Artificial Intelligence Screening for Body Dysmorphic Disorder in Aesthetic Surgery: Improving Patient Safety and Outcomes
Conditions: Body Dysmorphic Disorder
Sponsor: Bukret Plastic Surgery

Cover Page
Title: Artificial Intelligence Screening for Body Dysmorphic Disorder in Aesthetic
Surgery: Improving Patient Safety and Outcomes
●
NCT Number: [NCT Number Here]
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Document Date: August 30, 2024
●
Document Review Statement: This document was most recently updated
and reviewed by the sole researcher, Williams E. Bukret, MD, EMBA, on
August 30, 2024. As there is no human subjects protection review board
required for this study, this date reflects the most recent review and update by
the principal investigator.

Study Protocol
Introduction:
This
study
protocol
outlines
the
procedures,
objectives,
and
methodologies for the observational study titled "Artificial Intelligence Screening for
Body Dysmorphic Disorder in Aesthetic Surgery: Improving Patient Safety and
Outcomes." The primary aim is to evaluate the clinical utility of an AI-powered
screening tool for Body Dysmorphic Disorder (BDD) in patients seeking elective
aesthetic surgery.
Study Objectives
●
To evaluate the effectiveness of the AI-based screening tool in identifying
patients at risk for BDD.
●
To determine the correlation between various demographic and psychological
factors (such as age, gender, and stress levels) and positive BDD screening
results.
●
To assess the impact of early psychological intervention on surgical
decision-making and outcomes.
Study Design
This is an observational study with a prospective time perspective. The study
involves the collection of clinical and psychological data from patients seeking
elective aesthetic surgery. The AI model analyzes this data to provide risk scores
and identify patients who may benefit from psychological intervention.
Participant Eligibility
Inclusion Criteria:
●
Individuals aged 18 and older.
●
Completion of the Body Dysmorphic Disorder Questionnaire (BDDQ).
Exclusion Criteria:
●
Individuals below 18 years of age.
●
Patients who did not complete the Body Dysmorphic Disorder Questionnaire
(BDDQ).
Study Population Description

Participants are individuals seeking elective aesthetic surgery. They will complete the
BDDQ and provide demographic and psychological data, including age, gender, and
recent stress levels. The AI model will analyze this information to screen for BDD.
Data Collection: The following data will be collected from each participant:
●
Age
●
Gender
●
Stress levels
●
BDDQ scores
●
Surgical outcomes (where applicable)
●
Patient satisfaction (as measured by the Visual Analog Scale, VAS)
Study Procedures
Participants will undergo a screening assessment using the AI model, which
evaluates multiple factors based on their responses to the BDDQ and additional
psychological data. The AI model will generate risk scores and recommendations for
psychological referral if necessary.
Follow-Up
The follow-up period for this study ranges from 1 to 43 months post-surgery, with a
mean follow-up of 22 months. Data on patient satisfaction and surgical outcomes will
be collected and analyzed.
Outcome Measures
Primary Outcome Measure
●
Title: Patient Satisfaction Post-Surgery
●
Description: The primary outcome measure is the level of patient satisfaction
with the cosmetic surgery results, measured using the Visual Analog Scale
(VAS) within the follow-up period after surgery.
Secondary Outcome Measure
●
Title: Correlation Between Positive BDD Screening and Risk Factors
●
Description: This measure evaluates the correlation between a positive Body
Dysmorphic Disorder (BDD) screening result and various risk factors,
including demographic factors (age, gender), psychological stress levels, and
previous psychiatric history. The study will utilize chi-square tests to analyze
gender differences in the prevalence of BDD among the patient population.

The goal is to identify patterns that could inform better patient selection and
management in cosmetic surgery.
Conclusion
This study protocol provides a comprehensive overview of the study design,
objectives, data collection methods, and outcome measures for the observational
study "Artificial Intelligence Screening for Body Dysmorphic Disorder in Aesthetic
Surgery: Improving Patient Safety and Outcomes." The integration of AI-powered
screening is expected to improve patient selection, surgical planning, and overall
outcomes in aesthetic surgery.

Statistical Analysis Plan (SAP)
Introduction: This Statistical Analysis Plan (SAP) outlines the statistical methods
and analyses to be performed for the study titled "Artificial Intelligence Screening for
Body Dysmorphic Disorder in Aesthetic Surgery: Improving Patient Safety and
Outcomes." The primary aim of the study is to assess the effectiveness of the
AI-powered screening tool in identifying patients at risk for BDD and its impact on
surgical decision-making.
Study Objectives:
●
To evaluate the effectiveness of the AI-based screening tool in identifying
patients at risk for BDD.
●
To determine the correlation between various demographic and psychological
factors (such as age, gender, and stress levels) and positive BDD screening
results.
●
To assess the impact of early psychological intervention on surgical
decision-making and outcomes.
Study Design: This is an observational study with a prospective time perspective.
The study involves the collection of psychological and demographic data from
patients seeking elective aesthetic surgery. The AI model analyzes this data to
identify at-risk patients and provide recommendations for psychological evaluation.
Data Collection: The following data will be collected from each participant:
●
Age
●
Gender
●
Stress levels
●
BDDQ scores
●
Surgical outcomes (where applicable)
●
Patient satisfaction (as measured by the Visual Analog Scale, VAS)
Statistical Methods:
●
Descriptive Statistics:
○
Frequency and percentage of patients screening positive for BDD.
○
Analysis of demographic data and stress factors.

●
Pearson Correlation:
○
Correlation between BDD and factors such as age, gender, and stress.
Python code
# Correlation calculations
stats.pearsonr(corr4['BDD'], corr4['Age'])
stats.pearsonr(corr4['BDD'],
corr4['Stress
in
the
last
3
months'])
stats.pearsonr(corr4['BDD'], corr4['Male'])
stats.pearsonr(corr4['BDD'], corr4['Female'])
●
Chi-Square Analysis:
○
Chi-square tests will be conducted to evaluate the differences in the
prevalence of BDD between genders, helping to identify any
statistically significant associations between gender and BDD.
Python code
from scipy.stats import chi2_contingency
# Create a contingency table
contingency_table
=
pd.crosstab(tabla1['BDD'],
tabla1['Female'])
# Calculate the chi-squared test
chi2, p, _, _ = chi2_contingency(contingency_table)
#
Calculate
the ratio of Male to Female in the positive BDD
group
positive_BDD_female
=
tabla1[(tabla1['BDD']
==
1)
&
(tabla1['Female'] == 1)].shape[0]
positive_BDD_male
=
tabla1[(tabla1['BDD']
==
1)
&
(tabla1['Male'] == 1)].shape[0]

ratio_male_to_female_positive_BDD
=
positive_BDD_male
/
positive_BDD_female
#
Calculate
the ratio of Male to Female in the negative BDD
group
negative_BDD_female
=
tabla1[(tabla1['BDD']
==
0)
&
(tabla1['Female'] == 1)].shape[0]
negative_BDD_male
=
tabla1[(tabla1['BDD']
==
0)
&
(tabla1['Male'] == 1)].shape[0]
ratio_male_to_female_negative_BDD
=
negative_BDD_male
/
negative_BDD_female
# Calculate the relative risk
relative_risk
=
(positive_BDD_male
/
positive_BDD_female)
/
(negative_BDD_male / negative_BDD_female)
●
Outcome Analysis:
○
Patient satisfaction levels as measured by the Visual Analog Scale
(VAS).
●
Data Analysis:
○
Data analysis will be performed using Python in a Colab environment.
The analysis will focus on correlations between BDD and identified risk
factors, chi-square tests for gender differences, as well as the
effectiveness of the AI model in screening for BDD.
Access Instructions: To request permission to access the individual participant
data
(IPD)
and supporting information, please email drbukret@drbukret.com.
Detailed instructions for requesting access can be found at IPD Sharing Instructions.
Conclusion: This SAP provides a detailed plan for the statistical analysis of the
study "Artificial Intelligence Screening for Body Dysmorphic Disorder in Aesthetic
Surgery: Improving Patient Safety and Outcomes." The analysis aims to provide
insights into the effectiveness of the AI model in identifying and managing patients at
risk for BDD, ultimately improving surgical outcomes and patient safety.