Reducing the Heart Failure Burden in Romania by Predicting Congestive Heart Failure Using Artificial Intelligence: Proof of Concept

Voice Assessment and Vocal Biomarkers in Heart Failure: A Systematic Review

Despite major advances in recent years, the timely detection and prevention of incipient congestion in patients with chronic heart failure remains challenging. Most approaches are either invasive or require the acquisition of additional hardware. Leveraging voice analysis for the purposes of diagnosing, predicting risks, and telemonitoring clinical outcomes of patients with heart failure represents a promising, cost-effective, and convenient alternative compared with hitherto deployed methods. To expand this field, close collaboration of several disciplines of medicine and computer science is an obligatory requirement. The current review aims to lay out the state-of-the-art in this quickly advancing area of research. It elucidates the foundation for voice feature extraction, describes the prospective capabilities of this evolving technology, and outlines the challenges involved in identifying vocal biomarkers both in general and in the context of heart failure.

The Impairment of Social and Environmental Relationships in Patients With Heart Failure Correlated With Therapeutic Class

Background and objectives Heart failure (HF) significantly influences the quality of life, both physically and emotionally, as well as social and environmental relationships. One major objective of HF treatment is to maintain or improve the quality of life. The aims of the study were to assess the impact of HF on social relationships and the relationship with the environment, according to therapeutic class and the presence of comorbidities, and to identify predictive factors for the impairment of these dimensions of the quality of life. Materials and methods This study was based on a cross-sectional survey; 252 patients with HF who have referred themselves to the medical rehabilitation department of the "Avram Iancu" Clinical Hospital, Oradea, between February 2023 and February 2024 were included. The patients were divided into two groups (Group HF-S/V, patients undergoing treatment with sacubitril/valsartan; Group HF-CT, patients receiving conventional therapy). All patients were asked to complete two assessment tools: the Charlson Comorbidity Index (CCI) questionnaire and the World Health Organization Quality of Life Brief Version (WHOQOL-BREF) questionnaire. Results The mean values obtained per the domain of social relationships were significantly better for Group HF-CT (65.762 ± 12.519 versus 61.266 ± 12.428, p = 0.024). The mean values obtained on the domain of social relations and in relation to the environment were significantly better for Group HF-CT (65.762 ± 12.519 versus 61.266 ± 12.428, p = 0.024; 61.333 ± 13.461 versus 51.719 ± 16.769, p < 0.001). Both dimensions of the quality of life correlate with age and CCI (F = 7.793, p < 0.001, for social relationships; F = 16.821, p < 0.001, for relationship with the environment). Conclusions Social relationships and the relationship with the environment are affected in HF patients and correlate with age and comorbidity index, regardless of the type of therapy.

A machine learning approach to classifying New York Heart Association (NYHA) heart failure

According to the European Society of Cardiology, globally the number of patients with heart failure nearly doubled from 33.5 million in 1990 to 64.3 million in 2017, and is further projected to increase dramatically in this decade, still remaining a leading cause of morbidity and mortality. One of the most frequently applied heart failure classification systems that physicians use is the New York Heart Association (NYHA) Functional Classification. Each NYHA class describes a patient's symptoms while performing physical activities, delivering a strong indicator of the heart performance. In each case, a NYHA class is individually determined routinely based on the subjective assessment of the treating physician. However, such diagnosis can suffer from bias, eventually affecting a valid assessment. To tackle this issue, we take advantage of the machine learning approach to develop a decision-tree, along with a set of decision rules, which can serve as additional blinded investigator tool to make unbiased assessment. On a dataset containing 434 observations, the supervised learning approach was initially employed to train a Decision Tree model. In the subsequent phase, ensemble learning techniques were utilized to develop both the Voting Classifier and the Random Forest model. The performance of all models was assessed using 10-fold cross-validation with stratification.The Decision Tree, Random Forest, and Voting Classifier models reported accuracies of 76.28%, 96.77%, and 99.54% respectively. The Voting Classifier led in classifying NYHA I and III with 98.7% and 100% accuracy. Both Random Forest and Voting Classifier flawlessly classified NYHA II at 100%. However, for NYHA IV, Random Forest achieved a perfect score, while the Voting Classifier reported 90%. The Decision Tree showed the least effectiveness among all the models tested. In our opinion, the results seem satisfactory in terms of their supporting role in clinical practice. In particular, the use of a machine learning tool could reduce or even eliminate the bias in the physician's assessment. In addition, future research should consider testing other variables in different datasets to gain a better understanding of the significant factors affecting heart failure.

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

BACKGROUND

Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems.

OBJECTIVE

This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest.

METHODS

This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group.

RESULTS

In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network-based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models.

CONCLUSIONS

This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.

Special Issue on Big Data for eHealth Applications

In the last few years, the rapid growth in available digitised medical data has opened new challenges for the scientific research community in the healthcare informatics field [...]

Recent Advances in Managing Spinal Intervertebral Discs Degeneration

Low back pain (LBP) represents a frequent and debilitating condition affecting a large part of the global population and posing a worldwide health and economic burden. The major cause of LBP is intervertebral disc degeneration (IDD), a complex disease that can further aggravate and give rise to severe spine problems. As most of the current treatments for IDD either only alleviate the associated symptoms or expose patients to the risk of intraoperative and postoperative complications, there is a pressing need to develop better therapeutic strategies. In this respect, the present paper first describes the pathogenesis and etiology of IDD to set the framework for what has to be combated to restore the normal state of intervertebral discs (IVDs), then further elaborates on the recent advances in managing IDD. Specifically, there are reviewed bioactive compounds and growth factors that have shown promising potential against underlying factors of IDD, cell-based therapies for IVD regeneration, biomimetic artificial IVDs, and several other emerging IDD therapeutic options (e.g., exosomes, RNA approaches, and artificial intelligence).