A cross-sectional study of explainable machine learning in Alzheimer’s disease: diagnostic classification using MR radiomic features

IntroductionAlzheimer’s disease (AD) even nowadays remains a complex neurodegenerative disease and its diagnosis relies mainly on cognitive tests which have many limitations. On the other hand, qualitative imaging will not provide an early diagnosis because the radiologist will perceive brain atrophy on a late disease stage. Therefore, the main objective of this study is to investigate the necessity of quantitative imaging in the assessment of AD by using machine learning (ML) methods. Nowadays, ML methods are used to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers in the assessment of AD.MethodsIn this study radiomic features from both entorhinal cortex and hippocampus were extracted from 194 normal controls (NC), 284 mild cognitive impairment (MCI) and 130 AD subjects. Texture analysis evaluates statistical properties of the image intensities which might represent changes in MRI image pixel intensity due to the pathophysiology of a disease. Therefore, this quantitative method could detect smaller-scale changes of neurodegeneration. Then the radiomics signatures extracted by texture analysis and baseline neuropsychological scales, were used to build an XGBoost integrated model which has been trained and integrated.ResultsThe model was explained by using the Shapley values produced by the SHAP (SHapley Additive exPlanations) method. XGBoost produced a f1-score of 0.949, 0.818, and 0.810 between NC vs. AD, MC vs. MCI, and MCI vs. AD, respectively.DiscussionThese directions have the potential to help to the earlier diagnosis and to a better manage of the disease progression and therefore, develop novel treatment strategies. This study clearly showed the importance of explainable ML approach in the assessment of AD

A systematic review of person-centred adjustments to facilitate magnetic resonance imaging for autistic patients without the use of sedation or anaesthesia

Magnetic resonance imaging is widely used for different diagnostic examinations involving autistic patients. The noisy, narrow, isolating magnetic resonance imaging environment and long scan times may not be suitable for autistic individuals, given their communication challenges, sensory sensitivities and often heightened anxiety. This systematic review aims to reveal any reasonable and feasible radiography-based adjustments to facilitate magnetic resonance imaging scanning without the use of sedation or general anaesthesia. Nine electronic databases were systematically searched. Out of 4442 articles screened, 53 were deemed directly relevant; when assessed against eligibility criteria, only 21 were finally included in this systematic review. Customising communication was found to be a key adjustment, as well as scan-based optimisation and environmental adaptations. The importance of distraction techniques and use of technology for familiarisation with the processes was also highlighted. The results of this study can inform recommendations to improve magnetic resonance imaging practice and patient experience, without the use of sedation or anaesthesia, where feasible. They can also inform the basis of dedicated training for magnetic resonance imaging radiographers. Lay abstract: Autistic patients often undergo magnetic resonance imaging examinations. Within this environment, it is usual to feel anxious and overwhelmed by noises, lights or other people. The narrow scanners, the loud noises and the long examination time can easily cause panic attacks. This review aims to identify any adaptations for autistic individuals to have a magnetic resonance imaging scan without sedation or anaesthesia. Out of 4442 articles screened, 53 more relevant were evaluated and 21 were finally included in this study. Customising communication, different techniques to improve the environment, using technology for familiarisation and distraction have been used in previous studies. The results of this study can be used to make suggestions on how to improve magnetic resonance imaging practice and the autistic patient experience. They can also be used to create training for the healthcare professionals using the magnetic resonance imaging scanners.Society and College of Radiographers CORIPSCity Radiography Research Fun

Toward Autism-Friendly Magnetic Resonance Imaging: Exploring Autistic Individuals' Experiences of Magnetic Resonance Imaging Scans in the United Kingdom, a Cross-Sectional Survey

Background: Autistic individuals might undergo a magnetic resonance imaging (MRI) examination for clinical concerns or research. Increased sensory stimulation, lack of appropriate environmental adjustments or lack of streamlined communication in the MRI suite may pose challenges to autistic patients and render MRI scans inaccessible. This study aimed to i) explore the MRI scan experiences of autistic adults in the UK, ii) identify barriers and enablers towards successful and safe MRI examinations, iii) assess autistic individuals’ satisfaction with MRI service, and iv) inform future recommendations for practice improvement. Methods: We distributed an online survey to the autistic community on social media, using snowball sampling. Inclusion criteria were: being older than 16, have an autism diagnosis or self-diagnosis, self-reported capacity to consent and having had an MRI scan in the UK. We used descriptive statistics for demographics, inferential statistics for group comparisons/correlations, and content analysis for qualitative data. Results: We received 112 responses. A total of 29.6% of the respondents reported not being sent any information before the scan. Most participants (68%) confirmed that radiographers provided detailed information on the day of the examination but only 17.1% reported that radiographers offered some reasonable environmental adjustments. Only 23.2% of them confirmed they disclosed their autistic identity when booking MRI scanning. We found that quality of communication, physical environment, patient emotions, staff training and confounding societal factors impacted autistic people’s experiences. Autistic individuals rated their overall MRI experience as neutral and reported high levels of claustrophobia (44.8%). Conclusion: The study highlighted a lack of effective communication and coordination of care, either between healthcare services or between patients and radiographers, and lack of reasonable adjustments as vital for more accessible and person-centred MRI scanning for autistic individuals. Enablers of successful scans included effective communication, adjusted MRI environment, scans tailored to individuals’ needs/preferences, and well-trained staff.College of Radiographers Industry Partnership Scheme Research GrantAhead of print, check citing and date details in 6 m pleaseCan't get access trhough UCD so can't see pdf and no pages2023-06-15 JG: PDF updated to remove duplicate cover page erroneously added by RR

An Inverse Neural Controller Based on the Applicability Domain of RBF Network Models

This paper presents a novel methodology of generic nature for controlling nonlinear systems, using inverse radial basis function neural network models, which may combine diverse data originating from various sources. The algorithm starts by applying the particle swarm optimization-based non-symmetric variant of the fuzzy means (PSO-NSFM) algorithm so that an approximation of the inverse system dynamics is obtained. PSO-NSFM offers models of high accuracy combined with small network structures. Next, the applicability domain concept is suitably tailored and embedded into the proposed control structure in order to ensure that extrapolation is avoided in the controller predictions. Finally, an error correction term, estimating the error produced by the unmodeled dynamics and/or unmeasured external disturbances, is included to the control scheme to increase robustness. The resulting controller guarantees bounded input-bounded state (BIBS) stability for the closed loop system when the open loop system is BIBS stable. The proposed methodology is evaluated on two different control problems, namely, the control of an experimental armature-controlled direct current (DC) motor and the stabilization of a highly nonlinear simulated inverted pendulum. For each one of these problems, appropriate case studies are tested, in which a conventional neural controller employing inverse models and a PID controller are also applied. The results reveal the ability of the proposed control scheme to handle and manipulate diverse data through a data fusion approach and illustrate the superiority of the method in terms of faster and less oscillatory responses

A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons

The increasing penetration of renewable energy sources tends to redirect the power systems community’s interest from the traditional power grid model towards the smart grid framework. During this transition, load forecasting for various time horizons constitutes an essential electric utility task in network planning, operation, and management. This paper presents a novel mixed power-load forecasting scheme for multiple prediction horizons ranging from 15 min to 24 h ahead. The proposed approach makes use of a pool of models trained by several machine-learning methods with different characteristics, namely neural networks, linear regression, support vector regression, random forests, and sparse regression. The final prediction values are calculated using an online decision mechanism based on weighting the individual models according to their past performance. The proposed scheme is evaluated on real electrical load data sensed from a high voltage/medium voltage substation and is shown to be highly effective, as it results in R2 coefficient values ranging from 0.99 to 0.79 for prediction horizons ranging from 15 min to 24 h ahead, respectively. The method is compared to several state-of-the-art machine-learning approaches, as well as a different ensemble method, producing highly competitive results in terms of prediction accuracy

Responsible AI practice and AI education are central to AI implementation

Objectives: It is essential to study the dosimetric performance and reliability of personal dosimeters. This study examines and compares the responses of two commercial thermoluminescence dosimeters (TLDs), the TLD-100 and the MTS-N. Methods: We compared the two TLDs to various parameters such as energy dependence, linearity, homogeneity, reproducibility, light sensitivity (zero point), angular dependence, and temperature effects using the IEC 61066 standard. Results: The results acquired showed that both TLD materials show linear behavior as indicated by the quality of the fit. In addition, the angular dependence results for both detectors show that all dose responses are within the range of acceptable values. However, the TLD-100 outperformed the MTS-N in terms of light sensitivity reproducibility for all detectors together, while the MTS-N outperforms the TLD-100 for each detector independently and that showed TLD-100 has more stability than MTS-N. The MTS-N shows better batch homogeneity (10.84%) than TLD-100 (13.65%). The effect of temperature in signal loss was clearer at higher temperature 65°C and it was however below ±30%. Conclusions: The overall results for dosimetric properties determined in terms of dose equivalents for all combinations of detectors are satisfactory. The MTS-N cards have better results in the energy dependence, angular dependency, batch homogeneity and less signal fading, whereas the TLD-100 cards are less sensitive to light and more reproducible. Advances in knowledge: Although previous studies showed several types of comparisons between TLDs, they have used limited parameters and different data analysis. This study has dealt with more comprehensive characterization methods and examinations combining TLD-100 and MTS-N cards