TB Detect Test with AI
Veritas developed TBDetect AI, an advanced medical diagnostic system designed to enhance the detection and analysis of Tuberculosis (TB) using state-of-the-art deep learning techniques. It leverages a customized ResNet model to classify chest radiographs (CXR) and accurately identify TB cases.
This system integrates multiple AI-driven processes to provide reliable and efficient TB diagnosis, significantly improving upon traditional diagnostic methods.
The global burden of Tuberculosis (TB) remains substantial, with millions of new cases and deaths each year, especially in low- and middle-income countries. Despite advancements in healthcare, current diagnostic methods for TB are often slow, resource-intensive, and lack precision. The main challenges included:
Created a custom AI model to accurately classify chest X-ray images into TB-positive or TB-negative categories.
Implemented comprehensive pre-processing steps to improve the quality of X-ray images.
Ensured reliable diagnostic outcomes by rigorously testing and validating the model using performance metrics such as accuracy, precision, recall, F1-Score, and AUC-ROC.
Traditional methods for TB diagnosis are often slow and lack precision, leading to delayed treatment and potential misdiagnosis.
Many regions with high TB burdens face shortages of trained radiologists and specialized laboratory facilities, limiting the availability of accurate diagnostics.
Variations in the quality of chest X-ray images due to noise and poor contrast make it challenging to obtain accurate diagnostic results.
A deep learning model tailored for TB diagnosis, replacing the final fully connected layer of ResNet18 to suit the binary classification task of identifying TB.
Includes converting image formats, enhancing image quality, isolating regions of interest (lung fields), and normalizing images.
Model performance is validated using metrics like accuracy, precision, recall, F1-Score, and AUC-ROC to ensure reliable diagnostic outcomes.
TB Detect AI Test
Automated Data Processing
Efficiently managed and analyzed large datasets of chest X-ray images.
Advanced AI Model
Leveraged a customized ResNet model to accurately classify TB cases.
Comprehensive Validation
Ensured high reliability through rigorous testing and validation metrics.
The automated AI-driven process significantly reduced the time required for TB diagnosis compared to traditional methods.
The customized ResNet model achieved an 85% accuracy rate in identifying TB cases from chest radiographs.
Enhanced pre-processing and AI analysis led to a 70% increase in overall diagnostic efficiency.
Client Testimonial
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Date: 07/24/2024
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mining the truth within your data
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