AI based model to detect Silicosis, tuberculosis and other related disease at an early stage/ प्रारंभिक अवस्था में सिलिकोसिस, तपेदिक और अन्य संबंधित बीमारियों का पता लगाने के लिए AI आधारित मॉडल
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.
- 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?