Voice feature analysis is one of the earliest and most mature research directions in intelligent depression detection. Cummins et al. [19] systematically summarized the application of voice analysis in depression assessment and found that the voice of depressed patients has obvious changes in prosodic features, including a decrease in fundamental frequency (F0), a weakening of voice energy, and a slowdown in speech rate. These features can be objectively quantified through digital signal processing technology. Low et al. [10] demonstrated how to use natural language processing technology to analyze voice features and verified that these features have a significant correlation with the severity of depressive symptoms. In addition to basic acoustic features, more complex features are also included.

Facial expressions are important external manifestations of emotional states. Depressed patients often exhibit reduced facial expressions and dull emotional expressions. The facial expression analysis system developed by Ringeval et al. [12] can track the movement of key facial feature points in real-time. By analyzing the spatio-temporal change patterns of these feature points, the facial expression features of depressed patients can be effectively identified. The system proposed by Ringeval et al. [12] can not only track the movement of facial key points but also analyze micro-expression changes and emotional transition processes. This study found that depressed patients have specific features in the dynamic change patterns of facial expressions, such as delayed expression transitions and the duration of emotional expressions. Facial expression analysis can also be combined with temporal information for dynamic modeling.

Physiological signal analysis provides an important basis for the objective assessment of depression. Cai et al. [14] demonstrated a depression detection method based on electroencephalogram (EEG) signals and verified the value of physiological signal analysis in depression diagnosis. Jacobson et al. [15] explored the application of digital biomarkers in emotional disorder assessment and demonstrated the advantages of wearable devices in continuously monitoring physiological indicators. These studies provide new technical means for the dynamic assessment of depressive states.

Social media behavior analysis provides a new research perspective for depression detection. De Choudhury et al. [11] developed a depression prediction model based on social media by analyzing users' posting content, interaction behavior, and activity patterns. Tadesse et al. [17] specifically studied depression-related posts on the Reddit platform and achieved high detection accuracy through natural language processing technology, verifying the feasibility of social media data analysis in depression screening. Guntuku et al. [16] systematically summarized the application of social media data in mental health assessment and demonstrated the potential of this method in early depression screening. Additionally, De Choudhury et al. [29] explored the use of social media as a measurement tool for assessing depression prevalence in populations, demonstrating its potential for large-scale mental health monitoring through behavioral pattern analysis.

Multimodal feature fusion aims to comprehensively utilize feature information from different sources to improve the comprehensiveness and accuracy of detection. From an algorithmic perspective, multimodal feature fusion mainly includes two strategies: feature-level fusion and decision-level fusion. Yang et al. [18] proposed a decision-level fusion method based on decision trees. This method first processes and predicts audio, visual, and language features separately, and then integrates the PHQ - 8 score prediction results of each modality and participant features (such as PTSD diagnosis, sleep status, etc.) through a gender - specific decision tree model [18].

The development of AI-based depression detection systems relies heavily on high-quality, diverse datasets. The DAIC-WOZ dataset [40] contains clinical interviews with 189 participants, providing audio, video, and text data along with PHQ-8 depression scores, serving as a gold standard for multimodal depression research. The AVEC challenge datasets [12] offer annually updated resources with synchronized audio-visual recordings specifically designed for affective computing research.

Table 1

Commonly used depression prediction datasets.

As summarized in Table 1, more recently, the D-VLOG dataset [31] has emerged as a valuable resource, containing 961 video blogs (approximately 160 hours) from 816 subjects in naturalistic settings, offering a more ecologically valid alternative to laboratory-collected data. The Pittsburgh Depression Dataset [32] includes 49 subjects with 130 audio-visual samples recorded during clinical interviews, with expert annotations of depression severity.

For physiological signal analysis, several datasets are available: MODMA [41] provides 53-channel EEG recordings from clinically diagnosed patients and matched controls; DEAP [33] contains EEG and peripheral physiological signals from 32 participants during emotion elicitation; and SEED [34] offers EEG data specifically collected for emotion and depression analysis.

Text-based depression research is supported by datasets such as the eRisk CLEF collection [35], which contains early risk detection data from social media platforms; the Reddit Depression Corpus [42], which includes user posts from depression-related subreddits; and various Twitter depression datasets [36] that enable large-scale analysis of linguistic patterns.

Emotion-focused datasets that support depression research include EmotionLines [37] (containing 29,245 labeled utterances from 2,000 dialogues), IEMOCAP [38] (Interactive Emotional Dyadic Motion Capture Database), and MELD [39] (Multimodal EmotionLines Dataset), which provide emotional annotations valuable for understanding affective patterns in depression.

These diverse datasets collectively address different aspects of depression manifestation while presenting unique challenges in data quality, annotation consistency, and ethical considerations. Researchers must carefully select appropriate datasets based on their specific research questions and methodological approaches.

Conventional machine learning algorithms established the methodological groundwork for early depression detection research. Yang et al. [1] demonstrated the efficacy of support vector machines (SVM) in depression diagnosis using electroencephalography (EEG) data. Their approach employed a radial basis kernel function to effectively map high-dimensional, non-linear EEG features into a more discriminative feature space. Through systematic grid search optimization of the penalty factor C and kernel parameter γ, they achieved an optimal balance between model complexity and generalization capability.

In another significant application, Sau and Bhakta [28] implemented artificial neural networks (ANN) to predict depression among elderly residents in resource-limited settings in Kolkata, India. Their innovative approach integrated standardized clinical assessment tools with multimodal feature analysis. To overcome challenges associated with feature heterogeneity, they developed a hierarchical encoding framework that normalized diverse input data types and applied one-hot encoding where appropriate. Their model architecture incorporated dropout regularization to mitigate overfitting risks, resulting in a computationally efficient prediction system particularly suited for community-based mental health screening programs. Furthermore, a meta-analysis by Lee et al. [27] systematically reviewed the application of machine learning algorithms in predicting therapeutic outcomes for depression, highlighting their potential in enhancing clinical decision-making by identifying effective treatment strategies.

Deep learning has transformed depression detection through its powerful feature extraction and representation learning capabilities. Low et al. [10] advanced this field by applying BERT (Bidirectional Encoder Representations from Transformers) to analyze linguistic patterns in social media content. Their approach fine-tuned pre-trained language models on depression-specific texts, enabling the identification of subtle linguistic markers of depression that traditional methods might miss. The contextual understanding provided by these models significantly improved detection accuracy over conventional techniques. Guntuku et al. [13] further developed specialized frameworks for mental health monitoring on social platforms. Their approach combined convolutional neural networks (CNNs) with recurrent neural networks (RNNs) to capture both semantic content and temporal patterns in user-generated text. This architecture effectively detected changes in linguistic patterns that often precede clinical depression symptoms.

Multimodal deep learning has shown particular promise by integrating diverse data sources—audio, visual, and physiological signals—to create comprehensive representations of depressive symptoms. Yang et al. [18] demonstrated that neural networks could effectively combine features from speech, facial expressions, and interview content, outperforming single-modality approaches. Attention mechanisms represent another significant advancement, enhancing both model performance and interpretability. Knyazev et al. [21] provided insights into how these mechanisms improve generalization in neural networks, with important implications for depression detection systems. These approaches not only improve accuracy but also help clinicians understand which features influence the model's decisions—critical for clinical adoption. The ongoing development of explainable AI techniques continues to improve transparency, building trust and facilitating integration of these tools into clinical practice.

To address clinical implementation challenges, researchers have pursued targeted algorithm optimizations that balance performance with practical constraints. Roy et al. [22] conducted a comprehensive review of deep learning applications in EEG signal analysis, identifying key optimization strategies for neural network architectures processing neurophysiological data. Their analysis highlighted the importance of temporal convolutional networks and recurrent architectures in capturing the dynamic patterns characteristic of depression-related EEG signals, while also emphasizing computational efficiency considerations essential for real-time monitoring applications.

Kingphai and Moshfeghi [23] extended these optimization approaches to mental workload assessment using EEG signals, demonstrating novel regularization techniques and parameter optimization methods that significantly improved model robustness across diverse patient populations. Their work introduced adaptive learning rate schedules and domain-specific data augmentation strategies that effectively addressed the data scarcity challenges common in clinical depression research. These optimization techniques have broader implications for depression detection systems, particularly in resource-constrained environments where computational efficiency and model reliability are paramount concerns.

High-quality, standardized data represents the cornerstone of effective intelligent detection systems. Larson et al. [24] identified several fundamental challenges in medical data acquisition and utilization that directly impact model performance and clinical validity. These include inconsistent data collection protocols, variable annotation standards, and limited representation of diverse patient populations.

To overcome these limitations, researchers advocate for establishing unified acquisition standards and rigorous quality control processes. This approach necessitates the formation of specialized annotation teams working with comprehensive guidelines and protocols. Implementing multi-rater annotation procedures with formal consistency assessments significantly enhances the reliability of training data, which directly translates to improved model performance in clinical applications.

The reliability and robustness of depression detection technologies remain significant concerns for real-world deployment. In their comprehensive review, Shatte et al. [20] highlighted three primary technical challenges: limited model generalization across diverse populations and clinical contexts, heightened sensitivity to data noise and environmental interference, and insufficient stability in multimodal feature fusion frameworks. These limitations can lead to inconsistent performance in naturalistic settings, potentially undermining clinical trust and adoption.

Bridging the gap between technological innovation and clinical implementation presents multifaceted challenges. Kellogg and Sadeh-Sharvit [30] emphasized that successful clinical integration depends on addressing model interpretability, seamless workflow integration, and real-time performance capabilities. Clinicians require systems that not only provide accurate assessments but also offer transparent reasoning that aligns with established clinical frameworks. Well-designed AI-assisted diagnosis systems have demonstrated potential to enhance diagnostic efficiency and accuracy while providing reliable decision support, but their implementation requires careful attention to clinical workflows and practitioner needs.

The deployment of intelligent detection systems raises significant ethical considerations regarding patient privacy and data security. Larson et al. [24] proposed a comprehensive framework for protecting patient privacy in medical data utilization, emphasizing that technological innovation must proceed in parallel with robust ethical safeguards. This framework highlights the importance of informed consent, data minimization principles, and transparent algorithmic processes. Establishing sound ethical codes and privacy protection mechanisms not only safeguards patient rights but also significantly influences social acceptance and long-term technological adoption.

The widespread adoption of intelligent detection technologies necessitates careful evaluation of economic factors and implementation requirements. Huckvale et al. [26] advocated for comprehensive assessment frameworks that consider system development and maintenance costs, infrastructure requirements, training needs, and measurable clinical outcomes. Successful implementation strategies must balance technological capabilities with resource constraints, particularly in diverse healthcare settings. This requires establishing unified data standards, developing robust and interpretable algorithms, conducting rigorous clinical validation studies, implementing appropriate ethical safeguards, and creating evidence-based evaluation systems to demonstrate clinical and economic value [20, 24, 25].

The continuous innovation of artificial intelligence technology will bring new development opportunities for intelligent depression detection. In terms of data sharing and privacy protection, Graham et al. [25] emphasized the need to pay special attention to privacy protection, data security, and algorithmic bias when applying AI in the mental health field. Larson et al. [24] proposed an ethical framework for medical data sharing, emphasizing that data sharing should be part of clinical research rather than a commercial transaction.

The development of intelligent depression detection requires the deep integration of multidisciplinary knowledge. The guidance of psychological theories is crucial for improving the scientific nature of detection models. Shatte et al. [20] pointed out in a review study that combining psychological theories with machine learning methods can significantly improve the clinical practicality and interpretability of models. Cummins et al. [19] presented a depression detection method based on voice analysis in a systematic review and proposed a complete technical roadmap from voice features to clinical application. Combining this method with traditional clinical assessments can provide more objective diagnostic evidence.

Promoting the clinical application of intelligent detection technology is a key direction for future development. Graham et al. [25] showed that the application of artificial intelligence in the mental health field requires a multi - dimensional evaluation system. Technical verification should be carried out in a single - center to evaluate the basic performance and usability of the system. Through multi - center collaborative research, the applicability and consistency of the technology in different scenarios should be verified, and a standardized clinical application process and quality control system should be established.