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               ·232 ·                            南 京    医 科 大 学 学         报                        2022年2月


              考虑继续优化模型,把此类患者纳入研究。                                    tion and classification of rib fractures on thoracic CT us⁃
                  综上所述,AI辅助医师诊断肋骨骨折能够提高                              ing convolutional neural network:accuracy and feasibility
              肋骨骨折检出的灵敏度,并减少诊断时间,使不同                                [J]. Korean J Radiol,2020,21(7):869-879
              年资医师的诊断效能趋于一致。                                    [11] TALBOT B S,GANGE C P JR,CHATURVEDI A,et al.
                                                                     Traumatic rib injury:patterns,imaging pitfalls,complica⁃
             [参考文献]
                                                                     tions,and treatment[J]. Radiographics,2017,37(2):
             [1] 刘    云,都定元,胡      旭,等. 严重胸部创伤病死率的相                  628-651
                   关因素分析[J]. 中国医学科学院学报,2013,35(1):              [12] 王春杰,袁慧书. 卷积神经网络在骨骼肌肉放射学中的
                   74-79                                             研究进展[J]. 中国医学影像技术,2020,36(9):1375-
             [2] ZREIK N H,FRANCIS I,RAY A,et al. Blunt chest trau⁃  1378
                   ma:bony injury in the thorax[J]. Br J Hosp Med(Lond),  [13] RODRÍGUEZ⁃RUIZ A,KRUPINSKI E,MORDANG J J,
                   2016,77(2):72-77                                  et al. Detection of breast cancer with mammography:ef⁃
             [3] SIRMALI M,TÜRÜT H,TOPÇU S,et al. A comprehen⁃
                                                                     fect of an artificial intelligence support system[J]. Radiol⁃
                   sive analysis of traumatic rib fractures:morbidity,mortali⁃
                                                                     ogy,2019,290(2):305-314
                   ty and management[J]. Eur J Cardiothorac Surg,2003,24
                                                                [14] LIU C,HU S C,WANG C,et al. Automatic detection of
                  (1):133-138
                                                                     pulmonary nodules on CT images with YOLOv3:develop⁃
             [4] PEEK J,BEKS R B,HIETBRINK F,et al. Epidemiology
                                                                     ment and evaluation using simulated and patient data
                   and outcome of rib fractures:a nationwide study in the
                                                                    [J]. Quant Imaging Med Surg,2020,10(10):1917-1929
                   Netherlands [J]. Eur J Trauma Emerg Surg,2020,
                                                                [15] ONISHI Y,TERAMOTO A,TSUJIMOTO M,et al. Auto⁃
                   10.1007/s00068⁃020⁃01412⁃2
                                                                     mated pulmonary nodule classification in computed to⁃
             [5] CHAPMAN B C,OVERBEY D M,TESFALIDET F,et al.
                                                                     mography images using a deep convolutional neural net⁃
                   Clinical utility of chest computed tomography in patients
                                                                     work  trained  by  generative  adversarial  networks
                   with rib fractures CT chest and rib fractures[J]. Arch
                                                                    [J]. Biomed Res Int,2019,2019:6051939
                   Trauma Res,2016,5(4):e37070
                                                                [16] WEIKERT T,NOORDTZIJ L A,BREMERICH J,et al.
             [6] 郝    懿,杨海平,韩      洋,等. DR平片、肌骨超声及多层
                                                                     Assessment of a deep learning algorithm for the detection
                   螺旋 CT 诊断肋骨及肋软骨骨折的临床分析[J]. 医学
                                                                     of rib fractures on whole⁃body trauma computed tomogra⁃
                   影像学杂志,2019,29(4):697-699
                                                                     phy[J]. Korean J Radiol,2020,21(7):891-899
             [7] MURPHY C E 4TH,RAJA A S,BAUMANN B M,et al.
                                                                [17] MENG X H,WU D J,WANG Z,et al. A fully automated
                   Rib fracture diagnosis in the panscan era[J]. Ann Emerg
                                                                     rib fracture detection system on chest CT images and its
                   Med,2017,70(6):904-909
             [8] 陈    飞. 85 例肋骨骨折的法医学鉴定分析[J]. 中国校                    impact on radiologist performance[J]. Skeletal Radiol,
                                                                     2021,10.1007/s00256⁃021⁃03709⁃8
                   医,2015,29(7):529-530
                                                                [18] 谭  辉,田占雨,潘      宁,等. 基于深度学习的计算机辅
             [9] ZHOU Q Q,TANG W,WANG J,et al. Automatic detec⁃
                   tion and classification of rib fractures based on patients’  助诊断系统在提高急性肋骨骨折诊断效能上的价值
                                                                    [J]. 临床放射学杂志,2020,39(12):2493-2497
                   CT images and clinical information via convolutional neu⁃
                   ral network[J]. Eur Radiol,2021,31(6):3815-3825                        [收稿日期] 2021-11-27
                                                                                               (本文编辑:唐      震)
             [10] ZHOU Q Q,WANG J,TANG W,et al. Automatic detec⁃
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