Revolutionising osseous biopsy: the impact of artificial intelligence in the era of personalized medicine

被引:0
作者
Isaac, Amanda [1 ]
Klontzas, Michail E. [2 ]
Dalili, Danoob [3 ]
Akdogan, Asli Irmak [4 ]
Fawzi, Mohamed [5 ]
Gugliemi, Giuseppe [6 ]
Filippiadis, Dimitrios [7 ]
机构
[1] Kings Coll London, Sch Biomed Engn & Imaging Sci, 100 Lambeth Palace Rd, London SE1 7AR, England
[2] Univ Crete, Sch Med, Dept Radiol, Iraklion 71003, Greece
[3] Epsom & St Helier Hosp, Southwest London Elective Orthopaed Ctr, London SM5 1AA, England
[4] Izmir Katip Celebi Univ, Ataturk Training & Res Hosp, Izmir, Turkiye
[5] Natl Hepatol & Trop Res Inst, Dept Radiol, Cairo, Egypt
[6] Univ Foggia, Foggia, Italy
[7] Natl & Kapodistrian Univ Athens, Univ Gen Hosp ATTIKON, Med Sch, Dept Radiol 2, Athens 12462, Greece
关键词
artificial intelligence; osseous biopsy; personalized medicine; bone diagnostics; machine learning; precision healthcare; BONE; TELEPATHOLOGY; MODEL;
D O I
10.1093/bjr/tqaf018
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalized medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, biopsy navigation, and interdisciplinary communication are reshaping how bone biopsies are conducted and interpreted. We provide a brief review of AI in image- guided biopsy of bone tumours (primary and secondary) and specimen handling, in the era of personalized medicine. This article explores AI's role in enhancing diagnostic accuracy, improving safety in biopsies, and enabling more precise targeting in bone lesion biopsies, ultimately contributing to better patient outcomes in personalized medicine. We dive into various AI technologies applied to osseous biopsies, such as traditional machine learning, deep learning, radiomics, simulation, and generative models. We explore their roles in tumour-board meetings, communication between clinicians, radiologists, and pathologists. Additionally, we inspect ethical considerations associated with the integration of AI in bone biopsy procedures, technical limitations, and we delve into health equity, generalizability, deployment issues, and reimbursement challenges in AI-powered healthcare. Finally, we explore potential future developments and offer a list of open-source AI tools and algorithms relevant to bone biopsies, which we include to encourage further discussion and research.
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页数:15
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共 61 条
  • [51] A Multiparametric Method Based on Clinical and CT-Based Radiomics to Predict the Expression of p53 and VEGF in Patients With Spinal Giant Cell Tumor of Bone
    Wang, Qizheng
    Zhang, Yang
    Zhang, Enlong
    Xing, Xiaoying
    Chen, Yongye
    Nie, Ke
    Yuan, Huishu
    Su, Min-Ying
    Lang, Ning
    [J]. FRONTIERS IN ONCOLOGY, 2022, 12
  • [52] Revolutionizing Digital Pathology With the Power of Generative Artificial Intelligence and Foundation Models
    Waqas, Asim
    Bui, Marilyn M.
    Glassy, Eric F.
    El Naqa, Issam
    Borkowskif, Piotr
    Borkowski, Andrew A.
    Rasool, Ghulam
    [J]. LABORATORY INVESTIGATION, 2023, 103 (11)
  • [53] Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics
    Wennmann, Markus
    Ming, Wenlong
    Bauer, Fabian
    Chmelik, Jiri
    Klein, Andre
    Uhlenbrock, Charlotte
    Groezinger, Martin
    Kahl, Kim-Celine
    Nonnenmacher, Tobias
    Debic, Manuel
    Hielscher, Thomas
    Thierjung, Heidi
    Rotkopf, Lukas T.
    Stanczyk, Nikolas
    Sauer, Sandra
    Jauch, Anna
    Goetz, Michael
    Kurz, Felix T.
    Schlamp, Kai
    Horger, Marius
    Afat, Saif
    Besemer, Britta
    Hoffmann, Martin
    Hoffend, Johannes
    Kraemer, Doris
    Graeven, Ullrich
    Ringelstein, Adrian
    Bonekamp, David
    Kleesiek, Jens
    Floca, Ralf O.
    Hillengass, Jens
    Mai, Elias K.
    Weinhold, Niels
    Weber, Tim F.
    Goldschmidt, Hartmut
    Schlemmer, Heinz-Peter
    Maier-Hein, Klaus
    Delorme, Stefan
    Neher, Peter
    [J]. INVESTIGATIVE RADIOLOGY, 2023, 58 (10) : 754 - 765
  • [54] Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Marrow Characterization From Whole-Body MRI A Multicentric Feasibility Study
    Wennmann, Markus
    Klein, Andre
    Bauer, Fabian
    Chmelik, Jiri
    Groezinger, Martin
    Uhlenbrock, Charlotte
    Lochner, Jakob
    Nonnenmacher, Tobias
    Rotkopf, Lukas Thomas
    Sauer, Sandra
    Hielscher, Thomas
    Goetz, Michael
    Floca, Ralf Omar
    Neher, Peter
    Bonekamp, David
    Hillengass, Jens
    Kleesiek, Jens
    Weinhold, Niels
    Weber, Tim Frederik
    Goldschmidt, Hartmut
    Delorme, Stefan
    Maier-Hein, Klaus
    Schlemmer, Heinz-Peter
    [J]. INVESTIGATIVE RADIOLOGY, 2022, 57 (11) : 752 - 763
  • [55] Feasibility and Safety of Percutaneous CT-Guided Bone Biopsies in Patients with Cancer Using a Patient-Mounted Robotic System: A Retrospective Analysis of 40 Consecutive Biopsies
    Witkowska, Agnieszka
    Petre, Elena N.
    Moussa, Amgad M.
    Santos, Ernesto
    Sarkar, Debkumar
    Lis, Eric
    Cornelis, Francois H.
    [J]. JOURNAL OF VASCULAR AND INTERVENTIONAL RADIOLOGY, 2023, 34 (12) : 2174 - 2179
  • [56] Spinal MRI-Based Radiomics Analysis to Predict Treatment Response in Multiple Myeloma
    Wu, Zengjie
    Bian, Tiantian
    Dong, Cheng
    Duan, Shaofeng
    Fei, Hairong
    Hao, Dapeng
    Xu, Wenjian
    [J]. JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, 2022, 46 (03) : 447 - 454
  • [57] Improved localization and segmentation of spinal bone metastases in MRI with nnUNet radiomics
    Xu, Yong
    Meng, Chengjie
    Chen, Dan
    Cao, Yongsheng
    Wang, Xin
    Ji, Peng
    [J]. JOURNAL OF BONE ONCOLOGY, 2024, 48
  • [58] Bone tumor necrosis rate detection in few-shot X-rays based on deep learning
    Xu, Zhiyuan
    Niu, Kai
    Tang, Shun
    Song, Tianqi
    Rong, Yue
    Guo, Wei
    He, Zhiqiang
    [J]. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2022, 102
  • [59] Zhan X., 2023, Diagnostics (Basel), V13
  • [60] Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology
    Zhang, Daiwei
    Schroeder, Amelia
    Yan, Hanying
    Yang, Haochen
    Hu, Jian
    Lee, Michelle Y. Y.
    Cho, Kyung S.
    Susztak, Katalin
    Xu, George X.
    Feldman, Michael D.
    Lee, Edward B.
    Furth, Emma E.
    Wang, Linghua
    Li, Mingyao
    [J]. NATURE BIOTECHNOLOGY, 2024, 42 (09) : 1372 - 1377