E-Paper
Lucknow University develops AI tool for early detection of larynx cancer
The team included research scholar Mohammad Usman, Siddharth Chaurasia from Pragnyx Labs and Ginika Mahajan from Manipal University Jaipur
: Researchers from the Computer Science department of Lucknow University have developed an Artificial Intelligence (AI) model that can detect larynx cancer at an early stage with high accuracy. The study has been published in Scientific Reports, a journal of the Nature Group.
The research was led by prof Puneet Mishra. The team included research scholar Mohammad Usman, Siddharth Chaurasia from Pragnyx Labs and Ginika Mahajan from Manipal University Jaipur. They developed ‘MedLK-SCNet’, an AI system for detecting cancer of the larynx, the second deadliest cancer of the respiratory tract after lung cancer.
Prof Mishra said the model analyses Narrow Band Imaging and endoscopic images to identify key microvascular patterns associated with cancer. By reducing computational complexity while capturing fine image details, it helps doctors make faster and more accurate clinical decisions. He said cases of larynx cancer are rising rapidly across India, including Uttar Pradesh, mainly due to tobacco, bidi, cigarette and gutkha consumption. Early symptoms are often missed during routine endoscopy, leading to delayed treatment and a higher risk to life.
“Our goal was to build an AI tool that reaches beyond laboratories and benefits people at the grassroots. ‘MedLK-SCNet’ will work as an assistive tool for doctors and make early detection of throat cancer easier, faster and more affordable,” Mishra said.
The researchers said early detection can greatly improve treatment success and survival rates. In rural and remote areas, where specialist doctors and advanced pathology laboratories are limited, the AI system can work with basic medical infrastructure and assist less-skilled healthcare staff. They added that automated screening can reduce the need for repeated expensive tests, lowering the financial burden on economically weaker patients while saving doctors’ time and enabling them to treat more patients.
