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

Itch and skin rash from chocolate during fluoxetine and sertraline treatment: Case report

BACKGROUND: The skin contains a system for producing serotonin as well as serotonin receptors. Serotonin can also cause pruritus when injected into the skin. SSRI-drugs increase serotonin concentrations and are known to have pruritus and other dermal side effects. CASE PRESENTATION: A 46-year-old man consulted his doctor due to symptoms of depression. He did not suffer from any allergy but drinking red wine caused vasomotor rhinitis. Antidepressive treatment with fluoxetine 20 mg daily was initiated which was successful. After three weeks of treatment an itching rash appeared. An adverse drug reaction (ADR) induced by fluoxetine was suspected and fluoxetine treatment was discontinued. The symptoms disappeared with clemastine and betametasone treatment. Since the depressive symptoms returned sertraline medication was initiated. After approximately two weeks of sertraline treatment he noted an intense itching sensation in his scalp after eating a piece of chocolate cake. The itch spread to the arms, abdomen and legs and the patient treated himself with clemastine and the itch disappeared. He now realised that he had eaten a chocolate cake before this episode and remembered that before the first episode he had had a chocolate mousse dessert. He had never had any reaction from eating chocolate before and therefore reported this observation to his doctor. CONCLUSIONS: This case report suggests that there may be individuals that are very sensitive to increases in serotonin concentrations. Dermal side reactions to SSRI-drugs in these patients may be due to high activity in the serotonergic system at the dermal and epidermo-dermal junctional area rather than a hypersensitivity to the drug molecule itself

Carotid Ultrasound Boundary Study (CUBS): An Open Multicenter Analysis of Computerized Intima–Media Thickness Measurement Systems and Their Clinical Impact

Common carotid intima–media thickness (CIMT) is a commonly used marker for atherosclerosis and is often computed in carotid ultrasound images. An analysis of different computerized techniques for CIMT measurement and their clinical impacts on the same patient data set is lacking. Here we compared and assessed five computerized CIMT algorithms against three expert analysts’ manual measurements on a data set of 1088 patients from two centers. Inter- and intra-observer variability was assessed, and the computerized CIMT values were compared with those manually obtained. The CIMT measurements were used to assess the correlation with clinical parameters, cardiovascular event prediction through a generalized linear model and the Kaplan–Meier hazard ratio. CIMT measurements obtained with a skilled analyst's segmentation and the computerized segmentation were comparable in statistical analyses, suggesting they can be used interchangeably for CIMT quantification and clinical outcome investigation. To facilitate future studies, the entire data set used is made publicly available for the community at http://dx.doi.org/10.17632/fpv535fss7.1

Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications

Measurement of ultrasonic diaphragmatic motion

The motion characteristics of the diaphragmatic muscle may provide useful information about normal and abnormal diaphragmatic function and indicate diaphragmatic weakness. The objective of this paper was to introduce a simple system for the quantitative analysis of ultrasonic diaphragmatic motion. The measurements routinely carried out by the experts were computed and these include: (i) excursion, (ii) inspiration time (Tinsp) and (iii) cycle duration (Ttot). The system was evaluated on four simulated videos and one real video. Manual and automated measurements were very close. Further work in a larger number of videos is needed for validating the proposed method © 2015 IEEE

Assessment of stroke by analysing carotid plaque morphology

Stroke is the third leading cause of death in the Western world and a major cause of disability in adults. The objective of this work was to investigate morphological feature extraction techniques and the use of automatic classifiers; in order to develop a computer aided system that will facilitate the automated characterization of carotid plaques for the identification of individuals with asymptomatic carotid stenosis at risk of stroke. Through this chapter we summarize the recent advances in ultrasonic plaque characterization and evaluate the efficacy of computer aided diagnosis based on neural and statistical classifiers using as input morphological features. Several classifiers like the K-Nearest Neighbour(KNN) the Probabilistic Neural Network(PNN) and the Support Vector Machine(SVM) were evaluated resulting to a diagnostic accuracy up to 73.7%

Computer-aided detection of breast cancer nuclei

A computer-aided detection system for tissue cell nuclei in histological sections is introduced and validated as part of the Biopsy Analysis Support System (BASS). Cell nuclei are selectively stained with monoclonal antibodies, such as the anti-estrogen receptor antibodies, which are widely applied as part of assessing patient prognosis in breast cancer. The detection system uses a receptive field filter to enhance negatively and positively stained cell nuclei and a squashing function to label each pixel value as belonging to the background or a nucleus. In this study, the detection system assessed all biopsies in an automated fashion. Detection and classification of individual nuclei as well as biopsy grading performance was shown to be promising as compared to that of two experts. Sensitivity and positive predictive value were measured to be 83% and 67.4%, respectively. One major advantage of BASS stems from the fact that the system simulates the assessment procedures routinely employed by human experts; thus it can be used as an additional independent expert. Moreover, the system allows the efficient accumulation of data from large numbers of nuclei in a short time span. Therefore, the potential for accurate quantitative assessments is increased and a platform for more standardized evaluations is provided