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Artificial Intelligence, Radiology, and Tuberculosis: A Review.

Sagar Kulkarni¹, Saurabh Jha¹

¹Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA.

Academic Radiology

|November 25, 2019

Summary

Artificial intelligence (AI) offers a promising solution for diagnosing tuberculosis (TB) globally. AI tools, from basic systems to deep learning, can improve diagnostic accuracy, especially in resource-limited areas.

Keywords:

Artificial intelligence Computer-aided diagnosis Deep learning Global health Tuberculosis

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Area of Science:

Medical Imaging
Artificial Intelligence
Infectious Diseases

Background:

Tuberculosis (TB) remains a significant global health threat, particularly in developing nations with limited medical resources.
Access to timely and accurate diagnostic tests for TB is often challenging in endemic regions.
Advancements in artificial intelligence (AI) present potential solutions to overcome these diagnostic barriers.

Purpose of the Study:

To review the current applications of AI in diagnosing tuberculosis using chest radiography.
To explore the spectrum of AI tools, from computer-aided diagnosis to deep learning algorithms.
To highlight AI's potential impact on global health through enhanced TB diagnosis.

Main Methods:

Review of existing literature on AI applications in tuberculosis diagnosis.
Analysis of computer-aided diagnosis (CAD) systems for TB detection.
Evaluation of deep learning algorithms for interpreting chest radiographs in TB screening.

Main Results:

AI demonstrates potential in improving the accuracy and efficiency of TB diagnosis from chest X-rays.
Various AI approaches, including deep learning, show promise in identifying radiographic signs of TB.
The review covers a range of AI methodologies applied to TB diagnostics.

Conclusions:

Artificial intelligence holds significant potential to enhance tuberculosis diagnosis globally.
AI can help bridge the gap in diagnostic capabilities, particularly in underserved regions.
Further development and implementation of AI tools could substantially contribute to combating the global TB epidemic.