Applications of artificial intelligence in biliary tract cancers

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Biliary tract cancers, including cholangiocarcinomas and gallbladder cancer, are aggressive neoplasms with dismal prognoses, often diagnosed at advanced stages.
• Gallbladder cancer exhibits significant geographical prevalence, notably in northern India among women.
• Current diagnostic challenges in biliary tract cancers stem from overlapping radiological appearances with other biliary diseases.

Purpose of the Study:

• To review artificial intelligence (AI)-based strategies for enhancing the diagnosis and prognosis of biliary tract cancers.
• To highlight the potential of deep learning (DL) in improving diagnostic performance in medical imaging for these cancers.

Main Methods:

• Review of existing literature on AI and DL applications in biliary tract cancer diagnosis.
• Analysis of how AI can assist radiologists in interpreting medical images.
• Focus on DL-based approaches for improved diagnostic accuracy.

Main Results:

• AI demonstrates potential to augment radiologists' capabilities in identifying biliary tract cancers.
• DL techniques are increasingly integrated into medical imaging, showing promise for better diagnostic outcomes.
• Early detection and radical surgery remain critical for improving patient survival rates.

Conclusions:

• AI-driven diagnostic tools offer a promising avenue to improve the early detection and management of biliary tract cancers.
• Further development and integration of DL in radiological imaging are crucial for enhancing diagnostic accuracy and patient prognosis.
• Addressing the diagnostic challenges in biliary tract cancers can lead to improved survival rates.