Depression is a major psychological disorder with a growing impact worldwide. Traditional methods for detecting the risk of depression, predominantly reliant on psychiatric evaluations and self-assessment questionnaires, are often criticized for their inefficiency and lack of objectivity. Advancements in deep learning have paved the way for innovations in depression risk detection methods that fuse multimodal data. This paper introduces a novel framework, the Audio, Video, and Text Fusion-Three Branch Network (AVTF-TBN), designed to amalgamate auditory, visual, and textual cues for a comprehensive analysis of depression risk. Our approach encompasses three dedicated branches—Audio Branch, Video Branch, and Text Branch—each responsible for extracting salient features from the corresponding modality. These features are subsequently fused through a multimodal fusion (MMF) module, yielding a robust feature vector that feeds into a predictive modeling layer. To further our research, we devised an emotion elicitation paradigm based on two distinct tasks—reading and interviewing—implemented to gather a rich, sensor-based depression risk detection dataset. The sensory equipment, such as cameras, captures subtle facial expressions and vocal characteristics essential for our analysis. The research thoroughly investigates the data generated by varying emotional stimuli and evaluates the contribution of different tasks to emotion evocation. During the experiment, the AVTF-TBN model has the best performance when the data from the two tasks are simultaneously used for detection, where the F1 Score is 0.78, Precision is 0.76, and Recall is 0.81. Our experimental results confirm the validity of the paradigm and demonstrate the efficacy of the AVTF-TBN model in detecting depression risk, showcasing the crucial role of sensor-based data in mental health detection.

Background: Depression affects people’s daily lives and even leads to suicidal behavior. Text-based depression estimation using natural language processing has emerged as a feasible approach for early mental health screening. However, most existing reviews often included studies with weak depression labels, which affected the reliability of the results and further limited the practical application of the automatic depression estimation models. Objective: This review aimed to evaluate the predictive performance of text-based depression models that used standard labels, and to identify text resources, text representation, model architecture, annotation source, and reporting quality contributing to performance heterogeneity. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, we systematically searched 4 main databases (PubMed, Scopus, IEEE Xplore, and Web of Science) for studies published between 2014 and 2025. The eligible studies were included: machine learning models were developed based on the text generated by the participants and used validated scales or clinical diagnoses as depression labels. Pooled effect sizes (r) were calculated using random-effects meta-analysis with Hartung-Knapp-Sidik-Jonkman correction, and subgroup and meta-regression analyses were conducted to explore potential moderators. Results: We scanned 3067 articles and finally filtered 15 models from 11 studies for the meta-analysis. The overall pooled effect size was 0.605 (95% CI 0.498-0.693), indicating a large strength of association. Subgroup analyses showed that models using embedding-based text representations achieved higher performance than those using traditional features (r=0.741, 95% CI 0.648-0.812 vs r=0.514, 95% CI 0.385-0.623; P<.001 for subgroup difference), and deep learning architectures outperformed shallow models (r=0.731, 95% CI 0.660-0.789 vs r=0.486, 95% CI 0.352-0.599; P<.001). Models trained with clinician diagnoses also outperformed better than those relying on self-report scales (r=0.688, 95% CI 0.554-0.787 vs r=0.500, 95% CI 0.340-0.631; P=.03). Reporting quality was positively associated with model performance (β=0.085, 95% CI 0.050-0.119; P<.001). Begg–Mazumdar and Egger tests provided no evidence of small-study effects. Begg–Mazumdar test (Kendall τ=0.17143, P=.37) and the Egger test (t14=1.13401, 2-tailed P=.28) indicated no evidence of small-study effects. Conclusions: Text-based depression estimation models trained with standard depression labels demonstrate solid predictive performance, with embedding features, deep model architectures, and clinician diagnosis labels showing significantly higher performance. Transparent reporting is also positively associated with model performance. This study highlights the importance of standard labels, feature representation, and reporting quality for improving model reliability. Unlike prior reviews that included weak or heterogeneous depression labels, this study offers more clinically reliable and comparable evidence. Moreover, this review provides clearer methodological guidance for developing more consistent and practically informative text-based depression screening models. Trial Registration: PROSPERO CRD420251056902; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251056902

BACKGROUND Depression affects people’s daily lives and even leads to suicidal behavior. Text-based depression estimation using natural language processing has emerged as a feasible approach for early mental health screening. However, most existing reviews often included studies with weak depression labels, which affected the reliability of the results and further limited the practical application of the automatic depression estimation models. OBJECTIVE This review aimed to evaluate the predictive performance of text-based depression models that used standard labels, and to identify text resources, text representation, model architecture, annotation source, and reporting quality contributing to performance heterogeneity. METHODS Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, we systematically searched 4 main databases (PubMed, Scopus, IEEE Xplore, and Web of Science) for studies published between 2014 and 2025. The eligible studies were included: machine learning models were developed based on the text generated by the participants and used validated scales or clinical diagnoses as depression labels. Pooled effect sizes (r) were calculated using random-effects meta-analysis with Hartung-Knapp-Sidik-Jonkman correction, and subgroup and meta-regression analyses were conducted to explore potential moderators. RESULTS We scanned 3067 articles and finally filtered 15 models from 11 studies for the meta-analysis. The overall pooled effect size was 0.605 (95% CI 0.498-0.693), indicating a large strength of association. Subgroup analyses showed that models using embedding-based text representations achieved higher performance than those using traditional features (r=0.741, 95% CI 0.648-0.812 vs r=0.514, 95% CI 0.385-0.623; P<.001 for subgroup difference), and deep learning architectures outperformed shallow models (r=0.731, 95% CI 0.660-0.789 vs r=0.486, 95% CI 0.352-0.599; P<.001). Models trained with clinician diagnoses also outperformed better than those relying on self-report scales (r=0.688, 95% CI 0.554-0.787 vs r=0.500, 95% CI 0.340-0.631; P=.03). Reporting quality was positively associated with model performance (β=0.085, 95% CI 0.050-0.119; P<.001). Begg–Mazumdar and Egger tests provided no evidence of small-study effects. Begg–Mazumdar test (Kendall τ=0.17143, P=.37) and the Egger test (t14=1.13401, 2-tailed P=.28) indicated no evidence of small-study effects. CONCLUSIONS Text-based depression estimation models trained with standard depression labels demonstrate solid predictive performance, with embedding features, deep model architectures, and clinician diagnosis labels showing significantly higher performance. Transparent reporting is also positively associated with model performance. This study highlights the importance of standard labels, feature representation, and reporting quality for improving model reliability. Unlike prior reviews that included weak or heterogeneous depression labels, this study offers more clinically reliable and comparable evidence. Moreover, this review provides clearer methodological guidance for developing more consistent and practically informative text-based depression screening models. CLINICALTRIAL PROSPERO CRD420251056902; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251056902