Common medical ethics dilemmas: Few reflections from a Saudi perspective.

Medical ethics plays a crucial role in physicians' daily practice, as it reflects on themselves, their institution, and, most importantly, the outcome of the treatment they provide to their patients. Common medical ethics dilemmas faced in Saudi Arabia include: end-of-life care, patient rights, which comprise autonomy, informed consent, and confidentiality, reproductive ethics, and equity of resources. The identification of flaws within the healthcare system and the implementation of clear guidelines are important to overcome the risk of malpractice and flawed judgment, and ensure the delivery of the best possible care to patients.

Management of Free-Floating Thrombus in the Vertebral Artery in a Middle-Aged Smoker with Secondary Polycythemia Vera.

Free-floating thrombus (FFT) of the cervicocranial arteries is a rare neurovascular condition. Up to now, there is no standardized definition for FFT. Therefore, FFT is occasionally mistaken for intraluminal thrombus (ILT) or smooth mural thrombus. The most precise and ideal definition of FFT would be a long-extended intraarterial thrombus that is attached to the arterial wall with its one end, while its other end is surrounded by blood flow and moves freely with the cardiac cycle. FFT usually manifests as an ischemic stroke, thus it is considered as an emergency case. Herein, we report a rare case of symptomatic FFT in the left vertebral artery extending from V0 to V2 segments in a middle-aged smoker, who presented with multiple embolic strokes in different territories of posterior circulation and was successfully treated medically. This case sheds light on the challenges of the clinical approach of FFT in the vertebral artery and it is an attempt to draw attention to the necessity of conducting a large-scale study to find out the ideal approach to manage such conditions.

Combining anatomical and functional networks for neuropathology identification: A case study on autism spectrum disorder.

While the prevalence of Autism Spectrum Disorder (ASD) is increasing, research continues in an effort to identify common etiological and pathophysiological bases. In this regard, modern machine learning and network science pave the way for a better understanding of the neuropathology and the development of diagnosis aid systems. The present work addresses the classification of neurotypical and ASD subjects by combining knowledge about both the structure and the functional activity of the brain. In particular, we model the brain structure as a graph, and the resting-state functional MRI (rs-fMRI) signals as values that live on the nodes of that graph. We then borrow tools from the emerging field of Graph Signal Processing (GSP) to build features related to the frequency content of these signals. In order to make these features highly discriminative, we apply an extension of the Fukunaga-Koontz transform. Finally, we use these new markers to train a decision tree, an interpretable classification scheme, which results in a final diagnosis aid model. Interestingly, the resulting decision tree outperforms state-of-the-art methods on the publicly available Autism Brain Imaging Data Exchange (ABIDE) collection. Moreover, the analysis of the predictive markers reveals the influence of the frontal and temporal lobes in the diagnosis of the disorder, which is in line with previous findings in the literature of neuroscience. Our results indicate that exploiting jointly structural and functional information of the brain can reveal important information about the complexity of the neuropathology.

Towards interpretable machine learning models for diagnosis aid: A case study on attention deficit/hyperactivity disorder.

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that has heavy consequences on a child's wellbeing, especially in the academic, psychological and relational planes. The current evaluation of the disorder is supported by clinical assessment and written tests. A definitive diagnosis is usually made based on the DSM-V criteria. There is a lot of ongoing research on ADHD, in order to determine the neurophysiological basis of the disorder and to reach a more objective diagnosis. The advent of Machine Learning (ML) opens up promising prospects for the development of systems able to predict a diagnosis from phenotypic and neuroimaging data. This was the reason why the ADHD-200 contest was launched a few years ago. Based on the publicly available ADHD-200 collection, participants were challenged to predict ADHD with the best possible predictive accuracy. In the present work, we propose instead a ML methodology which primarily places importance on the explanatory power of a model. Such an approach is intended to achieve a fair trade-off between the needs of performance and interpretability expected from medical diagnosis aid systems. We applied our methodology on a data sample extracted from the ADHD-200 collection, through the development of decision trees which are valued for their readability. Our analysis indicates the relevance of the limbic system for the diagnosis of the disorder. Moreover, while providing explanations that make sense, the resulting decision tree performs favorably given the recent results reported in the literature.