iDoc-X: An artificial intelligence model for tuberculosis diagnosis and localization

Abstract

For decades, tuberculosis (TB) is an unavoidable lung disease and epidemic for several developing nations. It proceeds to be the main cause of demises worldwide. It is because of poor access to medical diagnosis, where tuberculosis disease is common. Further for this medical diagnosis problem, chest X-ray (CXR) is recognized to be a convenient, cost-effective, and primary tuberculosis diagnosis tool. However, reading each CXR manually for TB localization is a hectic task for the radiologist where TB disease is common. To overcome this limitation, in this paper we have discussed the iDoc-X model, which is a seamlessly integrated software of iDoc.ai (an initiative of Teleglobal Consulting LTD). iDoc-X diagnoses the TB disease using the AI model and gives the prioritized list to a medical practitioner. In addition to this, we have also performed and discussed the accuracy test of the iDoc-X model. This will overcome the restrictions of the TB diagnosis workflow and provide better assistance to the medical practitioner, where the TB disease is common.

Subject Classification:

• 68T07 (Artificial Neural Networks and Deep Learning)

Keywords:

• iDoc.ai
• iDoc-X
• TB
• AI
• Deep learning
• Neural network
• Healthcare safety
• AI in radiology