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2022, 04, v.34 76-80
骨关节医学影像在人工智能中的研究与应用
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发布时间: 2022-08-15
出版时间: 2022-08-15
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摘要:

目的:在医疗领域医学影像是人工智能(Artificial Intelligence, AI)的主要应用方向之一。尤其骨关节系统疾病的影像检查,存在临床工作繁重、重复性较强的现象,符合医学影像AI的应用场景。其应用可有效提高影像科医师诊断工作的效率,减轻工作压力,为疾病诊疗及预后提供可靠评估。本文对AI结合医学影像在骨关节系统中的应用现状进行综述,对临床应用价值及意义进行归纳,并对未来发展及存在的问题进行思考。

Abstract:

Objective: Medical imaging is one of the main application directions of artificial intelligence(AI) in the medical field. In particular, the imaging examination of bone and joint system diseases has the phenomenon of heavy clinical work and strong repetition, which is in line with the application scenario of medical imaging AI. Its application can effectively improve the diagnostic work efficiency of radiologists, reduce work pressure, and provide reliable evaluation for disease diagnosis, treatment and prognosis. This paper reviews the current application status of AI medical imaging in the bone and joint system, summarizes the clinical application value and significance, and considers the future development and existing problems.

参考文献

[1]MINTZ Y,BRODIE R.Introduction to artificial intelligence in medicine[J].Minim Invasive Ther Allied Technol,2019,28(2):73-81.

[3]刘志鹏,侯瑞刚,李雯,等.AI医学影像学诊断新模式前景的调查研究[J].山东医学高等专科学校学报,2020,42(3):224-226.

[3]GORE J C.Artificial intelligence in medical imaging[J].Magn Reson Imaging,2020,68:A1-A4.

[4]LIU C,XIE H,ZHANG Y.Self-supervised attention mechanism for pediatric bone age assessment with efficient weak annotation[J].IEEE Trans Med Imaging,2021,40 (10):2685-2697.

[5]LEE H,TAJMIR S,LEE J,et al.Fully automated deep learning system for bone age assessment[J].J Digit Imaging,2017,30(4):427-441.

[6]KOITKA S,KIM M S,QU M,et al.Mimicking the radiologists'workflow:estimating pediatric hand bone age with stacked deep neural networks[J].Med Image Anal,2020,64:101743.

[7]BUI T D,LEE J J,SHIN J.Incorporated region detection and classification using deep convolutional networks for bone age assessment[J].Artif Intell Med,2019,97:1-8.

[8]REDDY N E,RAYAN J C,ANNAPRAGADA A V,et al.Bone age determination using only the index finger:a novel approach using a convolutional neural network compared with human radiologists[J].Pediatr Radiol,2020,50 (4):516-523.

[9]BOOZ C,YEL I,WICHMANN J L,et al.Artificial intelligence in bone age assessment:accuracy and efficiency of a novel fully automated algorithm compared to the GreulichPyle method[J].Eur Radiol Exp,2020,4(1):6.

[10]AGHNIA F N,LAI J Y,WANG J C,et al.Sanders classification of calcaneal fractures in CT images with deep learning and differential data augmentation techniques[J].Injury,2021,52(3):616-624.

[11]MURATA K,ENDO K,AIHARA T,et al.Artificial intelligence for the detection of vertebral fractures on plain spinal radiography[J].Sci Rep,2020,10(1):20031.

[12]SATO Y,TAKEGAMI Y,ASAMOTO T,et al.Artificial intelligence improves the accuracy of residents in the diagnosis of hip fractures:a multicenter study[J].BMC Musculoskelet Disord,2021,22(1):407.

[13]LINDSEY R,DALUISKI A,CHOPRA S,et al.Deep neural network improves fracture detection by clinicians[J].Proc Natl Acad Sci U S A,2018,115(45):11591-11596.

[14]TANG C,ZHANG W,LI H,et al.CNN-based qualitative detection of bone mineral density via diagnostic CT slices for osteoporosis screening[J].Osteoporos Int,2021,32(5):971-979.

[15]MENG J,SUN N,CHEN Y,et al.Artificial neural network optimizes self-examination of osteoporosis risk in women[J].J Int Med Res,2019,47(7):3088-3098.

[16]FANG Y,LI W,CHEN X,et al.Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks[J].Eur Radiol,2021,31(4):1831-1842.

[17]LIM H K,HA H I,PARK S Y,et al.Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT:a retrospective single center preliminary study[J].PLo S One,2021,16(3):e0247330.

[18]ZHANG R,HUANG L,XIA W,et al.Multiple supervised residual network for osteosarcoma segmentation in CT images[J].Comput Med Imaging Graph,2018,63:1-8.

[19]HE Y,PAN I,BAO B,et al.Deep learning-based classification of primary bone tumors on radiographs:a preliminary study[J].EBio Medicine,2020,62:103121.

[20]DU X H,WEI H,LI P,et al.Artificial Intelligence (AI)Assisted CT/MRI Image Fusion Technique in Preoperative Evaluation of a Pelvic Bone Osteosarcoma[J].Front Oncol,2020,10:1209.

[21]AOKI Y,NAKAYAMA M,NOMURA K,et al.The utility of a deep learning-based algorithm for bone scintigraphy in patient with prostate cancer[J].Ann Nucl Med,2020,34(12):926-931.

[22]DO N T,JUNG S T,YANG H J,et al.Multi-level SegUnet Model with global and Patch-Based X-ray Images for knee bone tumor detection[J].Diagnostics (Basel),2021,11(4):691.

[23]LIU F,ZHOU Z,SAMSONOV A,et al.Deep learning approach for evaluating knee MR images:achieving high diagnostic performance for cartilage lesion detection[J].Radiology,2018,289(1):160-169.

[24]PARK H S,JEON K,CHO Y J,et al.Diagnostic performance of a new convolutional neural network algorithm for detecting developmental dysplasia of the hip on anteroposterior radiographs[J].Korean J Radiol,2021,22(4):612-623.

[25]SWIECICKI A,LI N,O'DONNELL J,et al.Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists[J].Comput Biol Med,2021,133:104334.

[26]RIZK B,BRAT H,ZILLE P,et al.Meniscal lesion detection and characterization in adult knee MRI:a deep learning model approach with external validation[J].Phys Med,2021,83:64-71.

[27]NAMIRI N K,FLAMENT I,ASTUTO B,et al.Deep Learning for Hierarchical Severity Staging of Anterior Cruciate Ligament Injuries from MRI[J].Radiol Artif Intell,2020,2(4):e190207.

[28]DING J,CAO P,CHANG H C,et al.Deep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat-water decomposition MRI[J].Insights Imaging,2020,11(1):128.

[29]KANG Y,CHOI D,LEE K J,et al.Evaluating subscapularis tendon tears on axillary lateral radiographs using deep learning[J].Eur Radiol,2021,31(12):9408-9417.

[30]林广,张志强.人工智能医学影像在骨关节系统中的应用进展[J].中国医学影像学杂志,2022,30(2):184-187.

基本信息:

中图分类号:TP391.41;TP18;R684

引用信息:

[1]王肖,李晶,李天然.骨关节医学影像在人工智能中的研究与应用[J].影像技术,2022,34(04):76-80.

发布时间:

2022-08-15

出版时间:

2022-08-15

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