Artificial intelligence in CT diagnosis: Current status and future prospects for ear diseases

Abstract

The human ear, possessing complex structures like the ossicular chain, cochlea, and auditory nerve, plays a crucial role in hearing and balance. Common ear diseases, such as hearing loss, tinnitus, facial paralysis and vertigo, affect the quality of life of millions in China. Computed tomography (CT) has made significant advancements since its introduction to China in 2000. The resolution improves from millimeter to sub-millimeter levels, and further, to 10 ​μm through bone-dedicated CT technology. The advancements have made CT become the preferred method for diagnosing various ear conditions, including congenital malformations, trauma, inflammation, and neoplasm. Artificial intelligence (AI) has brought significant breakthroughs in the CT diagnosis. The performance of automatic segmentation of ear structures has dramatically improved with the advent of ultra-high-resolution computed tomography (U-HRCT). AI-driven measurement tools are enhancing the precision and personalization of surgical planning, while deep learning-based anomaly detection is utilized to address the challenges of detecting diverse ear lesions. Furthermore, AI-driven natural language processing and large language models are revolutionizing the generation of radiology reports, providing accurate and standardized diagnostic information. Despite the ongoing challenges, the application of AI in CT is expected to faciliate the otological field, leading to more precise and personalized treatment for ear diseases.