# Acoustic Voice Analysis as a Tool for Assessing Nasal Obstruction: A Systematic Review
## 4 Discussion
They improve shared decision-making, symptom management, patient satisfaction, and quality of life. PROMs are essential for guiding and customizing personalized care strategies [50]. On the other hand, questionnaires present problems of accuracy and response rate, and, most of all, the administration of PROMs is time-consuming [50; 51]. Remote health monitoring systems, which have already demonstrated potential for improving the efficiency and quality of healthcare services, particularly in managing chronic diseases and the elderly [52], may offer a suitable solution for administering PROM questionnaires in the context of CRS care. Conversely, remote health monitoring systems can also access voice through mobile phones, tablets, computers, and wearables. Voice can serve as an invaluable resource for delivering objective information regarding the remote monitoring of nasal obstruction and, consequently, CRSwNP. These are the concept and needs behind the present review, which aims to define the present state of the art for the assessment of nasal obstruction through voice. It is clear that voice analysis has the theoretical potential to develop into a noninvasive, adjustable monitoring tool, especially when combined with machine learning techniques. Combining acoustic voice analysis with patient-reported outcome measures (PROMs) and other objective tests could provide a complete and affordable way to handle nasal obstruction and create personalized treatment plans [15; 50; 52]. Therefore, the present review aims to outline the current state of our knowledge about the specific voice output features that define the hyponasality of speech (HN-VOFs), or our ability to reliably obtain information about nasal obstruction through acoustic voice analysis. In this context, the findings of the current study are somewhat disappointing. In the existing literature, only 10 studies have attempted to measure nasal obstruction using voice analysis in conjunction with objective evaluations of nasal obstruction. Out of those studies, only one from 2009 [21] shows a clear link between a surgical procedure (septoplasty) and various measurements. However, it does not directly relate to how open the nasal passages are, and many of those measurements (VTI, SPI, DUV, DVB, and vAM) have not been examined in other research. Among traditional acoustic measures, shimmer emerged as the most sensitive parameter, with statistically significant changes observed in three studies [21; 22; 23]. In contrast, jitter, although often mentioned, was only shown to be important in one study [21], which suggests it is not very effective at detecting voice changes related to nasal issues. Parameters such as F0, NHR, and HNR were also investigated; however, only a few studies [21; 22] found significant relationships with nasal obstruction. Therefore, current knowledge about HN-VOFs is limited and conflicting. This failure may be attributed to methodological heterogeneity, small sample sizes (see Table 1), variability in speech materials, diverse acoustic measurement protocols, and a lack of standardized recording equipment (Table 2) [19; 20; 21; 23; 26; 27; 53]. However, we do believe that the main reason is that the current approach to acoustic voice analysis is focused on the features correlated with glottic sound and dysphonia and not on the modifications of the glottic output by the vocal tract, which ultimately produces the voice. If we want to use voice output to obtain information on the vocal tract, including the nasal cavities, we must focus on the features of voice output directly impacted by it. The most obvious approach could be an analysis of the resonance in the whole spectrum of the harmonics of our voice, and machine learning (ML) and artificial intelligence (AI) could be helpful in this matter [54]. In fact, machine learning (ML) and artificial intelligence (AI) techniques can enhance the detection of subtle voice changes linked to nasal obstruction by analyzing intricate, multidimensional acoustic data, potentially improving current outcomes [54].