AI helps detect early signs of alcoholism in firefighters with 80% accuracy – News-Medical

Report on a Multimodal Deep Learning Framework for Alcohol Use Disorder Risk Assessment in Firefighters

Executive Summary

A recent study demonstrates the successful application of a multimodal deep learning model for the objective screening of Alcohol Use Disorder (AUD) risk among active-duty firefighters. By integrating neuroimaging data with neuropsychological assessments, the model achieves approximately 80% accuracy, representing a significant advancement in occupational health. This innovation directly supports the achievement of several United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being), SDG 8 (Decent Work and Economic Growth), and SDG 11 (Sustainable Cities and Communities), by providing a tool to protect the mental health and operational readiness of essential public safety personnel.

1.0 Introduction: Occupational Health as a Pillar of Sustainable Development

Firefighters are exposed to chronic and cumulative trauma, placing them at a disproportionately high risk for mental health disorders, including AUD. This occupational hazard poses a direct challenge to SDG 3, which aims to ensure healthy lives and promote well-being, and its specific Target 3.5, focused on strengthening the prevention and treatment of substance abuse. Traditional self-reporting screening methods are often ineffective due to professional and social stigma, hindering early intervention. This report details an objective, technology-driven screening method that aligns with SDG 8 by promoting safe and secure working environments for all workers and supports SDG 11 by ensuring the resilience and effectiveness of emergency responders who are critical to community safety.

2.0 Research Methodology

The cross-sectional study utilized a novel machine learning framework to analyze data from a national cohort of public safety professionals.

2.1 Participant Cohort and Data Collection

The study involved a cohort of 689 active-duty firefighters in South Korea. Data was collected through a multimodal approach, ensuring a comprehensive dataset for analysis. The primary data sources were:

1. Structural Neuroimaging: T1-weighted structural Magnetic Resonance Imaging (MRI) scans were used to capture brain morphology.
2. Neuropsychological Assessments: Standardized tests, including the Grooved Pegboard Test (visual-motor coordination) and the Trail Making Test (executive function), provided data on neurological function.
3. Clinical Screening: The World Health Organization’s Alcohol Use Disorder Identification Test (AUDIT) was used to stratify participants into risk categories.

2.2 Deep Learning Model Architecture

A cooperative fusion model was designed to integrate the different data types for a synergistic analysis. The architecture included:

• ResNet-50: A convolutional neural network to extract localized morphological patterns from brain scans.
• Vision Transformer (ViT): A module to identify global, broad-based anatomical relationships within the brain.
• Multilayer Perceptron (MLP): A network to identify patterns within the numerical clinical and neuropsychological data.

3.0 Key Findings and Performance Analysis

The integrated multimodal model demonstrated superior performance in identifying firefighters at high risk for AUD, contributing a valuable tool for advancing health and safety in high-stress occupations.

3.1 Predictive Accuracy

The combined model achieved approximately 80% accuracy in classifying AUD risk. This represents a 17-percentage-point improvement over unimodal approaches, where both clinical-only and neuroimaging-only models performed at approximately 62% accuracy. This enhanced performance underscores the value of data synergy over simple data addition.

3.2 Identification of Predictive Biomarkers

Interpretability analysis revealed that the model identified clear, biologically significant patterns. The most critical predictive features were:

• Sex: Highlighting known sex-specific variations in alcohol neurotoxicity and addiction pathways.
• Motor Coordination: Performance on the Grooved Pegboard Test, particularly with the non-dominant hand, emerged as a sensitive indicator of early neurological changes associated with AUD.

4.0 Implications for Sustainable Development Goals (SDGs)

The development and application of this objective screening tool have profound implications for several SDGs by addressing the health, safety, and sustainability of critical workforces and the communities they serve.

4.1 Advancing SDG 3: Good Health and Well-being

The model provides a pragmatic pathway for early detection and intervention for AUD, directly supporting Target 3.5. By replacing stigmatizing self-report questionnaires with an objective, biological-based screening, it facilitates proactive mental health support for at-risk populations.

4.2 Promoting SDG 8: Decent Work and Economic Growth

This research contributes to Target 8.8, which calls for the protection of labor rights and the promotion of safe and secure working environments. Ensuring the mental and physical fitness of firefighters reduces occupational incidents, enhances team safety, and maintains operational readiness, thereby fostering a more productive and secure workforce.

4.3 Strengthening SDG 11 and SDG 16: Sustainable Cities and Strong Institutions

Firefighters are a cornerstone of resilient communities (SDG 11) and effective public institutions (SDG 16). By safeguarding their well-being, this technology helps ensure that these essential services remain robust and capable of protecting communities from disasters and emergencies, thus contributing to urban and institutional sustainability.

5.0 Conclusion and Recommendations

The multimodal deep learning framework offers a significant step forward in the objective screening for AUD in high-risk professions. Its successful implementation provides a scalable solution that can be integrated into routine medical examinations to protect vulnerable workers. This aligns technological innovation (SDG 9) with critical public health needs, creating a safer and more sustainable environment for both workers and the communities they protect. Future research should focus on larger, longitudinal studies to refine predictive accuracy for prospective risk and conduct cost-effectiveness analyses to facilitate widespread adoption in high-stress occupational settings globally.

1. Which SDGs are addressed or connected to the issues highlighted in the article?

1. SDG 3: Good Health and Well-being

   The article directly addresses this goal by focusing on mental health and substance abuse, specifically Alcohol Use Disorder (AUD), among firefighters. It highlights the high risk of mental health disorders in this profession due to chronic stress and trauma and introduces a new technological approach for early screening and prevention.

2. SDG 8: Decent Work and Economic Growth

   The article connects to this goal by focusing on the occupational health and safety of firefighters. It discusses how AUD, a consequence of their high-stress work environment, “poses a serious risk to individual firefighters and their teams.” By developing a tool to mitigate this risk, the study contributes to creating a safer and more secure working environment for this high-risk profession.

3. SDG 9: Industry, Innovation, and Infrastructure

   This goal is relevant as the core of the article is about technological innovation and scientific research. The study presents a “novel multimodal machine learning framework” that uses AI, deep learning, and neuroimaging to solve a critical health problem. This represents an advancement in scientific research and the application of technology to improve health diagnostics.

2. What specific targets under those SDGs can be identified based on the article’s content?

• Target 3.5: Strengthen the prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcohol.

  This target is central to the article. The entire study is designed to “screen firefighters for high AUD risk” and predict the “harmful use of alcohol.” The development of an objective screening method is a direct effort to strengthen the prevention of substance abuse in a vulnerable population.

• Target 8.8: Protect labour rights and promote safe and secure working environments for all workers.

  The article’s focus on mitigating a key occupational hazard for firefighters aligns with this target. The text notes that AUD can lead to “dangerous behaviors” and a “greater likelihood of traumatic incidents” on the job. The AI model is presented as a tool to improve occupational safety by identifying at-risk individuals before severe incidents occur, thus promoting a safer work environment.

• Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries… encouraging innovation.

  The study described in the article is a clear example of this target in action. It details the development and successful application of a “multimodal deep learning model” that integrates “structural MRI neuroimaging data, combined with standardized neuropsychological tests.” This represents an enhancement of scientific research and the application of advanced technology (AI) to the field of mental health diagnostics.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

• Indicator for Target 3.5: Prevalence of Alcohol Use Disorder (AUD)

  The article provides a direct measurement related to this indicator. In the study cohort, participants were classified based on their alcohol consumption, with the results showing that “Participants were stratified into those with alcohol risk and non-alcohol risk, comprising 57% and 43%, respectively.” This percentage serves as a baseline indicator of the prevalence of harmful alcohol use within this specific group, which new prevention methods aim to reduce.

• Indicator for Target 8.8: Rate of severe occupational incidents

  The article implies this indicator when discussing the clinical and economic value of the screening tool. It states, “it would require 150 screens to prevent one severe occupational incident at cost-effective levels.” This directly links the screening intervention to the prevention of measurable negative workplace events, making the rate of such incidents a key performance indicator for occupational safety.

• Indicator for Target 9.5: Accuracy and efficiency of new diagnostic technologies

  The article provides specific metrics to measure the success of the innovation. The model’s performance is quantified: “The multimodal system classified firefighters at risk for AUD with ~80% accuracy.” It also highlights its improvement over existing methods, noting “a 17-percentage-point improvement in AUD prediction over either clinical-only or neuroimaging-only.” These accuracy rates are direct indicators of technological advancement and capability.

4. Table of SDGs, Targets, and Indicators

Source: news-medical.net