Cognitive trajectories before and after geriatric hip fracture: a matched longitudinal analysis of the Health and Retirement Study, 1996-2016.

In the post-pandemic era, depressive disorders have become increasingly prevalent. Although their incidence varies among countries, approximately 20% of individuals are estimated to experience depressive disorders at some point in their lives, with a rising trend observed globally. The World Health Organization (WHO) predicts that by 2030, depressive disorders will emerge as the leading global cause of disease burdeny. Major depressive disorder not only affects a significant number of people but also imposes a substantial economic cost, underscoring the critical need for early diagnosis and treatment. This is particularly urgent in low- and middle-income countries, where healthcare resources are often limited. In current clinical practice, the diagnosis of depression primarily relies on patients’ self-reporting and professional clinical evaluations. However, this approach faces several challenges, including (but not limited to) a shortage of mental health professionals, inaccuracies in primary care settings, time-consuming evaluation processes, the social stigma that leads to concealed symptoms, and hesitancy to seek medical help. These limitations highlight an urgent need for novel, objective, and user-friendly diagnostic methods. Fortunately, advances in scientific research and technology have led to the discovery of several new biomarkers in recent years, offering potential targets for depression screening. Among emerging digital phenotyping approaches, speech features stand out as a promising biomarker category due to their advantages of being rapid, non-invasive, privacy-preserving, and objective. The field of speech-based depression detection has evolved significantly since its inception. The earliest systematic recognition of depression-related vocal changes can be traced back to 1921, when Emil Kraepelin observed that patients with depression often exhibited more monotonous speech, reduced sound intensity, and slower speech rate. These seminal observations laid the theoretical foundation for subsequent scientific investigations. Building upon these early insights, initial research primarily focused on handcrafted acoustic features. These pioneering studies identified unique expression patterns in the speech of individuals with depression, establishing pitch, intensity, speech rate, pauses, and spectral characteristics as crucial biomarkers for depression detection. With advances in computational methods, the advent of machine learning marked a transformative period in the field. Williamson et al. achieved a significant milestone by demonstrating the feasibility of depression severity using acoustic features. During this era, traditional machine learning approaches relied heavily on handcrafted features, including prosodic, spectral, voice quality, and linguistic measures. The main classifiers employed during this period were Support Vector Machines (SVM), Random Forest (RF), and Gaussian Mixture Models (GMM).