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AI based model to detect Silicosis, tuberculosis and other related disease at an early stage

Pneumoconiosis is a lung disease caused by excessive exposure to dust (e.g., silica, asbestos, coal, and mixed dust), which often occurs in the workplace.

Pneumoconiosis is a major occupational lung disease with increasing prevalence and severity worldwide.

The clinical diagnosis of pneumoconiosis is mainly based on the examination of chest radiographs (i.e., X-ray images).

However, radiograph-based diagnosis of pneumoconiosis requires a well-trained and experienced radiologist to visually identify subtle graphic patterns and features described in the ILO guidelines.

The tedious process and considerable inconsistency in inter- and intra-observer variations in different scenarios makes it cumbersome to use.

Problem statements

  • How to solve the problem by an artificial intelligence (AI) – based model to assist radiologists in pneumoconiosis screening and staging using chest radiographs?
  • How to detect and intervene the disease timely, accurately and at a relatively lower cost?
  • How to use AI for diagnosis of lung abnormalities such as lung nodules, pulmonary tuberculosis, cystic fibrosis, and pneumoconiosis?
AI based model to detect Silicosis, tuberculosis and other related disease at an early stage