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.
引用
收藏
页数:15
相关论文
共 61 条
  • [41] MarrowQuant 2.0: A Digital Pathology Workflow Assisting Bone Marrow Evaluation in Experimental and Clinical Hematology
    Sarkis, Rita
    Burri, Olivier
    Royer-Chardon, Claire
    Schyrr, Frederica
    Blum, Sophie
    Costanza, Mariangela
    Cherix, Stephane
    Piazzon, Nathalie
    Barcena, Carmen
    Bisig, Bettina
    Nardi, Valentina
    Sarro, Rossella
    Ambrosini, Giovanna
    Weigert, Martin
    Spertini, Olivier
    Blum, Sabine
    Deplancke, Bart
    Seitz, Arne
    de Leval, Laurence
    Naveiras, Olaia
    [J]. MODERN PATHOLOGY, 2023, 36 (04)
  • [42] A deep look into radiomics
    Scapicchio, Camilla
    Gabelloni, Michela
    Barucci, Andrea
    Cioni, Dania
    Saba, Luca
    Neri, Emanuele
    [J]. RADIOLOGIA MEDICA, 2021, 126 (10): : 1296 - 1311
  • [43] Sohani AR, 2012, ANAL CELL PATHOL, V35, P25, DOI [10.1155/2012/676597, 10.3233/ACP-2011-0032]
  • [44] Multi-omics Data Integration, Interpretation, and Its Application
    Subramanian, Indhupriya
    Verma, Srikant
    Kumar, Shiva
    Jere, Abhay
    Anamika, Krishanpal
    [J]. BIOINFORMATICS AND BIOLOGY INSIGHTS, 2020, 14
  • [45] Tomaszewski MR, 2021, RADIOLOGY, V298, P505, DOI [10.1148/radiol.2021202553, 10.1148/radiol.2021219005]
  • [46] Mixed Reality Biopsy Navigation System Utilizing Markerless Needle Tracking and Imaging Data Superimposition
    Trojak, Michal
    Stanuch, Maciej
    Kurzyna, Marcin
    Darocha, Szymon
    Skalski, Andrzej
    [J]. CANCERS, 2024, 16 (10)
  • [47] International Validation of the SORG Machine-learning Algorithm for Predicting the Survival of Patients with Extremity Metastases Undergoing Surgical Treatment
    Tseng, Ting-En
    Lee, Chia-Che
    Yen, Hung-Kuan
    Groot, Olivier Q.
    Hou, Chun-Han
    Lin, Shin-Ying
    Bongers, Michiel E. R.
    Hu, Ming-Hsiao
    Karhade, Aditya, V
    Ko, Jia-Chi
    Lai, Yi-Hsiang
    Yang, Jing-Jen
    Verlaan, Jorrit-Jan
    Yang, Rong-Sen
    Schwab, Joseph H.
    Lin, Wei-Hsin
    [J]. CLINICAL ORTHOPAEDICS AND RELATED RESEARCH, 2022, 480 (02) : 367 - 378
  • [48] Artificial intelligence in radiology: 100 commercially available products and their scientific evidence
    van Leeuwen, Kicky G.
    Schalekamp, Steven
    Rutten, Matthieu J. C. M.
    van Ginneken, Bram
    de Rooij, Maarten
    [J]. EUROPEAN RADIOLOGY, 2021, 31 (06) : 3797 - 3804
  • [49] Radiomics in medical imaging-"how-to" guide and critical reflection
    van Timmeren, Janita E.
    Cester, Davide
    Tanadini-Lang, Stephanie
    Alkadhi, Hatem
    Baessler, Bettina
    [J]. INSIGHTS INTO IMAGING, 2020, 11 (01)
  • [50] Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors
    von Schacky, Claudio E.
    Wilhelm, Nikolas J.
    Schaefer, Valerie S.
    Leonhardt, Yannik
    Jung, Matthias
    Jungmann, Pia M.
    Russe, Maximilian F.
    Foreman, Sarah C.
    Gassert, Felix G.
    Gassert, Florian T.
    Schwaiger, Benedikt J.
    Mogler, Carolin
    Knebel, Carolin
    Von Eisenhart-Rothe, Ruediger
    Makowski, Marcus R.
    Woertler, Klaus
    Burgkart, Rainer
    Gersing, Alexandra S.
    [J]. EUROPEAN RADIOLOGY, 2022, 32 (09) : 6247 - 6257