Modern and promising methods of diagnostic post-traumatic stress disorder (scientific review)

Relevance. Post-traumatic stress disorder (PTSD) is a severe mental condition that occurs after traumatic events and leads to persistent disturbances in emotional, cognitive, and behavioral functioning. The importance of its diagnosis is increasing in the context of socioeconomic crises, military conflicts, and the increasing number of victims of violence. Late detection of PTSD has serious consequences: somatic diseases, decreased ability to work, social maladjustment, and suicidal risk. Objective: To analyze current and emerging diagnostic methods for post-traumatic stress disorder.

Results. The main part of the review examines current diagnostic methods for PTSD: 1. Traditional clinical questionnaires and interviews, which have high validity but are limited by subjectivity and time consumption; 2. PTSD biomarkers: cortisol, GABA, proinflammatory cytokines, as well as neuroimaging (fMRI, EEG) and electrophysiological (skin conductance, heart rate variability) measurements; 3. Detection of attentional bias toward threatening stimuli through eye movement and pupil diameter analysis using eye tracking; 4. Use of digital diagnostic platforms (PTSD Coach, Coviu) for remote screening and monitoring of PTSD symptoms; 5. Machine learning models analyzing text, EEG, and genetic data to improve diagnostic accuracy.

Conclusions. Prospects for the development of PTSD diagnostics are linked to the integration of multimodal approaches, including biosensors for continuous cortisol monitoring, portable neuroimaging systems (infrared spectroscopy), and personalized AI-based algorithms. This also requires addressing methodological challenges such as standardization of methods, interpretability of models, and ethical considerations.

Improving PTSD diagnostics requires a combination of traditional and innovative methods to improve early detection, treatment, and patient quality of life.

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