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Merging Two Models of One-Dimensional Convolutional Neural Networks to Improve the Differential Diagnosis between
1Information Technologies Department, Altinbas University, Istanbul 34217, Turkey.
Insights
A new AI model using merged 2-1D-CNNs accurately differentiates pediatric asthma from bronchitis. This tool aids doctors in rapid diagnosis of lower respiratory tract infections, improving patient outcomes.
Area of Science:
- Pediatric respiratory medicine
- Artificial intelligence in healthcare
- Machine learning for medical diagnosis
Background:
- Acute asthma and bronchitis are common pediatric lower respiratory tract infections (LRTIs) with overlapping symptoms, confusing junior doctors.
- Accurate differential diagnosis is crucial, especially in low- and middle-income countries (LMICs), to reduce mortality.
- Existing diagnostic methods can be time-consuming and resource-intensive.
Purpose of the Study:
- To develop an improved method for differentiating acute asthma from bronchitis in children.
- To reduce diagnostic time, effort, and cost for healthcare providers.
- To enhance the diagnostic capabilities of junior and practitioner doctors.
Main Methods:
- A dataset of 512 prospective pediatric cases with 12 clinical features was collected in Iraq.
- A novel approach merging two one-dimensional convolutional neural networks (2-1D-CNNs) was proposed.
- The performance of the 2-1D-CNNs model was compared against a 1D-CNNs + LSTM merged model.
Main Results:
- The merged 2-1D-CNNs model achieved an accuracy of 99.72% and an Area Under the Curve (AUC) of 1.0.
- The 1D-CNNs + LSTM model achieved an accuracy of 99.44% and an AUC of 99.96%.
- The 2-1D-CNNs approach demonstrated superior performance in differentiating the conditions.
Conclusions:
- Merging 2-1D-CNNs provides highly accurate results due to combined hyperparameter optimization.
- 1D-CNNs are effective for analyzing textual healthcare data, proving beneficial for diagnostic tools.
- This AI-driven approach can empower doctors for rapid, accurate differentiation of pediatric asthma and bronchitis.
Abstract:
(1) Background: Acute asthma and bronchitis are common infectious diseases in children that affect lower respiratory tract infections (LRTIs), especially in preschool children (below six years). These diseases can be caused by viral or bacterial infections and are considered one of the main reasons for the increase in the number of deaths among children due to the rapid spread of infection, especially in low- and middle-income countries (LMICs). People sometimes confuse acute bronchitis and asthma because there are many overlapping symptoms, such as coughing, runny nose, chills, wheezing, and shortness of breath; therefore, many junior doctors face difficulty differentiating between cases of children in the emergency departments. This study aims to find a solution to improve the differential diagnosis between acute asthma and bronchitis, reducing time, effort, and money. The dataset was generated with 512 prospective cases in Iraq by a consultant pediatrician at Fallujah Teaching Hospital for Women and Children; each case contains 12 clinical features. The data collection period for this study lasted four months, from March 2022 to June 2022. (2) Methods: A novel method is proposed for merging two one-dimensional convolutional neural networks (2-1D-CNNs) and comparing the results with merging one-dimensional neural networks with long short-term memory (1D-CNNs + LSTM). (3) Results: The merged results (2-1D-CNNs) show an accuracy of 99.72% with AUC 1.0, then we merged 1D-CNNs with LSTM models to obtain the accuracy of 99.44% with AUC 99.96%. (4) Conclusions: The merging of 2-1D-CNNs is better because the hyperparameters of both models will be combined; therefore, high accuracy results will be obtained. The 1D-CNNs is the best artificial neural network technique for textual data, especially in healthcare; this study will help enhance junior and practitioner doctors' capabilities by the rapid detection and differentiation between acute bronchitis and asthma without referring to the consultant pediatrician in the hospitals.
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