Advanced Ultrasound in Diagnosis and Therapy ›› 2023, Vol. 7 ›› Issue (2): 73-81.doi: 10.37015/AUDT.2023.230019

• Review Articles • Previous Articles     Next Articles

Intelligent Ultrasonic Diagnosis and Clinical Application: Technical Development and Prospectives

Rendong Chen, PhDa, Xiaoqian Wang, BSa, Ping Liang, MDb, Xiaoping Ouyang, PhDc, Dexing Kong, PhDd,*()   

  1. a School of Mathematical Sciences, Qufu Normal University, Qufu, Shandong, China
    b Department of Interventional Ultrasound, the Fifth Medical Center, Chinese PLA General Hospital, Beijing, China
    c The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, China
    d School of Mathematical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
  • Received:2023-03-31 Revised:2023-04-07 Accepted:2023-04-22 Online:2023-06-30 Published:2023-04-27
  • Contact: Dexing Kong, PhD, E-mail:dxkong@zju.edu.cn

Abstract:

Ultrasound intelligent diagnosis is an emerging technology that combines artificial intelligence (AI) and medical ultrasonography. It has gained significant attention in recent years due to its potential to improve the accuracy and efficiency of medical diagnosis. The core elements of ultrasound artificial intelligence are the construction of data and algorithm models. Therefore, developing autonomous and controllable models, algorithms, and data platforms is extremely important. In this paper, we provide a comprehensive review of the current state-of-the-art in ultrasound intelligent diagnosis including the aspects of the construction of ultrasonic database, deep learning techniques in ultrasound intelligent diagnosis, and the clinical application of ultrasound-AI products. With continued advancements in AI and ultrasound imaging technology, we believe ultrasound intelligent diagnosis will be a valuable tool in the hands of healthcare professionals, providing them with more accurate and efficient diagnoses and treatment plans in the coming years.

Key words: Ultrasonic images; Intelligent diagnosis; Image database; Deep learning; Clinical application