# The Role of Data Analytics in the Assessment of Pathological Speech--A Critical Appraisal
## 3 Review of Selected Works
### Study Assessment Premises  
The criteria taken into account in the selected studies on voice laryngeal pathology characterization listed in Table 2 aim to answer what a study in laryngeal pathology assessment is expected to provide, whether based on MD or DD approaches regarding the following premises:  
**Trustworthiness**: A scientific study is required to provide a clear description of experimental settings for third parties to be able to check results by manifesting the study hypotheses and describing the methodology in easily understandable means, either as formal, mathematical, or open code descriptions, or providing them as Supplementary Data or in code repositories. The study should make use of standard databases well-accepted by the research community, at least for reference purposes, even if proprietary databases were used. **Gender issues**: Given the nature of voice, the data subsets used in the studies should be well-balanced and populated, both in gender and age, matching paired distributions between normative and pathological subsets, whereas experiments should consider gender-separated studies or gender-sensitive feature-normalization procedures. This strong requirement is based on clear reasons: human voice acoustic characteristics are strongly dependent on sexual hormone activity throughout the lifetime, especially in the case of the female voice (childhood, puberty, menstrual period, pregnancy, menopause [45; 46]), showing strong differences in phonation and articulation concerning males, who on their counterpart also experience strong changes during lifetime [47] (from childhood to third age with a remarkable transition during puberty [48]). An example of pathology detection degradation when gender separation was not taken into account is reported in [49]. If studies do not consider separate samples and populations by gender and age, there is \begin{table}
\begin{tabular}{c c c} \hline \hline
**Criteria/Orientation** & **Data-Driven** & **Model-Driven** \\ \hline Performance & High & High \\ Training & High & Moderate \\ Generalization & Low 1  & Moderate \\ Semantic power & Low & High \\ Explainability & Low & High \\ Clinical acceptance & Low & Moderate 2  \\ Computational costs & Large & Low \\ Portability & Poor & Moderate \\ Robustness & High & Poor \\ Development Effort & Small 3  & Moderate \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of DD vs. MD approaches. Observations: 1 If contaminated by training data (see [8]). 2 Assuming that results meet specific clinical criteria. 3 When using transfer learning (TL) with little adaptation needs. the risk that normative male features might be taken as non-normative or pathological ones when classified under the female normative feature distributions, and vice-versa, introducing important bias affecting classification score trustworthiness, and hampering acceptability by clinicians and users: \"The design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes\" [50]. Taking this matter into account, governments and regulatory agencies are producing important legal requirements to adopt the best methodologies and practices for AI and ML in medical products and services to be respectful of gender bias issues (see [51] for a comprehensive review). **Statistical relevance**: Study hypotheses should be stated in statistical terms and methods, declaring confidence intervals, providing supporting statistical tests, and \(p\)-values when possible and if meaningful. For instance, in binary classification cases, results could be presented in terms of ROC curves handling small-stride threshold fixing if possible (on smooth many-point curves). Multiple-class separation problems could use performance metrics such as Cohen's Kappa, or multi-class F-measures [52; 53; 54].