Applications of Machine Learning in Pediatric Hydrocephalus: A Systematic Review
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Published on: October 14, 2022
Bhavya Pahwa1, Ojasvini Bali2, Sarvesh Goyal3
1Medical Student, University College of Medical Sciences and GTB Hospital, Delhi, India.
Insights
Machine learning (ML) models show promise in diagnosing and treating pediatric hydrocephalus, achieving high accuracy and faster processing. These AI tools may improve clinical decision-making and patient care outcomes.
Area of Science:
- Pediatric Neurology
- Medical Artificial Intelligence
- Computational Medicine
Background:
- Hydrocephalus affects nearly 7 in 10,000 live births globally, significantly impacting children's quality of life and leading to increased morbidity and mortality.
- High treatment costs and post-intervention complications associated with hydrocephalus present a substantial disease burden.
- The integration of machine learning (ML) models into clinical settings has demonstrated potential for improving patient outcomes.
Purpose of the Study:
- To systematically review and analyze the achievements and utility of Artificial Intelligence (AI) and ML in clinical decision-making for hydrocephalus.
- To evaluate the advancements and applications of ML in the diagnosis, treatment, and prognostication of pediatric hydrocephalus.
Main Methods:
- A systematic literature search was conducted using PubMed and Cochrane databases.
- Relevant articles published up to May 2021 were included based on specific search terminology.
- Fifteen studies detailing the use of ML models in pediatric hydrocephalus were identified.
Main Results:
- ML models demonstrated a median prediction accuracy of 0.88 across various diagnostic, treatment, and prognostication tasks.
- Ventricular segmentation for hydrocephalus diagnosis was the most common application of ML models.
- Neural networks were the most frequently utilized ML model, with ML models achieving faster processing speeds than manual or non-ML automated methods.
Conclusions:
- ML applications show significant advances in pediatric hydrocephalus management, offering near-human accuracy and faster processing.
- ML methods may be superior to manual approaches for clinical use in hydrocephalus care.
- Future research should focus on the feasibility and implementation of these ML models in real-world patient care and field settings.
Introduction:
Annually, hydrocephalus affects nearly 7 children per 10,000 live births around the world. It significantly impairs the quality of life of such children and is associated with increased morbidity and mortality The high cost of treatment and post-intervention complications add to the burden of disease. Deployment of machine learning (ML) models in actual clinical settings have led to improved outcomes.
Objective:
The aim of this systematic review is to analyze the utility as well as acknowledge the achievements of AI/ML in HCP decision making.
Methodology:
PubMed and Cochrane databases were used to perform a systematic search with proper terminology to include all the relevant articles up to May 2021.
Results:
Fifteen studies that described the use of ML models in the diagnosis, treatment, and prognostication of pediatric hydrocephalus were identified. The median accuracy of prediction by the ML model in various tasks listed above was found to be 0.88. ML models were most commonly employed for ventricular segmentation for diagnosis of hydrocephalus. The most frequently used model was neural networks. ML models attained faster processing speeds than their manual and non-ML-based automated counterparts.
Conclusion:
This study attempts to evaluate the important advances and applications of ML in pediatric hydrocephalus. These methods may be better suited for clinical use than manual methods alone due to faster automated processing and near-human accuracy. Future studies should evaluate whether the use of these models is feasible in the future for patient care and management in field settings.
