Artificial Intelligence Clinical Trial
Official title:
Correlation of Audiovisual Features With Clinical Variables and Neurocognitive Functions in Bipolar Disorder, Mania
NCT number | NCT03857438 |
Other study ID # | BipolarAI |
Secondary ID | |
Status | Completed |
Phase | |
First received | |
Last updated | |
Start date | September 30, 2016 |
Est. completion date | July 8, 2017 |
Verified date | February 2019 |
Source | Istanbul Saglik Bilimleri University |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
The aim of this study is to show the physiological changes during manic episode in bipolar mania how much they differentiate from remission and healthy control. Relation of audio-visual features as physiological changes and cognitive functions and clinical variables will be searched. The aim is to find biologic markers for predictors of treatment response via machine learning techniques to be able to reduce treatment resistance and give an idea for personalized treatment of bipolar patients.
Status | Completed |
Enrollment | 89 |
Est. completion date | July 8, 2017 |
Est. primary completion date | February 20, 2017 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | 18 Years to 65 Years |
Eligibility |
Inclusion Criteria: - diagnosis of BD type I, manic episode according to DSM-5 [10] given by the following doctor, - being informed of the purpose of the study and having given signed consent before enrollment. Exclusion Criteria: - being younger than 18 years or older than 60 years, - showing low mental capacity during the interview - expression of hallucinations and disruptive behaviors during the interview, - presence of severe organic disease, - presence of any organic disease that may affect cognition - having less than five years of public education - diagnosis of substance or alcohol abuse in the last three months (except nicotine and caffeine) - presence of cerebrovascular disorder, head trauma with longer duration of loss of consciousness, severe hemorrhage and dementia, - having electroconvulsive therapy in the last one year. For the healthy control group, the following additional criteria were considered for exclusion - presence of family history of mood or psychotic disorder, - presence of psychiatric disorder during interview or in the past. |
Country | Name | City | State |
---|---|---|---|
Turkey | SBU Erenkoy Mental State Hospital | Istanbul |
Lead Sponsor | Collaborator |
---|---|
Istanbul Saglik Bilimleri University | Bosphorus University, Namik Kemal University |
Turkey,
Çiftçi E, Kaya H, Güleç H and Salah AA Potential audio treatment predictors for bipolar mania Psychiatry and Clinical Psychopharmacology Supplementary
Ciftci E, Kaya H, Gulec H, Salah AA (2018) The Turkish Audio-Visual Bipolar Disorder Corpus. In: 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia), pp 1-6. IEEE. Available at: https://ieeexplore.ieee.org/document/8470362/
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Treatment response | The proportion of Young Mania Rating Scale(YMRS) score ( at baseline to 3rd- 7th- 14th- 28th day and 3rd month ( Baseline scale/ Follow-up day scale) YMRS score utilized rating scales to assess manic symptoms ranged between 0-76 Remission: Yt <= 7 Hypomania: 7 < Yt < 20 Mania: Yt >= 20. |
from baseline until 3rd month | |
Primary | Changes in visual features | Functionals of appearance descriptors extracted from fine-tuned Deep Convolutional Neural Networks (DCNN), geometric features obtained using tracked facial landmarks (Unweighted Average Recall) Geometric frame level 23 geometric features and apperance descriptors 4096 dimensional features from the last convolutional layer of the FER fine-tuned CNN which are summarized via mean and range functionals over sub-clips and the decisions are voted at video level, an UAR performance is obtained. Feature vectors extracted from video is modelled using Partial Least Squares (PLS) regression and Extreme Learning Machines classifiers Unweighted Average Recall (UAR), which is mean of class-wise recall scores, is commonly used as performance measure, instead of accuracy, which can be misleading in the case of class-imbalance |
Baseline and 3rd month | |
Primary | Changes in audio features | Functionals of acoustic features extracted via openSMILE tool (Unweighted Average Recall) Acoustic low level descriptors including prosody (energy, Fundamental Frequency - F0), voice quality features (jitter and shimmer), Mel Frequency Cepstral Coefficients, which are commonly used in many speech technologies from audio, we use the 76-dimensional standard feature set used in the INTERSPEECH 2010 paralinguistic challenge as baseline. The second is our proposed set of 10 functionals, Mean, standard deviation, curvature coefficient , slope and offset , minimum value and its relative position, maximum value and its relative position, and the range Feature vectors extracted from audio is modelled using Partial Least Squares (PLS) regression and Extreme Learning Machines classifiers. |
Baseline and 3rd month | |
Primary | in Stop Signal Test | (milisecond) SST- Succesful Stop Ratio SST- go- Reaction Time SST- Stop Signal Delay SST- Stop Signal Reaction Time SST- Total Correct | Baseline and 3rd month | |
Primary | Changes in Rapid Visual Processing | RVP A' (A prime) is the signal detection measure of sensitivity to the target, regardless of response tendency (range 0.00 to 1.00; bad to good). RVP B'' (B double prime) is the signal detection measure of the strength of trace required to elicit a response (range -1.00 to +1.00) |
Baseline and 3rd month | |
Primary | in Cambridge Gambling Task | (milisecond) CGT Quality of decision making CGT Deliberation time CGT Delay aversion CGT Overall proportion bet | Baseline and 3rd month | |
Primary | Changes in Emotion Recognition Test | (rate of emotion prediction) Percent and numbers correct/incorrect prediction | Baseline and 3rd month |
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