# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
The project Spain-RDR (Spanish Rare Diseases Registries Research Network) aims to build the National Registry of Rare Diseases in Spain, coordinated by the Institute of Rare Diseases Research and involving collaboration from various healthcare departments, patient organizations, medical societies, research networks, and the pharmaceutical industry [21]. Spain-RDR focuses on patient-oriented research outcome registries and population-based registries for epidemiological and socio-health research and health planning. Organizations like NORD, Rare Diseases International (RDI), European Organisation for Rare Diseases (EURORDIS) [22], or Spanish Federation of Rare Diseases (FEDER) advocate for increased research funding and support for rare disease patients. Rare diseases often have a profound impact on quality of life, requiring ongoing medical care and management. Early diagnosis is vital for less common genetic and neurodevelopmental syndromes like Williams, Prader-Willi, Fragile X, Smith-Magenis, and Angelman syndromes, among many others. Delayed diagnosis poses a significant challenge, resulting in frustrating and prolonged (mis)diagnosis/accurate diagnosis for families. This delay can lead to emotional distress and hinder access to appropriate treatments, which are crucial for improving a patient's quality of life. Conventional diagnostic methods for genetic syndromes face limitations, driving the search for faster, accessible, and cost-effective alternatives. Voice analysis using advanced deep learning methods emerges as a promising solution. While initially developed for neurodegenerative disorders with severe dysarthria, these methods show potential for detecting subtle phonation deviations in genetic syndromes with milder dysarthria [23; 24; 25]. By leveraging deep learning's capacity to analyze complex datasets, these models can identify and utilize fined phonatory differences [26; 27]. Such advancements promise earlier and more accurate genetic syndrome diagnoses, potentially transforming the lives of patients and their families [28; 29; 30]. Neurodevelopmental delays resulting from chromosomal abnormalities are a primary cause of distinct phonation patterns observed in syndromes. Variations among syndromes are further influenced by additional clinical factors affecting laryngeal biomechanics. Examples include elastin deficiency in [PERSON] syndrome and heightened laryngeal tension in [PERSON] syndrome [24; 31]. This research work explores the potential of voice analysis, specifically phonation patterns, as a classification tool for genetic syndromes. The core idea lies in the possibility that distinct genetic syndromes manifest with unique phonatory characteristics, similar to how [PERSON]'s disease is diagnosed through voice analysis [32]. Here, deep learning emerges as a powerful ally. By training neural networks on datasets of voice samples from patients with a genetic syndrome, a network could learn to identify the specific phonatory signatures of the disease. Ideally, this possibility could lead to classifying a particular syndrome based on a single voice recording. Likewise, this study represents a pioneering step towards this ambitious goal. There are some gaps where the research can contribute valuable insights into the application of noninvasive deep learning techniques for analyzing voice characteristics in individuals with SMS, to enhance the understanding and the development of different treatment techniques. Some of these are the following:  
1. The limited research on SMS voice characteristics. This gap highlights the need for more comprehensive research to understand the peculiarities of the patients [30]. 2. The lack of noninvasive deep learning applications using phonation data. Research on applying these techniques to SMS voice features is lacking, offering a chance to explore noninvasive deep learning for vocal data analysis [33]. 3. Integration of deep learning with clinical practice. There is a gap in using deep learning models in clinical practice for SMS identification or classification.