Fuzzy logic applications for data acquisition systems of practical measurement

In laboratory works, the error in measurement, reading the measurring devices, similarity of experimental data and lack of understanding of practicum materials are often found. These will lead to the inacurracy and invalid in data obtanined. As an alternative solution, application of fuzzy logic to the data acquisition system using a web server can used. This research focuses on the design of data acquisition systems with the target of reducing the error rate in measuring experimental data on the laboratory. Data measurement on laboratory practice module is done by taking the analog data resulted from the measurement. Furthermore, the data are converted into digital data via arduino and stored on the server. To get valid data, the server will process the data by using fuzzy logic method. The valid data are integrated into a web server so that it can be accessed as needed. The results showed that the data acquisition system based on fuzzy logic is able to provide recommendation of measurement result on the lab works based on the degree value of membership and truth value. Fuzzy logic will select the measured data with a maximum error percentage of 5% and select the measurement result which has minimum error rate

Towards Integration of Artificial Intelligence into Medical Devices as a Real-Time Recommender System for Personalised Healthcare:State-of-the-Art and Future Prospects

In the era of big data, artificial intelligence (AI) algorithms have the potential to revolutionize healthcare by improving patient outcomes and reducing healthcare costs. AI algorithms have frequently been used in health care for predictive modelling, image analysis and drug discovery. Moreover, as a recommender system, these algorithms have shown promising impacts on personalized healthcare provision. A recommender system learns the behaviour of the user and predicts their current preferences (recommends) based on their previous preferences. Implementing AI as a recommender system improves this prediction accuracy and solves cold start and data sparsity problems. However, most of the methods and algorithms are tested in a simulated setting which cannot recapitulate the influencing factors of the real world. This review article systematically reviews prevailing methodologies in recommender systems and discusses the AI algorithms as recommender systems specifically in the field of healthcare. It also provides discussion around the most cutting-edge academic and practical contributions present in the literature, identifies performance evaluation matrices, challenges in the implementation of AI as a recommender system, and acceptance of AI-based recommender systems by clinicians. The findings of this article direct researchers and professionals to comprehend currently developed recommender systems and the future of medical devices integrated with real-time recommender systems for personalized healthcare

On the development of intelligent medical systems for pre-operative anaesthesia assessment

This thesis describes the research and development of a decision support tool for determining a medical patient's suitability for surgical anaesthesia. At present, there is a change in the way that patients are clinically assessedp rior to surgery. The pre-operative assessment, usually conducted by a qualified anaesthetist, is being more frequently performed by nursing grade staff. The pre-operative assessmenet xists to minimise the risk of surgical complications for the patient. Nursing grade staff are often not as experienced as qualified anaesthetists, and thus are not as well suited to the role of performing the pre-operative assessment. This research project used data collected during pre-operative assessments to develop a decision support tool that would assist the nurse (or anaesthetist) in determining whether a patient is suitable for surgical anaesthesia. The three main objectives are: firstly, to research and develop an automated intelligent systems technique for classifying heart and lung sounds and hence identifying cardio-respiratory pathology. Secondly, to research and develop an automated intelligent systems technique for assessing the patient's blood oxygen level and pulse waveform. Finally, to develop a decision support tool that would combine the assessmentsa bove in forming a decision as to whether the patient is suitable for surgical anaesthesia. Clinical data were collected from hospital outpatient departments and recorded alongside the diagnoses made by a qualified anaesthetist. Heart and lung sounds were collected using an electronic stethoscope. Using this data two ensembles of artificial neural networks were trained to classify the different heart and lung sounds into different pathology groups. Classification accuracies up to 99.77% for the heart sounds, and 100% for the lung sounds has been obtained. Oxygen saturation and pulse waveform measurements were recorded using a pulse oximeter. Using this data an artificial neural network was trained to discriminate between normal and abnormal pulse waveforms. A discrimination accuracy of 98% has been obtained from the system. A fuzzy inference system was generated to classify the patient's blood oxygen level as being either an inhibiting or non-inhibiting factor in their suitability for surgical anaesthesia. When tested the system successfully classified 100% of the test dataset. A decision support tool, applying the genetic programming evolutionary technique to a fuzzy classification system was created. The decision support tool combined the results from the heart sound, lung sound and pulse oximetry classifiers in determining whether a patient was suitable for surgical anaesthesia. The evolved fuzzy system attained a classification accuracy of 91.79%. The principal conclusion from this thesis is that intelligent systems, such as artificial neural networks, genetic programming, and fuzzy inference systems, can be successfully applied to the creation of medical decision support tools.EThOS - Electronic Theses Online ServiceMedicdirect.co.uk Ltd.GBUnited Kingdo

Design An Intelligent System to Support Dental Cyst Detection Using Two Convolutional Neural Networks

The aim of this paper is to develop a methodology, through studies on Computer Vision techniques, for the automatic identification of dental cysts in panoramic radiography images, providing Dental professionals with an alternative aid in the interpretation of these images. In addition segmentation techniques are applied in the inner region of the jaws, seeking to separate the regions with a greater possibility of cyst. The objective of this work is to design an intelligent system that supports the diagnosis of Dental Cyst using convolutional neural networks in order to help detect Dental Cyst at an early stage. The research method applied in this study consists of model design, where built and trained two convolutional neural network architectures, supporting 80% of the dataset with a total of 775 images with four image categories, and proposal validation, where we work with the remaining 20% of the dataset. Our results show that the ResNet50 architecture achieved the best classification with an accuracy of 98%

Validation of a magnetic resonance imaging-based auto-contouring software tool for gross tumour delineation in head and neck cancer radiotheraphy planning

To perform statistical validation of a newly developed magnetic resonance imaging (MRI) auto-contouring software tool for gross tumour volume (GTV) delineation in head and neck tumours to assist in radiotherapy planning. Axial MRI baseline scans were obtained for 10 oropharyngeal and laryngeal cancer patients. GTV was present on 102 axial slices and auto-contoured using the modified fuzzy c-means clustering integrated with level set method (FCLSM). Peer reviewed (C-gold) manual contours were used as the reference standard to validate auto-contoured GTVs (C-auto) and mean manual contours (C-manual) from 2 expert clinicians (C1 and C2). Multiple geometrical metrics, including Dice Similarity Coefficient (DSC) were used for quantitative validation. A DSC ≥0.7 was deemed acceptable. Inter-and intra-variabilities amongst the manual contours were also validated. The 2-dimension (2D) contours were then reconstructed in 3D for GTV volume calculation, comparison and 3D visualisation. The mean DSC between C-gold and C-auto was 0.79. The mean DSC bet ween C-gold and C-manual was 0.79 and that between C1 and C2 was 0.80. The average time for GTV auto-contouring per patient was 8 minutes (range 6-13mins; mean 45seconds per axial slice) compared to 15 minutes (range 6-23mins; mean 88 seconds per axial slice) for C1. The average volume concordance between C-gold and C-auto volumes was 86. 51% compared to 74.16% between C-gold and C-manual. The average volume concordance between C1 and C2 volumes was 86.82%. This newly-designed MRI-based auto-contouring software tool shows initial acceptable results in GTV delineation of oropharyngeal and laryngeal tumours using FCLSM. This auto-contouring software tool may help reduce inter-and intra- variability and can assist clinical oncologists with time-consuming, complex radiotherapy planning