novel features with the use of ATM connection for supporting the time-critical service of video transfer to and from the medical database.
Abstract: Keeping the oxygenation status of newborn infants within physiologic limits is a crucial task in intensive care. For this purpose several vital parameters are supervised routinely by monitors, such as electrocardiograph, transcutaneous partial oxygen pressure monitor and pulse oximeter. Each monitor issues an alarm signal whenever an upper or lower limit of the parameter(s) measured is exceeded. However, in practice it turns out, that a considerable amount of false alarms is generated by artefacts, which are attributed mostly to movements of the infants. Eliminating these false alarms would be of benefit to the staff as well as the patients of…the intensive care unit. Accordingly, an automated system based on Fuzzy Logic was developed, which is capable of distinguishing between critical situations and artefacts. The system is based on a Transputer IMS T425 in a PC, which collects the data from the monitors, plots it on a colour screen, saves it to hard disk and analyses it by Fuzzy Logic. Fuzzy algorithms were developed to generate more reliable alarms. All vital parameters of eight infants, who either moved often and/or frequently produced real alarm situations, were recorded. Synchronously the infants’ movements and care procedures were video taped. The data and video were analysed off line with the help of an experienced neonatologist. His judgement was compared to the analysis of the Fuzzy Logic system. The results show that it is possible to improve the reliability of the monitored data with the aid of an evaluation strategy based on Fuzzy Logic and hence distinguish between real alarm situations and movement artefacts to the extent that an application in an intensive care unit under routine conditions becomes conceivable.
Abstract: Machine learning methods have been applied in a variety of medical domains in order to improve medical decision making. Improved medical diagnosis and prognosis can be achieved through automatic analysis of patient data stored in medical records, i.e., by learning from past experience. Given patient records with corresponding diagnoses, machine learning methods are able to classify new cases either through constructing explicit rules that generalize the training cases (e.g., rule induction) or by storing (some of) the training cases for reference (instance-based learning). This paper presents the methodologies of rule induction and instance-based learning and their application to medical diagnosis,…in particular, the problem of early diagnosis of rheumatic diseases. It also discusses the possibility to use existing expert knowledge to support the learning process and the utility of such knowledge.
Abstract: The object of this study is the assessment of gestational age by using fetal biparietal diameter and head circumference based on automated image analysis and artificial neural networks. Standard ultrasonic measurements were made in 143 normal fetuses between 14 and 40 weeks’ gestation. Sixhundred and thirteen fetal head images were transferred to a microcomputer environment by means of a frame grabber and spatial software. Biparietal diameter and head circumference measurements were made by automated image processing and analysis techniques. In the next stage, these two fetal parameters were used to determine the gestational age by using an unsupervised artificial…neural network. Back propagation learning algorithm was trained by 552 fetal head images and the system was tested with the remaining 61 images. It has been demonstrated that 98% of gestational weeks were estimated correctly by our system.