The Evolution of Machine Learning In TB Diagnostics: Unlocking Patterns and Insights – ETHealthWorld

By Raghavendra Goud Vaggu

The severity of tuberculosis (TB) makes it a troubling crisis across the globe, especially as it is responsible for millions of deaths around the world. According to the World Health Organisation (WHO), TB was responsible for 1.6 million deaths in 2021, making it the 13th biggest killer and second leading infectious killer only after COVID-19 that year. With 10.6 million people falling ill in 2021 to the dreaded disease, and patients cutting across all demographics, there is need for vigilance.

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The rise of computer-aided diagnostics has certainly added impetus to the drive for better TB diagnosis, especially because of better medical imaging that gives radiologists more precise interpretation of the patient's chest, blood, spine, or brain, depending on the part of the body that is affected. One of such tools is the CAD model which offers precise diagnosis of the TB cavity and clearly displays areas of interest when observing the chest x-ray image. This is a huge improvement on preexisting CAD systems which could not identify TB cavities, as a result of the lung field's superimposed anatomic parts.

Looking ahead in TB diagnosis and treatment

Within the framework of modern TB analysis, the place of data cannot be overemphasised. Fully automated CAD systems are being experimented for their great features, including handcrafted systems and deep features. These systems use pre-trained CNN frameworks and supervised learning to probe the parts of the body that are of interest using obtained data and processing such data to reach conclusive diagnosis. This also comes with the ongoing conversation around the difference between supervised and unsupervised learning and how the choice of the future can shape TB diagnosis. More advanced CAD systems will likely emerge in the future, powered by superior AI.

Raghavendra Goud Vaggu, Global CEO, Empe Diagnostics

(DISCLAIMER: The views expressed are sole of the author and ETHealthworld does not necessarily subscribe to it. ETHealthworld.com shall not be responsible for any damage caused to any person/organisation directly or indirectly)

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The Evolution of Machine Learning In TB Diagnostics: Unlocking Patterns and Insights - ETHealthWorld

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