Speech patterns and enunciation for encephalopathy determination-A prospective study of hepatic encephalopathy

Minimal Hepatic Encephalopathy

Minimal hepatic encephalopathy (MHE) is a pervasive frequent complication of cirrhosis of any etiology. The diagnosis of MHE is difficult as the standard neurologic examination is essentially within normal limits. None of the symptoms and signs of overt HE is present in a patient with MHE, such as confusion, disorientation, or asterixis. Progress has been made in diagnostic tools for detection of attention and cognitive deficits at the point of care of MHE. The development of MHE significantly impacts quality of life and activities of daily life in affected patients including driving motor vehicles and machine operation.

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

The problem of cleaning impaired speech is crucial for various applications such as speech recognition, telecommunication, and assistive technologies. In this paper, we propose a novel approach that combines Pareto-optimized deep learning with non-negative matrix factorization (NMF) to effectively reduce noise in impaired speech signals while preserving the quality of the desired speech. Our method begins by calculating the spectrogram of a noisy voice clip and extracting frequency statistics. A threshold is then determined based on the desired noise sensitivity, and a noise-to-signal mask is computed. This mask is smoothed to avoid abrupt transitions in noise levels, and the modified spectrogram is obtained by applying the smoothed mask to the signal spectrogram. We then employ a Pareto-optimized NMF to decompose the modified spectrogram into basis functions and corresponding weights, which are used to reconstruct the clean speech spectrogram. The final noise-reduced waveform is obtained by inverting the clean speech spectrogram. Our proposed method achieves a balance between various objectives, such as noise suppression, speech quality preservation, and computational efficiency, by leveraging Pareto optimization in the deep learning model. The experimental results demonstrate the effectiveness of our approach in cleaning alaryngeal speech signals, making it a promising solution for various real-world applications.

Artificial intelligence, machine learning, and deep learning in liver transplantation

Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.

Predicting which patients with cirrhosis will develop overt hepatic encephalopathy: Beyond psychometric testing

It remains challenging to identify covert hepatic encephalopathy and predict progression to overt hepatic encephalopathy. Psychometric testing is a widely used diagnostic modality, but it is often inaccurate and difficult to implement in diverse populations, making it a less than ideal assessment. Alternatively, by using easily accessible data from the electronic health record, simple clinical assessment tools, and patient-reported outcomes, we may be better able to predict hepatic encephalopathy across multiple populations. Furthermore, incorporation of patient-reported outcomes into our diagnostic toolset not only aids detection of covert hepatic encephalopathy and prediction of overt hepatic encephalopathy, but also allows us to target therapies and track their impact. Herein, we outline a potential algorithm based on these easily integrated tools to promote patient risk-stratification and early therapeutic intervention.

Gut microbiome-brain-cirrhosis axis

Cirrhosis is characterized by inflammation, degeneration, and fibrosis of liver tissue. Along with being the most common cause of liver failure and liver transplant, cirrhosis is a significant risk factor for several neuropsychiatric conditions. The most common of these is HE, which is characterized by cognitive and ataxic symptoms, resulting from the buildup of metabolic toxins with liver failure. However, cirrhosis patients also show a significantly increased risk for neurodegenerative diseases such as Alzheimer and Parkinson diseases, and for mood disorders such as anxiety and depression. In recent years, more attention has been played to communication between the ways the gut and liver communicate with each other and with the central nervous system, and the way these organs influence each other's function. This bidirectional communication has come to be known as the gut-liver-brain axis. The gut microbiome has emerged as a key mechanism affecting gut-liver, gut-brain, and brain-liver communication. Clinical studies and animal models have demonstrated the significant patterns of gut dysbiosis when cirrhosis is present, both with or without concomitant alcohol use disorder, and have provided compelling evidence that this dysbiosis also influences the cognitive and mood-related behaviors. In this review, we have summarized the pathophysiological and cognitive effects associated with cirrhosis, links to cirrhosis-associated disruption of the gut microbiome, and the current evidence from clinical and preclinical studies for the modulation of the gut microbiome as a treatment for cirrhosis and associated neuropsychiatric conditions.