JMIR Publications

ABSTRACT

Background:

Current standards of psychiatric assessment and diagnostic evaluation rely primarily on the clinical subjective interpretation of a patient’s outward manifestations of their internal state. While psychometric tools can help to evaluate these behaviors more systematically, the tools still rely on the clinicians’ interpretation of what are frequently nuanced speech and behavior patterns. With advances in computing power, increased availability of clinical data, and improving resolution of recording and sensor hardware (including acoustic, video, accelerometer, infrared and other modalities), researchers have begun to demonstrate the feasibility of cutting-edge technologies in aiding the assessment of psychiatric disorders.

Objective:

We present a research protocol that utilizes facial expression, eye gaze, voice and speech, locomotor, heart rate, and electroencephalography monitoring to assess schizophrenia symptoms and to distinguish schizophrenia from other psychiatric disorders and control populations.

Methods:

We plan to recruit three outpatient groups: 1) 50 patients with schizophrenia, 2) 50 patients with unipolar major depressive disorder, and 3) 50 individuals with no prior psychiatric history. Using an internally developed semi-structured interview, psychometrically validated clinical outcome measures, and a multi-modal sensing system utilizing video, acoustic, actigraphic, heart rate, and electroencephalographic sensors, we aim to evaluate the system’s capacity in classifying subject grouping (schizophrenia, depression, or control); to evaluate the systems sensitivity to within group symptom severity; and to determine if such a system can further classify variations in disorder subtypes.

Results:

Data collection began in July 2020 and is expected to continue through December 2022.

Conclusions:

If successful, this study will help advance current progress in the merger between state-of-the-art technology in aiding clinical psychiatric assessments and treatment. If our findings suggest these technologies to be capable of resolving diagnosis and symptoms to the level of current psychometric testing/clinician judgment, then we would be among the first to develop a system that can eventually be used by clinicians to more objectively diagnose and assess schizophrenia and depression, with the possibility of less risk of bias. Such a tool has the potential to improve accessibility to care, to aid clinicians in objectively evaluating diagnosis, severity of symptoms, and treatment efficacy through time, and to reduce treatment related morbidity.