Enhancing diagnostic precision in liver lesion analysis using a deep learning-based system: opportunities and challenges

被引：0
|
作者：
Lee, Jeong Min [1 ,2 ,3 ]
Bae, Jae Seok [1 ]
机构：
[1] Seoul Natl Univ Hosp, Dept Radiol, Seoul, South Korea
[2] Seoul Natl Univ, Coll Med, Dept Radiol, Seoul, South Korea
[3] Seoul Natl Univ, Inst Radiat Med, Med Res Ctr, Seoul, South Korea
来源：
NATURE REVIEWS CLINICAL ONCOLOGY | 2024年 / 21卷 / 07期
关键词：
FUTURE;
D O I：
10.1038/s41571-024-00887-x
中图分类号：
R73 [肿瘤学];
学科分类号：
100214 ;
摘要：
A recent study reported the development and validation of the Liver Artificial Intelligence Diagnosis System (LiAIDS), a fully automated system that integrates deep learning for the diagnosis of liver lesions on the basis of contrast-enhanced CT scans and clinical information. This tool improved diagnostic precision, surpassed the accuracy of junior radiologists (and equalled that of senior radiologists) and streamlined patient triage. These advances underscore the potential of artificial intelligence to enhance hepatology care, although challenges to widespread clinical implementation remain.
引用
收藏
页码：485 / 486
页数：2
相关论文
共 50 条
  • [31] Deep learning-based pseudo-mass spectrometry imaging analysis for precision medicine
    Shen, Xiaotao
    Shao, Wei
    Wang, Chuchu
    Liang, Liang
    Chen, Songjie
    Zhang, Sai
    Rusu, Mirabela
    Snyder, Michael P.
    BRIEFINGS IN BIOINFORMATICS, 2022, 23 (05)
  • [32] High Precision Deep Learning-Based Tabular Position Detection
    Jiang, JiChu
    Simsek, Murat
    Kantarci, Burak
    Khan, Shahzad
    2020 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (ISCC), 2020, : 282 - 288
  • [33] U-SDRC: A NOVEL DEEP LEARNING-BASED METHOD FOR LESION ENHANCEMENT IN LIVER CT IMAGES
    Zheng, Zhongshu
    Ma, Ling
    Yang, Songxiao
    Boumaraf, Said
    Liu, Xiabi
    Ma, Xiaohong
    MEDICAL IMAGING 2021: IMAGE PROCESSING, 2021, 11596
  • [34] Enhancing chromosomal analysis efficiency through deep learning-based artificial intelligence graphic analysis
    Zhou, Ying
    Xu, Lingling
    Zhang, Lichao
    Shi, Danhua
    Wu, Chaoyu
    Wei, Ran
    Song, Ning
    Wu, Shanshan
    Chen, Changshui
    Li, Haibo
    DISCOVER APPLIED SCIENCES, 2024, 6 (06)
  • [35] Deep Learning-Based Superresolution Reconstruction for Upper Abdominal Magnetic Resonance Imaging An Analysis of Image Quality, Diagnostic Confidence, and Lesion Conspicuity
    Almansour, Haidara
    Gassenmaier, Sebastian
    Nickel, Dominik
    Kannengiesser, Stephan
    Afat, Saif
    Weiss, Jakob
    Hoffmann, Rudiger
    Othman, Ahmed E.
    INVESTIGATIVE RADIOLOGY, 2021, 56 (08) : 509 - 516
  • [36] Deep learning-based diagnostic system for gastrointestinal submucosal tumor under endoscopic ultrasonography
    张晨霞
    China Medical Abstracts(Internal Medicine), 2022, 39 (04) : 223 - 223
  • [37] Deep Learning-based Border Surveillance System using Thermal Imaging
    Kumar, Saurav
    Malik, Sarthak
    Sumathi, P.
    2022 IEEE 19TH INDIA COUNCIL INTERNATIONAL CONFERENCE, INDICON, 2022,
  • [38] A Deep Learning-Based Coyote Detection System Using Audio Data
    Jung, Heesun
    Kwon, Bokyung
    Kim, Youngbin
    Lee, Yejin
    Park, Jihyeon
    Pegg, Griffin
    Wang, Yaqin
    Smith, Anthony H.
    2023 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION, ICAIIC, 2023, : 170 - 175
  • [39] Dicentric chromosome assay using a deep learning-based automated system
    Soo Kyung Jeong
    Su Jung Oh
    Song-Hyun Kim
    Seungsoo Jang
    Yeong-Rok Kang
    HyoJin Kim
    Yong Uk Kye
    Seong Hun Lee
    Chang Geun Lee
    Moon-Taek Park
    Joong Sun Kim
    Min Ho Jeong
    Wol Soon Jo
    Scientific Reports, 12 (1)
  • [40] The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis
    Ataei, Ali
    Eggermont, Florieke
    Verdonschot, Nico
    Lessmann, Nikolas
    Tanck, Esther
    BONE, 2024, 179
12345