Open AccessLanguage, Speech, and Facial Expression Features for Artificial Intelligence–Based Detection of Cancer Survivors’ Depression: Scoping Meta-Review
Author(s) -
Urška Smrke,
Izidor Mlakar,
Simon Lin,
Bojan Musil,
Nejc Plohl
Publication year - 2021
Publication title -
jmir mental health
Language(s) - English
Resource type - Journals
ISSN - 2368-7959
DOI - 10.2196/30439
Subject(s) - context (archaeology) , depression (economics) , psychology , facial expression , thematic analysis , inclusion (mineral) , meta analysis , artificial intelligence , computer science , clinical psychology , medicine , qualitative research , social psychology , paleontology , social science , sociology , biology , economics , macroeconomics
Background Cancer survivors often experience disorders from the depressive spectrum that remain largely unrecognized and overlooked. Even though screening for depression is recognized as essential, several barriers prevent its successful implementation. It is possible that better screening options can be developed. New possibilities have been opening up with advances in artificial intelligence and increasing knowledge on the connection of observable cues and psychological states. Objective The aim of this scoping meta-review was to identify observable features of depression that can be intercepted using artificial intelligence in order to provide a stepping stone toward better recognition of depression among cancer survivors. Methods We followed a methodological framework for scoping reviews. We searched SCOPUS and Web of Science for relevant papers on the topic, and data were extracted from the papers that met inclusion criteria. We used thematic analysis within 3 predefined categories of depression cues (ie, language, speech, and facial expression cues) to analyze the papers. Results The search yielded 1023 papers, of which 9 met the inclusion criteria. Analysis of their findings resulted in several well-supported cues of depression in language, speech, and facial expression domains, which provides a comprehensive list of observable features that are potentially suited to be intercepted by artificial intelligence for early detection of depression. Conclusions This review provides a synthesis of behavioral features of depression while translating this knowledge into the context of artificial intelligence–supported screening for depression in cancer survivors.