Automated detection of bone lesions using CT and MRI: a systematic review.

IF 9.7 1区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Radiologia Medica Pub Date : 2024-11-06 DOI:10.1007/s11547-024-01913-9

Fatih Erdem, Salvatore Gitto, Stefano Fusco, Maria Vittoria Bausano, Francesca Serpi, Domenico Albano, Carmelo Messina, Luca Maria Sconfienza

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引用次数: 0

Abstract

Purpose: The aim of this study was to systematically review the use of automated detection systems for identifying bone lesions based on CT and MRI, focusing on advancements in artificial intelligence (AI) applications.

Materials and methods: A literature search was conducted on PubMed and MEDLINE. Data were extracted and grouped into three main categories, namely baseline study characteristics, model validation strategies, and the type of AI algorithms.

Results: A total of 10 studies were selected and analyzed, including 2,768 patients overall with a median of 187 per study. These studies utilized various AI algorithms, predominantly deep learning models (6 studies) such as Convolutional Neural Networks. Among machine learning validation strategies, K-fold cross-validation was the mostly used (5 studies). Clinical validation was performed using data from the same institution (internal testing) in 8 studies and from both the same and different (external testing) institutions in 1 study, respectively.

Conclusion: AI, particularly deep learning, holds significant promise in enhancing diagnostic accuracy and efficiency. However, the review highlights several limitations, such as the lack of standardized validation methods and the limited use of external datasets for testing. Future research should address these gaps to ensure the reliability and applicability of AI-based detection systems in clinical settings.

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使用 CT 和 MRI 自动检测骨病变：系统性综述。

目的：本研究旨在系统回顾基于 CT 和 MRI 识别骨病变的自动检测系统的使用情况，重点关注人工智能（AI）应用的进展：在 PubMed 和 MEDLINE 上进行了文献检索。提取数据并将其分为三大类，即基线研究特征、模型验证策略和人工智能算法类型：共选择并分析了 10 项研究，包括 2768 名患者，每项研究的中位数为 187 名患者。这些研究采用了各种人工智能算法，主要是深度学习模型（6 项研究），如卷积神经网络。在机器学习验证策略中，使用最多的是 K 折交叉验证（5 项研究）。8项研究使用了来自同一机构（内部测试）的数据进行临床验证，1项研究使用了来自同一机构和不同机构（外部测试）的数据进行临床验证：结论：人工智能，尤其是深度学习，在提高诊断准确性和效率方面大有可为。然而，本综述强调了一些局限性，如缺乏标准化的验证方法和外部数据集的测试使用有限。未来的研究应弥补这些不足，以确保基于人工智能的检测系统在临床环境中的可靠性和适用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Radiologia Medica 医学-核医学

CiteScore

14.10

自引率

7.90%

发文量

133

审稿时长

4-8 weeks

期刊介绍： Felice Perussia founded La radiologia medica in 1914. It is a peer-reviewed journal and serves as the official journal of the Italian Society of Medical and Interventional Radiology (SIRM). The primary purpose of the journal is to disseminate information related to Radiology, especially advancements in diagnostic imaging and related disciplines. La radiologia medica welcomes original research on both fundamental and clinical aspects of modern radiology, with a particular focus on diagnostic and interventional imaging techniques. It also covers topics such as radiotherapy, nuclear medicine, radiobiology, health physics, and artificial intelligence in the context of clinical implications. The journal includes various types of contributions such as original articles, review articles, editorials, short reports, and letters to the editor. With an esteemed Editorial Board and a selection of insightful reports, the journal is an indispensable resource for radiologists and professionals in related fields. Ultimately, La radiologia medica aims to serve as a platform for international collaboration and knowledge sharing within the radiological community.