Myocardial perfusion imaging single photon emission computed tomography may detect silent myocardial ischemia in patient with epilepsy.

BACKGROUND: The aim of the present study was to compare the myocardial perfusion imaging (MPI) with [99mTc]tetrofosmin stress - rest single-photon emission computer tomography (SPECT) of patients with epilepsy with matched control individuals. MATERIAL AND METHODS: All 29 adult epileptic patients were receiving antiepileptic drugs (AEDs) for epilepsy. Thirty-two individuals matched for gender and age consisted of the control group. MPIs SPECT were performed, and myocardial summed scores were obtained during stress (SSS) and rest (SRS) images. Abnormal MPI was considered when SSS was ≥ 4. In addition, the difference (SDS) between SSS and SRS was also assessed, which represents a rate of reversibility after stress. RESULTS: Twenty of 29 (68.97%) patients with epilepsy had abnormal MPI and 14/32 (43.75%) of the controls (p = 0.04). Among males, 18/23 patients and 11/25 controls had abnormal MPI (p = 0.01), with quite a significant difference for mean SSS between male patients and controls (p = 0.002). Furthermore, SDS comparison showed that irreversible abnormalities were more common in patients than in control individuals. A difference of inadequately compensated myocardial ischemia between patients treated with enzyme inducing AEDs and patients treated with valproic acid was also detected. CONCLUSIONS: Single-photon emission computer tomography (SPECT) may detect increased risk for coronary artery disease and further cardiovascular events in patients with epilepsy. Our findings favor the conclusion that SPECT could be used for the early identification of cardiovascular comorbidity in epilepsy.

Artificial intelligence techniques in asthma: a systematic review and critical appraisal of the existing literature.

Artificial intelligence (AI) when coupled with large amounts of well characterised data can yield models that are expected to facilitate clinical practice and contribute to the delivery of better care, especially in chronic diseases such as asthma.The purpose of this paper is to review the utilisation of AI techniques in all aspects of asthma research, i.e. from asthma screening and diagnosis, to patient classification and the overall asthma management and treatment, in order to identify trends, draw conclusions and discover potential gaps in the literature.We conducted a systematic review of the literature using PubMed and DBLP from 1988 up to 2019, yielding 425 articles; after removing duplicate and irrelevant articles, 98 were further selected for detailed review.The resulting articles were organised in four categories, and subsequently compared based on a set of qualitative and quantitative factors. Overall, we observed an increasing adoption of AI techniques for asthma research, especially within the last decade.AI is a scientific field that is in the spotlight, especially the last decade. In asthma there are already numerous studies; however, there are certain unmet needs that need to be further elucidated.